It’s not just an inconvenience, it’s a growing crisis. A recent UK Public Sector Efficiency Survey revealed that NHS employees lose an average of five hours per week to clunky, inefficient systems. That adds up to a staggering 7.5 million hours of wasted work every single week. These valuable hours could be reinvested in treating patients, improving safety, and providing a better service. By addressing these systemic inefficiencies, we can improve healthcare services and enhance patient outcomes.

The UK government announced the much needed £3.25bn Transformation Fund to boost public service efficiency in its latest Spring statement. It’s poised to drive productivity in public services, including the NHS, at a time when efficiency is under immense scrutiny. Backing a range of initiatives, the fund will include the introduction of AI tools to revolutionise front line service delivery.

But if we are serious about modernising the NHS, we need to do more than throw money at the problem – we need targeted, measured reform. AI and automation are rightly gaining momentum in the sector. However, AI is not a magic solution on its own. Its effectiveness depends on the quality of the data it receives, how well and quickly we act on insights. If we aren’t prepared to act on its findings quickly, we create bottlenecks instead of breakthroughs. Without the right groundwork, AI risks producing noise instead of value. AI must be embedded into well-designed processes to ensure it delivers real economic benefits.

I am often asked what are the biggest technology challenges in healthcare today, and what are the opportunities and barriers for the sector to use AI effectively. My response typically focuses on the following areas.

Manual services and outdated processes

Despite ongoing digital transformation efforts, most departments still rely on manual processes. The DSIT report reveals that 45% of NHS services lack a fully digital pathway, with very few eliminating manual processing entirely.

The impact of outdated processes is felt directly by patients and healthcare workers alike. When services remain paper-based or rely on fragmented systems, productivity suffers, and resources are stretched thin. The functioning of these fragmented systems relies on ‘human glue’ – workers manually bridging siloes of data and process, which prevents recognition of the core deficiencies.

Streamlining these processes through digital transformation is not just a matter of convenience. It’s essential for improving efficiency, reducing administrative burdens, and ultimately enhancing service delivery for the public.

Process modernisation and automation is the most powerful lever available to drive service reform for such tasks. A process orchestration solution can automate time-consuming tasks such as data entry, appointment scheduling, progress tracking, compliance, and reporting. Automating these actions would enable a shift towards time spent on value-driven activities that can improve both internal efficiency and service delivery.

Fragmented and underused data

When data is scattered across multiple outdated legacy systems, information access and related processes slow down for everyone. This impacts productivity and the ability to resolve case work at speed. This lack of data integration also limits the potential of AI, machine learning, and advanced analytics. These data-driven technologies can only work with seamless access to high-quality data, to drive innovation and improve decision-making.

For the NHS to be truly AI-ready, the data must be in order. Solving this starts with adopting a platform that connects data and processes woven into a single framework. A data fabric, for example, creates a virtualised layer that links data across systems without needing to migrate it.

With advanced data management, organisations can train, refine, and deploy AI models more effectively, transforming vast amounts of information into valuable insights. High-quality data is the fuel AI needs to enhance decision-making and drive efficiency. Without it, the potential of a modern digital NHS will remain out of reach.

The future of AI-driven processes in the NHS

Optimism about AI is growing within the healthcare sector. 64% of NHS workers have some or high confidence in the potential for AI to improve their organisation’s efficiency.

The key to unlocking AI’s full potential is embedding it within existing processes. Process is where actions happen. It’s where healthcare professionals make decisions, allocate budget and resources, serve patients, and move things forward. When AI operates within processes, it gains purpose, governance, and accountability – all vital to delivering value from AI.

While organisations are under pressure to integrate AI, its success depends on strong data infrastructures and human oversight. AI should be a partner, not a replacement, ensuring efficiency and innovation without compromising security or accountability.

To sustain long-term growth, healthcare organisations must invest in agile platforms that adapt to rapid AI advancements with process orchestration technologies. A platform approach can streamline operations, enhance decision-making, and improve service outcomes. Embracing these tools isn’t just about modernisation, it’s essential for efficiency, stability, and better healthcare service delivery.

Now is the time for the NHS to seize the opportunity. Every part of our health service runs on processes – from patient referrals to hospital workflows. When we improve these processes with automation technologies like AI and process orchestration, we create better working environments for our healthcare workers, improving service delivery, and efficiency, for our NHS, for the betterment of patients.

By Peter Corpe, Industry Leader, UK Public Sector, Appian

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Barts Health NHS Trust, which serves one of the largest and most diverse patient populations in the country, has become the first healthcare provider in the NHS to implement the medical research tool Anonymise and Export from medical imaging IT company Sectra. As part of wider activity, it means that researchers will be empowered to shed new light on diseases, tailor treatments, and potentially inform the next generation of healthcare AI.

Anonymise and Export has been implemented within the trust’s existing Sectra enterprise imaging solution, a system widely used by NHS diagnosticians to analyse patient scans. The cutting-edge addition will allow for the seamless export of medical images to a secure data environment, with patient identifiers automatically removed. This removes a previously manually intensive process, releasing time for busy NHS teams, whilst addressing crucial privacy safeguards, and dramatically expanding research possibilities.

The de-identified imaging data will be integrated into the new Barts Health Data Platform (BHDP), which was formally launched in April 2025. The platform brings together different types of health information — such as scans, health records, and lab results — into one secure system that researchers can apply to use.

Steven Newhouse, deputy chief information officer for Barts Health NHS Trust, said: “We are now able to provide researchers and clinicians with access to health and imaging data at a scale we’ve not offered before. With robust safeguards in place, this development supports more efficient, secure research and marks meaningful progress in advancing medical innovation and understanding of disease.”

Deployed with the support of Sectra and the NIHR Barts Biomedical Research Centre, Sectra Anonymise and Export opens new avenues for medical research, paving the way for more comprehensive and insightful studies.

Professor Sir Mark Caulfield, Dean of the Faculty of Medicine at Queen Mary University of London, said: “The NIHR Barts Biomedical Research Centre is delighted to have enabled this ground-breaking advancement in access to medical imaging for research. This system represents a pivotal moment in our field — a true game-changer that unlocks the potential of big data while steadfastly protecting patient privacy. This is an exhilarating time of transformation, and I am proud to be part of this innovative journey.”

Sarah Jensen, chief information officer for Barts Health NHS Trust, added: “The diversity and sheer volume of data being integrated means a significant leap forward in our healthcare data research capabilities. NHS professionals are under pressure as they work to deliver the best possible care for patients. Academics and researchers in continual pursuit of medical advancements, can play a key role delivering innovations urgently needed.

“Now, we can securely and safely provide the data they need on a scale not previously possible, whilst safeguarding confidentiality, and without our busy NHS teams being asked to spend time manually removing identifiable information. The possibilities are immense.”

The technological deployment sets the stage for sophisticated AI-powered analysis of medical images. By leveraging advanced pattern recognition algorithms, that are available within the Azure Cloud that hosts the BHDP, researchers will be able to uncover hidden insights and draw more nuanced conclusions from the extensive dataset.

Jane Rendall, UK and Ireland managing director for Sectra, said: “Healthcare professionals at Barts Health have been at the forefront of innovation with imaging technology for many years – using our platform to diagnose and inform care for a great many patients across East London. This latest initiative takes that innovation to another level, securely and safely harnessing imaging data in ways that could radically change how care is delivered. I look forward to seeing the impact emerge for healthcare and patients alike.”

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In the US, nearly one in six adults is living with diabetes—95% of them with type 2. Around the world, the disease affects more than half a billion people. Alzheimer’s disease is another mounting challenge, with 6.9 million Americans over 65 currently diagnosed—a figure expected to double by 2060 and climb to 139 million globally by 2050.

To counter these rising numbers, billions of dollars are being poured into research and treatment—the diabetes drug market alone surpassed $88 billion in 2024. Now, a powerful new force has joined the fight: Artificial Intelligence.

But the real breakthrough isn’t AI working in isolation. It’s how healthcare providers and medical researchers are combining AI with advanced software tools and database technologies—especially knowledge graphs and GraphRAG (Graph Retrieval-Augmented Generation). Together, these innovations are poised to revolutionise how AI delivers accurate, contextually relevant insights; amplifying its potential far beyond what AI alone could achieve.

Reimagining diabetes care for real-world patients

One of the most compelling examples of this innovation comes from US-based telemedicine startup Precina Health, which is pioneering a new approach to supporting patients living with Type 2 diabetes.

Managing this condition is complex, often requiring medication, lifestyle changes, and ongoing monitoring. For older adults in rural areas or patients struggling with technology, getting that support can be an uphill battle, so Precina’s model integrates daily support, medication management, lifestyle coaching, and virtual consultations into one seamless experience.

It’s all quite radical. “We’ve deconstructed the traditional approach to managing Type 2 diabetes and instead built a model that’s designed to work for every single patient,” says Josiah Bryan, the company’s CTO and lead AI researcher. “We’re taking a holistic view, factoring in everything to help each individual more effectively.”

But Bryan is clear: technology is the enabler, not the decision-maker. “I’m not legally allowed to practice medicine. And neither is my technology,” he stresses. “When we say we’re optimizing insulin management, that doesn’t mean the AI is doing some sort of linear regression or tweaking the prescription up and down.” Instead, AI acts as a powerful assistant to the doctor, helping clinicians deliver the ‘high-touch’ care necessary for long-term Type 2 diabetes management.

Using the AI machine to help the human doctor

Precina’s flagship system, the Precina Provider-Patient CoPilot (P3C), is a virtual assistant that joins virtual consultations, providing real-time prompts and personalised insights over video or voice. It offers far more than standard medical guidance, as it taps into the patient’s holistic health journey; in other words, P3C doesn’t just check blood sugar levels; it asks about home life, emotional wellbeing, even pets—because those details can significantly affect health outcomes.

Bryan’s team built an advanced tech stack, combining a relational database (MySQL), a custom LLM based on GPT-4o mini, a knowledge graph queried with Cypher, vector search techniques, and classical search algorithms like Monte Carlo tree search.

Central to P3C’s success is a customised knowledge graph, serving as the organising layer for everything from personal patient history to the latest research. When a clinician speaks with a patient, a real-time transcription runs through GraphRAG (retrieval-augmented generation), cross-referencing past conversations, medical records, and thousands of medical documents. This allows the system to surface the most relevant, personalised insights instantly.

He adds, “When a provider and a patient chat in Google Meet, a plugin extracts the audio, immediately transcribes it and is constantly extracting anything useful in real time by performing a GraphRAG search against the things you’ve talked about with them, their individual medical files, and thousands of pages of documentation that’s been indexed and sorted to give medical advice.”

Bryan sums it up: “That’s implemented with graph technology as a way to give a holistic view of where their patient is at in not just their medical journey but their emotional state.”

Precina’s innovations don’t stop with diabetes; his team has also created a voice-driven AI assistant accessible via a rotary phone, designed to help older adults and Alzheimer’s patients stay connected without needing modern devices.

Harnessing the power of AI

The Alzheimer’s community is also starting to see the benefits of knowledge graph-powered AI. At Cedars-Sinai Medical Center—one of the largest nonprofit academic medical centers in the U.S.—researchers are using graph technology to uncover new insights into the disease, with the goal of better understanding, and ultimately finding more effective ways to treat, Alzheimer’s.

Its initiative, the Alzheimer’s Disease Knowledge Base (AlzKB), gathers insights from more than 20 knowledge sources: genes, genetic links, drugs, biological pathways, symptoms, and more. Built on a graph database platform, it contains 234,000 nodes and 1.67 million relationships, organised around a detailed Alzheimer’s ontology.

From coin tosses to breakthrough

According to Jason Moore from Cedars-Sinai’s Department of Computational Biomedicine,  when used out of the box, generative AI models like ChatGPT are no better than flipping a coin when it comes to complex biological reasoning. For sure, ChatGPT can answer questions about Alzheimer’s or specific genes, but it cannot understand and reason across the dense web of relationships that underpin real medical insights.

To fix that, Cedars-Sinai integrated knowledge graphs with GraphRAG and the emerging concept of ‘Graph of Thoughts’ from Hugging Face. Now, users can query AlzKB without needing to learn specialist database languages like Cypher. Even better, thanks to the knowledge graph, the AI doesn’t just pull random facts, but reasons across the entire Alzheimer’s ecosystem to uncover new patterns and connections.

“Graph gives us a higher-level synthesis of the knowledge than we would get in any other way,” Moore explains. In other words, the knowledge graph doesn’t just store facts, but helps us reason across them, turning fragments into genuine insights.

Searching in the dark

The Cedars-Sinai team is particularly excited about using these tools to uncover hidden risk factors and potential treatments. While much of the research community focuses on the roughly 100 known Alzheimer’s-related genes, Moore is committed to pushing beyond the obvious. “Lots of people are looking under the lamp post of a known gene,” he says. “But what I’m interested in are the novel discoveries ‘over there’ in the ‘dark’—the insights that aren’t yet illuminated by existing research.” By combining AI and knowledge graphs, the team hopes to reveal patterns and connections that might otherwise stay buried.

Early results are promising. Machine learning models, informed by AlzKB, are surfacing previously unrecognised genetic contributors to Alzheimer’s, and even suggesting that common medications like Temazepam or Ibuprofen might be repurposed to treat certain symptoms more effectively.

Promising pathways

For Moore, “I think we’ve shown how we can tailor large language models and accurately query a big Alzheimer’s database, plus demonstrated how we can use the knowledge graph to inform machine learning to give us new ideas for treating this disease.”

The next step is to use knowledge graphs and machine learning pipelines to accelerate discoveries and scale this approach. Soon, researchers will be able to enter natural language prompts like, “Show me the genes linked to this drug and disease,” and the AI will run algorithms, highlight key features, and deliver clear, actionable insights. This could significantly speed up research efforts—and, ultimately, help bring new treatments to patients faster.

Across both these examples—and many others—what’s clear is that developers are increasingly turning to graphs and GraphRAG to unlock the true potential of big medical data and AI. With these tools, researchers and clinicians are no longer confined to searching under the brightest lamp posts where everyone else is already looking. Instead, they can explore the unknown, surfacing hidden relationships and overlooked treatments that could drive real medical breakthroughs.

In medicine, as in life, some of the most powerful answers aren’t found in the obvious, but in the questions no one thought to ask. With this new approach, are we finally learning how to ask them?

Dominik Tomicevic is the CEO of knowledge graph leader Memgraph

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The Labour government’s recently announced “AI Opportunities Action Plan’”sets out an ambitious roadmap to make the UK an “AI superpower”. The healthcare industry is positioned as a key beneficiary and the NHS is expected to play a central role, supported by initiatives such as a proposed national data library. While the plan holds real promise, it also raises significant concerns. The biggest among them: how will sensitive NHS data be handled, who gets access, and how do we ensure the public remains confident in a system increasingly shaped by a “hidden process” of AI?

Potential and pitfalls

There’s no doubt that AI can improve patient outcomes and operational efficiency. Initiatives by NHS England already use AI to identify patients at risk of frequent emergency visits, enabling earlier intervention. Nevertheless, scaling these solutions demands access to vast quantities of health data — a shift that risks undermining public trust and data security if not handled with the utmost care.

Unlike traditional healthcare improvements, AI demands a new kind of contract with the public — one where data is the critical input, and trust is the foundation. If people feel their data is being misused or commercialised without consent, they may withhold vital information, which could lead to undermining both diagnosis and care.

We’ve already seen how data misuse can damage trust. The now-defunct Care.data programme collapsed in 2016, joining a long lineage of NHS digital initiatives from the £12 billion abandoned National Programme for IT to the troubled NHS Digital Strategy—each faltering when reforms prioritized technical solutions over democratic engagement. We can’t afford to repeat these cyclical mistakes that have characterized NHS modernization efforts since the 1990s.

Ethical data use is non-negotiable

AI introduces new layers of complexity into healthcare decision-making. Lacking clear governance creates a danger that private firms might gain monopolistic advantages, using exclusive access to NHS data to train proprietary models. If there is no designated public service, appropriate level of government oversight, or direct benefit to the public, these systems can operate as black boxes making decisions without transparency or accountability.

This is particularly concerning as private investment in AI infrastructure ramps up. The recent £14 billion committed by companies such as Vantage Data Centres and Kyndryl echoes earlier watershed moments in NHS history—from the introduction of General Management under Roy Griffiths in 1983 to the Private Finance Initiative of the 1990s—where private sector logics reshaped public healthcare. These investments could profoundly shape the future of UK healthcare delivery, but only if guided by rigorous data ethics and transparency standards that learn from this complex history of public-private entanglement. Ethical, patient-centric data governance isn’t a barrier to innovation. It is the foundation that makes it sustainable.

Patient control and trust in AI is key

The success of AI in the NHS hinges not only on accuracy and efficiency but on public trust. Patients must be empowered to control their data at every step of the process to understand what information is collected, why, and who has access.

Europe is already moving in this direction through initiatives like the European Digital Identity Wallet (EUDI Wallet) under eIDAS 2.0, which puts data control back in the hands of individuals. The NHS has the potential to do the same, evolving the NHS App into a fully-fledged digital health wallet where patients can grant, and revoke, access based on need and explicit consent.

This is where decentralised technologies, like the W3C Solid protocol, come in. Developed by web inventor Sir Tim Berners-Lee, Solid enables individuals to store their personal data in secure “Agentic Wallets” retaining full control over how it’s accessed and by whom. It’s a technical model designed to resist the extraction-based data economy that has dominated the tech world for the last two decades.

Far from slowing progress, this approach enhances it — by improving data quality, reducing systemic risks, and increasing patient engagement. It mirrors what we already know about good healthcare: outcomes improve when patients feel informed, respected, and in control.

Proven success in the UK

This isn’t a theory. A data-sharing pilot in Greater Manchester that gave clinicians secure access to patient-held records has improved care for vulnerable groups, including people with dementia. This model of secure, patient-controlled data sharing should serve as a blueprint for national policy.

Brigitte West, Product Director at DrDoctor, rightly pointed out that Labour’s plan is “much-needed recognition that AI can be used for operational reasons – so that clinical staff can spend less time on admin and more time delivering care.”

But to fully realise this, AI systems must be designed not just for efficiency but for fairness, accountability, and trust.

Avoiding the trap of “Big Data” thinking

There’s a long-standing temptation in health tech to centralise everything — to build ever-larger repositories under the banner of efficiency. But in reality, centralisation can restrict access, increase security risks, and lead to long-term dependence on external vendors.

Decentralised, standards-based systems like Solid allow for more scalable, secure, and flexible data use. Each patient’s data lives in their own secure wallet, and clinicians access only what’s necessary with explicit consent and clear audit trails. This design dramatically reduces the risk of mass data breaches while increasing transparency and control.

It’s time to move past the idea that innovation means building bigger silos. Real innovation means rethinking the structure altogether by creating a system where data works for people, not the other way around.

A future built on trust

For AI to succeed in the NHS, it must be understandable, explainable, and accountable. Patients need to know how AI arrives at recommendations and be able to trust solutions. Clinicians need the ability to interrogate and challenge its decisions in real-time without negative impacts to patients. That’s how we ensure AI complements, rather than replaces, human expertise in healthcare.

Labour’s “Plan for Change” could indeed deliver a modernised healthcare system. But it must be built on a foundation of trust, not just technology. As we move forward, the term “national data library” must be more than a metaphor — it should reflect a distributed, accessible, user-controlled system where data is borrowed with permission and returned with respect.

Just as the NHS revolutionised access to care in the 1940s—emerging from the crucible of war and the Beveridge Report’s vision of combating the ‘five giants’ of Want, Disease, Ignorance, Squalor and Idleness—we now have a chance to revolutionise how data underpins that care while remaining faithful to those founding principles that have weathered decades of political contestation. If we get it right, we can unlock the full potential of AI in healthcare — while staying true to the NHS’s founding values of fairness, equality, and public good.

By Davi Ottenheimer, VP of Trust and Digital Ethics, Inrupt

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Artificial intelligence (AI) has emerged as a potential solution for many of the issues encountered with COVID-19 pandemic. As research into this technology and its medical applications has increased, it now seems that AI could help prevent and manage the next pandemic. Here’s a closer look at how that might happen.

1. Outbreak Predictions

Any effective medical response hinges on early intervention. AI could facilitate such timely reactions by predicting disease outbreaks before they occur.

Recognizing the early signs of a disease and modeling how it might grow requires a complex understanding of multiple trends and how they interconnect. This kind of analysis is challenging for humans and conventional statistical models, but machine learning excels at it. Some AI applications have already achieved up to 95% accuracy in predicting outbreaks.

In addition to high accuracy, pandemic-predicting AI models work far faster than traditional means. Consequently, world health officials could monitor health reports in near real time with this technology to identify emerging epidemics before they spread. Professionals could then allocate resources to contain the disease before it reaches a tipping point.

2. Transmission Risk Identification

Similarly, machine learning algorithms can highlight areas or practices that may heighten transmission risks. Other models could identify potentially risky populations, such as frequent travelers or asymptomatic carriers. In both cases, AI reveals where change may be necessary to stop the spread of a disease.

A pre-COVID study found that AI monitoring stopped multiple outbreaks at hospitals by detecting possible pathogen transmission routes. During COVID-19, a different AI model nearly doubled the identification rate of asymptomatic travelers to inform safer border and travel policies. Similar applications in the future could help contain diseases before they become pandemics.

It’s not always outwardly clear what behaviors, environments or populations make pathogen transmission more likely until it’s too late. AI makes it easier to determine these risks ahead of time, informing healthier practices to minimize the spread of future infections.

3. Resource Distribution

While early recognition and preventive sanitation measures are crucial, effective interventions also require appropriate resource allocation. Health officials can only stop an outbreak if they know where to deliver which medicines, staff or technologies to enable the greatest possible help. AI clarifies these decisions.

Many health systems have already adapted to become remarkably effective in this regard. Health and Human Services was able to distribute nearly 200,000 monkeypox vaccines within weeks to stop the disease from becoming a COVID-level pandemic. AI can take things further by predicting high-risk areas to reveal which locations demand the most attention.

High-priority areas and treatments may vary between different diseases, making conventional practices fall short. AI can adapt its predictions according to new data, letting it inform health departments of the most up-to-date trends for more reliable triage and resource distribution.

4. Streamlined Vaccine Research

In cases like COVID-19, where there is no existing vaccine, AI can accelerate research and development to enable needed breakthroughs in minimal time. While this does not necessarily prevent pandemics, it does help medical professionals slow or even stop the spread faster.

The COVID-19 pandemic itself highlighted the potential of AI in this area. Pfizer and Moderna both used AI to help find and test potential drug candidates. As a result, they reduced vaccine development times from years to months without jeopardizing patient safety or regulatory assurance.

As more agencies and researchers employ these tools, machine learning will become increasingly reliable and efficient at aiding vaccine development. Other AI models could streamline clinical trials by facilitating data entry or highlighting ideal participant populations, further accelerating the process.

5. Staff Shortage Mitigation

Another barrier to effective pandemic responses that COVID-19 emphasized is the need for additional medical staff. AI can address this obstacle thanks to its ability to automate administrative tasks, enabling limited workforces to perform more work.

One health care system was able to handle 7% more surgical cases despite closing 20% of its operating rooms because of AI-driven efficiency improvements. When AI handles much of the nonmission-critical, data-heavy work, hospital staff can focus on direct patient care. Such a freeing of resources could prove vital in responding to an early outbreak.

Reducing workloads would enable even highly workforce-constrained areas to take on a greater patient volume. As a result, responses would be more ready to manage a sudden outbreak, helping control the issue before it spreads too dramatically.

AI Could Revolutionize Pandemic Preparedness

While many of these use cases are still in the early stages of development, AI’s potential in pandemic prevention is hard to ignore. This technology has already proved itself to some extent throughout the world’s COVID-19 response, and additional research could take the possibilities further.

As these five applications illustrate, AI has many uses in a pandemic outbreak preparedness and response. Health care systems may be able to stop future pandemics from happening by investing carefully in these areas.

By Zac Amos, ReHack

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Within just 2.5 years, Generative AI has disrupted entire industries. For a time, its potential was constrained by the materials the technology was exposed to; then the ability to understand this in context. But with escalating momentum those limitations are being overcome. This is presenting a challenging duality: a future that is already here, yet still largely unknown. In life sciences, where an advantage lost could mean patients missing out, companies are wondering how on earth they move forwards?

The unstoppable force of AI

GenAI is the branch of artificial intelligence that uses everything that is known already to create something new. From early conversational capabilities, through reasoning, the technology is already delivering ‘agentic’ capabilities (goal-driven abilities to act independently and make decisions with human intervention only where needed).

There are early signs too that “innovating AI” is emerging. That’s as AI becomes capable of creating novel frameworks, generate fresh hypotheses, and pioneer new approaches. This creative potential pushes AI from merely processing information to actively shaping the future of scientific discovery, applying it to problems yet to be solved.

At the core of the latest GenAI advances is the accelerated pace of large language model (LLM) development. These deep learning models, trained on extensive data sets, are capable of performing a range of natural language processing (NLP) and analysis tasks, including identifying complex data patterns, risks and anomalies. A growing movement towards open-source GenAI models, meantime, is making the technology more accessible and customisable (alongside proprietary models).

Reimagining scientific discovery and deep research

In life sciences, there are persuasive reasons to keep pace with and harness latest developments as they evolve. GenAI is poised to become a gamechanger in scientific discovery and new knowledge generation – at speed and at scale.

In human intelligence terms, we have already reached and surpassed human expertise levels[1]. Recent advancements in Agentic AI models have even led to the need for a new benchmark[2].

The advanced reasoning promise, a highlighted benefit of DeepSeek’s latest AI model, has enormous scope in science (enabling logical inferences and advanced decision-making). Google and OpenAI both have Deep Research agents that go off and perform their own searches, combining reasoning and agentic capabilities. As reasoning capabilities continue to improve, and as the technology becomes more context-aware, the potential to accelerate scientific discovery becomes real through the creation of new knowledge. The ability to project forward, and consider “What if?” and “What next?”.

Already OpenAI’s Deep Research is optimised for intelligence gathering, data analysis and multi-step reasoning. It employs end-to-end reinforcement learning for complex search and synthesis tasks, effectively combining LLM reasoning with real-time internet browsing.

Meanwhile Google has recently introduced its AI co-scientist[3], a multi-agent AI system built with Gemini 2.0 as a “virtual scientific collaborator”. Give it a research goal, and off it will go – suggesting novel hypotheses, novel research and novel research plans.

Which way now?

With all of this potential, the strategic question for biopharma R&D becomes one of how to keep pace with all of these technology developments and build them into the business-as-usual; how to prepare for a future that is simultaneously already here yet continuously changing shape?

Up to now, most established companies have experimented with GenAI to see how it might address everyday pain points in Safety/Pharmacovigilance, Regulatory, Quality and some Clinical and Pre-Clinical processes. These activities been largely about becoming familiar with the technology, and assessing its trustworthiness and value. Others have gone further, creating lab-like constructs for experimentation.

Yet the hastening pace of technology development, and the intangibility around what’s coming, means that the industry now needs to embed AI more intrinsically within its infrastructure and culture. This is about proactively becoming AI-ready rather than simply “receptive to” what the technology can do.

Being discerning as “experts” hover

In the past, a popular approach to a hyped new technology or business change lever has been to “pepper” associated champions across the business. In this case, some organisations are taking a venture-capital like approach of bringing in non-native AI talent to key roles – visionaries and master-crafters from other industries. But AI is moving so quickly, and its likely impact is so fundamental to life sciences, that experts need to be “neck deep” in it to be of strategic value.

One of the biggest challenges now is the duality companies are now grappling with: the simultaneous need to be ready for and get moving with deeper AI use today, while gearing up for a tomorrow that is likely to look very different. This has widespread “change” implications: at a mindset and method level; and from a technical and cultural perspective – both today and tomorrow.

For this reason, strategic partnerships are proving a safer route – with tech companies that are fully up to speed with the latest developments, are enmeshed in it and its expanding application, and are actively building sector-specific solutions. Even so, companies will need to choose their AI advocates wisely, as “AI washing” is commonplace among consultants and service providers now, as new converts to the technology inflate their credentials in the field.

The good news is that internal IT and data teams are well versed in AI technology today, and have high ambitions for it. The challenge is bringing the technology’s potential to fruition where it could make a difference strategically. This is likely to require involve sitting with an organisation’s real problem areas, and understanding if and how emerging iterations of AI might offer a solution.

About the author

Jason Bryant is Vice President, Product Management for AI & Data at ArisGlobal, based in London, UK. A Data Science Actuary, he has built his career in fintech and healthtech, and specialises in AI-powered, data-driven, yet human-centric product innovation.

[1] Measuring Massive Multitask Language Understanding, Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt (ICLR 2021): https://arxiv.org/pdf/2009.03300

[2] Humanity’s Last Exam, November 2024, https://agi.safe.ai/

[3] Accelerating scientific breakthroughs with an AI co-scientist, Google Research blog, February 2025: https://research.google/blog/accelerating-scientific-breakthroughs-with-an-ai-co-scientist/

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Despite these advancements, the sector still faces challenges in fully realising the potential of digital maturity compared to other industries.

Looking ahead to 2026, several key trends will shape the future of biotech, driven by the integration of digital technologies and advanced analytics.

Artificial Intelligence (AI) in Biotech and Drug Discovery

AI is accelerating the discovery of novel therapeutics by streamlining the identification of promising drug candidates.

Machine learning algorithms analyse vast biological datasets to identify viable molecules, significantly reducing R&D costs and timelines. AI-powered platforms enhance target identification, lead optimisation, and preclinical testing, improving efficiency in biotech research.

Cloud Computing for Biotech Innovation

Cloud and edge computing are revolutionising the scalability and innovation potential of biotech firms.

With enhanced data sharing, real-time collaboration, and seamless AI integration, cloud computing enables faster drug development cycles and robust data security. Companies leveraging cloud-based platforms will gain a competitive advantage in operational efficiency and scientific breakthroughs.

Machine Learning (ML) for Drug Development

Industrialised machine learning is transforming every stage of drug development. From predictive modelling in clinical trials to optimising biologics formulations, ML enhances data-driven decision-making. Advanced algorithms refine predictions, minimise trial failures, and accelerate regulatory approval processes for new therapies.

Real-Time Analytics in Clinical Trials

The demand for more efficient and effective clinical trials has led to greater adoption of real-time data analytics. AI-powered data processing enables biotech companies to monitor patient responses, detect anomalies early, and optimise trial designs. This trend is particularly critical in rare disease research, where patient recruitment and retention remain key challenges.

Investment in Digital Health Technologies

Venture capital is flowing into digital health solutions, particularly those that enhance patient engagement, remote monitoring, and commercialisation strategies. Biotech firms are increasingly partnering with health tech start-ups to develop wearable devices, mobile applications, and AI-powered telemedicine solutions that improve patient outcomes and treatment adherence.

Data-Driven Decision Making

Biotechnology companies are leveraging big data to optimise research, clinical development, and commercial operations. Advanced analytics provide deep insights into patient behaviour, biomarker discovery, and market dynamics, enabling more precise business and scientific strategies. Organisations that successfully utilise data-driven decision-making will drive innovation and maintain industry leadership.

Synthetic Biology and Precision Medicine

Synthetic biology is rapidly emerging as a disruptive field for engineering novel biological systems. By designing customised treatments for genetic disorders, regenerative medicine, and vaccine development, synthetic biology offers unprecedented potential for addressing unmet medical needs with precision and efficiency.

Decentralised and Virtual Clinical Trials

The shift towards virtual and decentralised clinical trials is improving patient accessibility, recruitment, and trial efficiency. AI-driven analytics, remote monitoring tools, and telemedicine solutions allow biotech companies to conduct trials with greater flexibility while ensuring data integrity and regulatory compliance. This trend is redefining the clinical trial landscape, making drug testing more patient-centric.

Quantum Computing in Drug Discovery

Quantum computing is poised to become a game-changer for biotech. By simulating molecular interactions at an unprecedented scale, quantum computers could dramatically accelerate drug discovery. While still in its early stages, this technology holds immense promise for solving complex chemical and biological challenges beyond the capabilities of traditional computing.

AI-Powered Diagnostics and Personalised Medicine

AI is transforming diagnostics by enabling early disease detection and precision medicine. AI-driven imaging, pathology analysis, and predictive algorithms are revolutionising how diseases are diagnosed and treated. As healthcare shifts towards personalised medicine, AI-powered diagnostics will play a crucial role in advancing targeted therapies and improving patient outcomes.

AI-Driven Scientific Research Assistants

AI-powered research assistants are becoming indispensable tools in biotech and life sciences. These digital assistants automate data analysis, literature reviews, and experiment documentation, significantly enhancing productivity. By integrating with cloud computing and real-time analytics, AI-driven assistants foster collaboration, accelerate discoveries, and reduce the workload for human researchers.

Conclusion

As we move towards 2026, the integration of digital and AI-driven solutions in biotech is not just a trend—it is a necessity. Companies that invest in these innovations will lead the charge in scientific and medical advancements, driving faster drug development, improving patient care, and optimising research operations. The future of biotechnology is digital, and those who embrace this transformation will be at the forefront of innovation and discovery.

Kevin Cramer, CEO, Sapio Sciences

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To address these challenges, Resonance Health developed FerriScan®, a system where MRI centres worldwide could send image data scanned either by 1.5T or 3T scanners to expert analysts for accurate LIC measurement. This system has been invaluable for hospitals treating patients and pharmaceutical companies developing and accessing drug efficacy with conditions like thalassemia, who require frequent and precise iron measurements.

Introducing FerriSmart®: The Future of Automated MRI Iron Measurement

In a significant leap forward, Resonance Health has harnessed artificial intelligence to create FerriSmart®, an automated system for calculating LIC from MRI images. This system simplifies the process: radiologists can upload image data into a secure portal hosting the software, which then provides a LIC report within seconds. FerriSmart® maintains the high standards of FerriScan® and is compatible with a wide range of MRI scanners, ensuring broader accessibility.

Liver iron concentration measurement through MRI is a powerful tool in diagnosing and managing conditions like hereditary hemochromatosis and thalassemia. With the advent of FerriSmart®, we are making this technology more accessible, offering rapid, reliable results at a lower cost. As we continue to expand the availability of these advanced imaging solutions, we remain committed to enhancing patient care and supporting clinicians with precise, non-invasive diagnostic tools.

Measuring Iron in Other Organs

MRI technology is also capable of assessing iron in the heart using the CardiacT2* technique which has regulatory approval for routine clinical practice. This method is frequently used in patients with thalassemia to monitor cardiac iron levels. Similarly, for investigational purposes, MRI can measure iron in the bone marrow, another suspected target organ. While not many studies have focused on this, there is evidence that patients with hereditary hemochromatosis can have excess iron in their bone marrow.

The spleen plays a crucial role in recycling iron from old or damaged red blood cells. Excess iron can be found in the spleen, due to different conditions, such as, hemochromatosis, and regular blood transfusion dependent patients, which can lead to several health issues. Accurate spleen iron measure can be important for proper diagnosis and treatment. Early intervention can help manage the condition and prevent complications.  The pancreas is another organ under investigation. Historically, hereditary hemochromatosis has been associated with diabetes, suggesting a link to pancreatic iron overload. Although MRI techniques for measuring pancreatic iron are still investigational, they hold promise for future clinical use. Additionally, iron accumulation in the spleen is typically not expected in hereditary hemochromatosis but is a significant marker in ferroportin disease, which can initially present similarly to hemochromatosis.

The Role of MRI in Assessing Liver Fat

An important complementary measurement to liver iron concentration (LIC) is the assessment of liver fat with HepaFatSmart®. Using MRI, we can quickly determine the presence of fatty liver, which can influence serum ferritin levels. This dual measurement can clarify ambiguities in diagnosing the cause of elevated ferritin, providing a clearer picture of a patient’s condition.

What to Expect During a FerriScan® or FerriSmart® MRI

For patients, the procedure is completely non-invasive, with no need for injections or contrast agents. Patients lie on a scanner bed with a radio antenna placed on their abdomen, wearing earmuffs or headphones to protect against the scanner’s noise. The data acquisition takes about 8 minutes, during which patients must remain still and breathe gently.

We understand that some patients may feel nervous or claustrophobic. To mitigate this, the scanner tube is open at both ends, with fresh air circulating, and patients can always communicate with the radiographer. Patients can also hold a device to alert the radiographer if they feel uncomfortable, and they can enter the scanner feet first if preferred. Closing eyes or using a blindfold, along with listening to relaxing music, can also help ease anxiety.

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Ask Google’s Gemini assistant how many articles have been published on AI in the UK over the past year, and the response is that the data is “too vast and dispersed” to say, but “it’s a very active area of discussion.”

Or, as Jeremy Nettle, chair of the Highland Marketing advisory board, said it’s almost impossible to avoid news about AI and new uses of AI just now: and not all of it is good.

“There has been some pretty horrific AI coming out of the US about Trump, and Gaza,” he said, referring to a widely circulated video showing a golden statue of the US president in a Las Vegas-style Gaza strip.

Putting the full weight of the British state behind AI  

This has not stopped the government making a big bet on AI. Last July, UK science secretary Peter Kyle asked tech entrepreneur Matt Clifford to conduct an AI ‘opportunities review’ and in January the government accepted its recommendations.

These range from creating the power and water infrastructure needed for new data centres, to setting up a library of public data sets to train new models, to creating ‘growth zones’ and running ‘scan, pilot, scale’ projects’ in public services.

Prime minister Sir Keir Starmer made a speech promising to put “the full weight of the British state” behind the plans. And a week later, the government published a ‘blueprint for modern, digital government’ with a big role for AI – and a suite of tools for civil servants, called Humphrey.

But haven’t we been here before? 

While Sir Keir’s speech made a splash, consultant and former NHS CIO Neil Perry pointed out that we’ve heard similar things before. “Previous governments have talked about being leaders in technology and investing in new UK companies, and we’re not seeing it come through,” he said.

As a case in point, the advisory board considered the development of AI in the NHS, which has so far mainly focused on clinical decision support tools and automated reporting for x-rays and scans.

Entrepreneur Ravi Kumar, whose company CyberLiver works on digital therapies for advanced liver disease, said this requires a huge amount of development work, regulatory compliance, and liaison with clinicians.

“Getting AI into a practical, implementable state to work in a clinical setting is very expensive,” he said. “So, when the government talks about creating a home-grown industry, we have to be realistic about the number of companies that will be able to persist long enough to make the grade.”

NHSX, the digital unit set up by former health secretary Matt Hancock, recognised this problem. It set up a National Artificial Intelligence Lab to “bring together academics, specialists and health tech companies” to “work on the biggest challenges” facing the system.

While it did some interesting work, advisory board members felt its record was spotty when it came to growing UK businesses. Some prominent health-AI companies have lost out to over-seas competitors or left for bigger markets themselves. And there’s no money on the table for anything similar, today.

Decision support tools: company and clinician experience has been mixed 

From a clinical perspective, radiology expert Rizwan Malik also felt mistakes had been made. “Millions have gone into AI in the NHS, particularly in the diagnostic imaging space,” he said. “But we gave a lot of companies money to develop things without asking what the business case was.

It was: ‘AI is the answer, now what’s the question?’” As an example, he said AI is good at detecting abnormalities in chest x-rays, but so are radiologists, who now have to check both the chest x-ray and the AI’s interpretation of it. Which adds to their workload, for unclear benefit.

“That’s why I say we need business cases,” he said, underlining that those business cases need to think about how AI can help individuals to work smarter, not just harder.

“At the moment, it’s always: ‘how many more scans will you be able to do with this, Rizwan?’ It’s never: ‘how will this make your practice safer’? Or: ‘how will it improve outcomes?’ Or: if it can do those things, is it worth the price tag?”

Coming soon: generative AI 

The AI that everybody is talking about right now, though, is generative AI or the large language models like OpenAI’s ChatGPT, Google’s Gemini, and the open-source Llama.

These take large-scale data inputs and use them to predict what character is most likely to follow another character, creating (or generating) new outputs in the process, such as a response to a web query, or a block of code, or a social media post.

Jason Broch, a GP and CCIO, said he was worried about putting LLMs into clinical spaces. “We have used Microsoft Copilot for administrative work,” he said. “Some of the people who take minutes at meetings have tried it, and they have found it can cut the time involved in producing a report from three hours to one hour.

“But that is because they are experts at producing reports. In a clinical setting, we don’t know whether the output from an LLM is good, or not.”

People are using AI assistants because they are free or, increasingly, built into consumer software packages. But they’re not transparent. “We don’t really know what data [a model] has been trained on,” he said. “It produces an output, but we don’t really know how it does that.

“If you run a prompt again, it can come up with a completely different output. We need guardrails for the use of LLMs. Or we need healthcare specific models, because if we are going to scale the use of these tools in the NHS, we need to be able to trust them.”

Sam Neville, a nurse and CNIO, agreed. “Trust is an important word,” she said. “Staff do not trust this technology, and patients don’t trust it either.

“If we tell patients that we are going to put their information into a third-party system like a patient portal, they don’t like it. If we tell them that the NHS is looking at AI, they think Trump video. They think we are just going to make things up.”

Where’s the regulation, where are the guardrails? 

David Hancock, a consultant and interoperability expert, said he is worried that the government is paying far too little attention to these issues, in its dash for growth and productivity gains.

The EU, he pointed out, has passed legislation (the EU AI Act) to ban certain uses of AI and encourage transparency and labelling. Whereas the UK’s approach does not have the same level of emphasis on human rights protections.

“The UK government has said that it sees not being in the EU as an opportunity, so it sounds as if it is not going to go down the same route,” he said. “It looks as if it will allow this to be more commercially driven, as it is in the US.”

Nicola Haywood-Cleverly, a former CIO and consultant who also works as an NHS non-executive director, felt the NHS also needs to think much harder about the data that is being fed into these tools. “We all know there is a lot of concerns regarding data quality out there,” she said. “If we want to train good models, we need better data to train them on.”

The NHS will also need better infrastructure, she added, to make sure new tools are properly embedded into clinical workflows, and clinicians are clear about when they are using AI outputs.

Neil Perry said this raised the question of how the NHS can make sure new AI tools are implemented safely. “I have just joined one of NHS England’s panels looking at refreshing DCB 0129 and 0160 [clinical risk management standards for companies and organisations looking to roll-out digital systems].

“One of the first questions asked was: is the standard fit for AI? And the answer is: not really. In fact, it’s not really fit for two-week sprints [software development cycles]. When DCB 0129 and 0160 were written, the NHS was lucky if it got a system update yearly. We need to refresh methodologies. And we need to educate and include clinicians and patients.”

Jane Brightman, a social care expert who works at Skills for Care, said social care staff and people drawing on care also need to be brought into the picture. The social care sector is doing some work with the University of Oxford on the “ethical use of AI” that should lead to some basic principles for its development and deployment.

Time to think clearly on NHS AI

Jason Broch also suggested that the NHS needs to avoid some of the mishaps that it has made with AI to date by thinking clearly about what LLMs are good at and where that can resolve some of the challenges that the NHS is facing.

“We need to get cleverer about language,” he said. “We talk about LLMs as if they generate meaning. But they don’t. We talk about ‘hallucinations.’ But the LLM isn’t hallucinating. It’s doing what it’s meant to do, it’s just that we don’t like the output. So, we need to understand that these things are a great language tool, but they are not a cognition tool.”

Following on from this, he suggested the best uses of generative AI in the NHS might be in helping with language tasks, such as summarising a mass of patient records before an appointment, or generating communications.

Advisory board members had many other ideas for using AI alongside other technologies. Sam Neville said she is looking at an AI tool that can review trends in outpatient appointments to identify patients who may be at risk of ‘DNAing’ or not attending appointments.

David Hancock said the NHS could usefully run something similar over its patient reported outcomes or PROMS data, to find out what it is getting for its money.

Highland Marketing chief executive Mark Venables said it is working with an AI firm that can take vital signs information from patients waiting for admission and alert clinical teams to signs of deterioration.

Neil Perry suggested that similar technology could be used in A&E, to make long waits safer. “We can argue about whether all of this is AI, or whether it is just technology,” he said. “The point is that it automates what we do anyway, accurately enough to trigger an alert that leads to a human decision.”

Build out, take people with you 

The biggest problem, he said, is that in the current NHS financial environment projects like this are difficult to implement. He argued that instead of making big statements about NHS AI, the government should focus on where it could address the big “volume” issues and use its buying power to secure solutions for the whole system.

“Back in the days of the national programme for IT we used to talk about ‘ruthless standardisation’,” he said. “Perhaps we could do a bit of that now. Build AI tools into the NHS App and 111 services to detect and diagnose conditions, or read vital signs from a selfie and direct patients to the most appropriate service.

“The technology is available; we need to make it meaningful, useful and used at scale.” Meanwhile, Rizwan Malik argued there were some good things to have come out of the faltering start that has been made on AI so far.

“The upside is that we have experience of decision support tools,” he said. “So, perhaps we can start talking about the best way to use them. Instead of sending everybody for an MRI or CT scan we can start talking about which patients really need them. Or which patients need to go first.

“We could make incremental improvements. For the millions invested so far, we cannot say we are at the forefront of AI in healthcare in the UK, or that we are supporting UK plc. But we do have a workforce ready to have meaningful conversations going forward.”

About the Highland Marketing advisory board        

The Highland Marketing advisory board includes: Jeremy Nettle (chair), formerly of Oracle and techUK; Cindy Fedell, regional chief information officer at North western Ontario Hospitals, Canada; Nicola Haywood-Cleverly, a former integrated care system chief information officer, non-executive director for NHS foundation trusts, and health tech strategist and advisor; Andy Kinnear, former director of digital transformation at NHS South, Central and West Commissioning Support Unit and now consultant at Ethical Healthcare; Ravi Kumar, health tech entrepreneur and chair of ZANEC; Dr Rizwan Malik, consultant NHS radiologist and director of SMR Health Tech Consultancy; James Norman, EMEA health and life science director, Pure Storage; Ian Hogan, CIO at the Leeds and York Partnership NHS Foundation Trust; Neil Perry, former director of digital transformation at Dartford and Gravesham NHS Trust and now director at Synergy Digital Health Innovation; David Hancock, digital health strategist specialising in interoperability; Jane Brightman, director of workforce strategy at Skills for Care; Jason Broch, GP and CCIO at Leeds Health and Care Partnership.  

About Highland Marketing           

Highland Marketing is a specialist marketing, communications, market access and consultancy agency, focusing on the health tech and med tech industries. We offer an integrated range of services, covering all elements of the marketing mix, to help organisations achieve their goals by ensuring their messages are heard, understood, and acted upon by their chosen audiences. Our highly experienced and well-connected team has deep knowledge of health and care technology, strong contacts in the industry, and is well-versed in delivering effective campaigns and content. We support clients across the NHS and EMEA healthcare markets and work with clients looking to expand from the UK into international markets, and with overseas companies looking to enter the UK market.       

Website: www.highland-marketing.com  X: @HighlandMarktng

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• Taking notes to record interactions such as meetings and phone calls with clients.
• Updating and managing a client’s case file and records.
• Preparing reports for supervisors or for legal purposes.
• Completing general paperwork or forms for legal and administrative purposes.
• Scheduling meetings and visits on a daily basis.

And much more. However, from things like digital case management systems, to telehealth and online counseling, to being able to complete online degrees, like an online MSW degree, technology has come a long way in transforming the social work sector, and we now have access to technologies that could potentially cut the time that social workers spend at their desks. By integrating AI into social work, we have the opportunity to create more efficient and accurate systems that reduce the administrative burden, freeing up social workers to do more of what they do best—spend time helping people.

AI administrative support for social workers

At the moment, 28 councils in England are using a specialized AI tool called Magic Notes that records meetings between clients and social workers and produces a meeting summary file. The AI tool works much like other AI meeting-summary assistants (you might be familiar with tools like Microsoft’s CoPilot, Google Gemini’s “Take notes for me” function, and Zoom’s AI-assistant), by listening to the session and producing a complete transcript, before using AI to summarize that transcript into easily digestible notes. At the end of the session, Magic Notes emails the practitioner a transcript, summary and AI-powered suggestions for what to include in case notes.

Early results show that implementation of the Magic Notes software has resulted in huge gains in efficiency and reduction in time spent on administrative tasks, with one council noting that the AI reduced admin time by a dramatic 48%. Not only that, but the AI has helped social care workers produce reports that are more accurate and evidence-based, leading to a higher quality of output that more efficiently communicates findings to the supervisor or other parties reading the reports. While Magic Notes is still in testing stages in the UK, and is yet to reach our shores here in the US, its success overseas shows promise that it, or something like it, might soon come to America.

Things to keep in mind

While the growth of AI in the social care space has potential to reduce practitioners’ workloads, it must also be used with caution, to reduce errors and other potential problems.

Data privacy and security

Social care is often also healthcare, and with healthcare rapidly becoming a top target for cyber criminals, it’s more important than ever that confidential patient data used by AI tools and companies is securely and privately held. This means that AI companies that offer note-taking or schedule-supporting AI tools should also reassure clients that data is held ideally onshore here in the States, and that that data is used only for a temporary period of time to generate relevant artefacts that are then emailed out to practitioners, before the initial unprocessed data is deleted.

Accuracy

AI, like any other tool, is fallible, and can make mistakes like any human. It also can “hallucinate” or make up information that doesn’t exist, but sounds accurate. It’s crucial that practitioners not totally rely on AI, but either use AI to support their own work (i.e., using the summary notes to create their own reports, or using AI-suggested templates to make their report writing faster), or read and closely check over AI-generated reports before submission.

Biases

AI is trained on historical data, and that data can reflect existing biases related to race, gender, status, or disability. When using AI, practitioners should note that AI tools could unfairly prioritize or de-prioritize specific clients—for example, an AI algorithm used by a Pittsburgh-area child protective services agency was accused of disproportionately flagging families with disabled members for investigation. Other potential biases include an AI lacking crucial context for a certain case, and therefore creating irrelevant or overly simplistic results.

At the end of the day, although AI has the potential to change the lives of many social workers, the technology is still new and should be used carefully.

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