The future of AI in the NHS: integrating innovation into everyday practice

The future of AI in the NHS: integrating innovation into everyday practice

by iatroX AI

The future of AI in the NHS: integrating innovation into everyday practice

Introduction

In the aftermath of COVID-19, the National Health Service (NHS) faces unprecedented pressures – from record-breaking patient backlogs to a workforce stretched thin by burnout (House of Commons Library, 2025, Medscape News, 2021). These challenges have accelerated a push for digital transformation in healthcare, with leaders viewing technology as vital to the NHS’s sustainability and recovery (UK Parliament Committee, 2022, House of Commons Library, 2025). Past decades saw uneven progress – with many outdated, fragmented systems still in use – but the pandemic underscored the need to modernise quickly; telemedicine uptake surged and new digital tools emerged out of necessity (UK Parliament Committee, 2022). The NHS Long Term Plan identifies AI as crucial “to help clinicians in applying best practice, eliminate unwarranted variation… and support patients in managing their health” (NHS England, 2019). Against this backdrop, we explore how AI can be thoughtfully integrated into everyday clinical practice to transform challenges into opportunities.

Current challenges in clinical practice

Frontline clinicians today work under intense time pressure and cognitive load. Even before the pandemic, NHS staff were struggling with unsustainable workloads – a 2021 parliamentary report warned that workforce burnout had reached an “emergency level” and posed an “extraordinarily dangerous risk” to the health service’s future (Medscape News, 2021). Studies show that physicians spend nearly twice as much time on electronic health records (EHRs) and desk work as they do in direct patient care (BMJ, 2020). Every hospital department typically operates a patchwork of software – for example, an ordering system separate from the notes system, which is separate from guideline databases – forcing clinicians to log into multiple platforms and search for information across siloed sources. In one year, nearly 4 million patients had encounters at hospitals that couldn’t access their previous records, leading to gaps in information at the point of care (Imperial College London, 2019). As one report starkly concluded, “policy-makers must act with urgency to unify fragmented systems… or risk the safety of patients” (Imperial College London, 2019).

Clinicians also face the challenge of keeping up with a relentless expansion of medical knowledge. It is estimated that the volume of medical information doubles roughly every few months in the modern era (Research Publication, 2020). New research findings, drug updates, and guideline changes emerge constantly, yet busy healthcare professionals have limited time to digest them. In practice, accessing the latest evidence-based guidance during a clinic or ward round is often cumbersome – relevant protocols might be buried in lengthy PDFs or hidden behind multiple login screens. This delay between evidence generation and adoption can span years, meaning patients may not immediately benefit from the best available knowledge. All these factors contribute to cognitive overload: clinicians must make critical decisions in high-pressure environments while sifting through overwhelming amounts of data and administrative noise. It is little surprise that adherence to clinical guidelines varies and errors or omissions occur when support systems do not match the workflow (BMJ, 2020).

The promise of AI in the NHS

Artificial intelligence offers a way to help clinicians cut through complexity by providing timely, data-driven support. Unlike traditional software, modern AI can analyse vast datasets and instantly highlight what is relevant to a given clinical scenario. This capability has immense potential to improve efficiency and accuracy in healthcare. For example, at Moorfields Eye Hospital a collaboration with DeepMind demonstrated that an AI system could analyse retinal scans to detect eye diseases with the accuracy of expert ophthalmologists (Wired, 2018). In dermatology, an AI medical device for skin lesion assessment – such as the DERM system by Skin Analytics – is already deployed in NHS pathways; in an evaluation of over 30,000 lesion assessments, the AI performed at least as well as consultant dermatologists in identifying benign versus suspicious lesions (NHS England Blog, 2023). Early results are promising: not only did the AI match expert accuracy, but it also showed potential to save money (£2.30 saved for every £1 spent) through efficiency gains (NHS England Blog, 2023).

A review led by Dr. Eric Topol painted a future in which automation and AI “enhance” healthcare professionals, giving them more time for patients (Guardian, 2019). AI-based clinical decision support systems (AI-CDSS) in primary care have been shown to improve clinical management and patient safety while reducing physician workload (MDPI Journal, 2023). Multiple investigations have also noted gains in diagnostic consistency and adherence to guidelines when AI assistance is available (BMJ, 2020). Moreover, the NHS AI Lab’s AI in Health and Care Award program has funded dozens of AI technologies, with early deployments reaching tens of thousands of patients – for example, an AI stroke diagnostic tool supported care for over 17,000 stroke patients, while another project for managing blood pressure reached 25,000 patients (NHS AI Lab, 2021). NHS England’s former chief Simon Stevens remarked that “we are seeing an artificial intelligence revolution” in healthcare, a sentiment that reflects the growing optimism for AI to reduce waiting times and ease the burden on clinicians (NHS England, 2019).

How our platform supports clinicians

We set out to turn the theoretical benefits of AI into concrete support for NHS clinicians at the point of care. Our platform leverages retrieval-augmented generation and prompt engineering to deliver fast, reliable answers during clinical workflows. When a clinician poses a question – for example, “What are the latest NICE guidelines on managing acute asthma in a child?” – our system immediately searches a curated index of trusted sources and retrieves the most relevant guidance. It might pull the specific section of a NICE guideline, a dosing recommendation from the British National Formulary (BNF), and a NICE Clinical Knowledge Summary paragraph – all within seconds. The system then synthesises this information into a concise, conversational reply that is aligned with current guidelines, with all sources clearly cited. Emerging research shows that customising AI with clinical knowledge significantly improves its accuracy and utility for medical queries (MDPI Journal, 2023). Rather than acting as a generic chatbot, our tool speaks the language of NHS evidence – it knows that when asked about asthma management, the answer must adhere to NICE and British Thoracic Society recommendations.

Our conversational interface is a deliberate design choice to reduce the cognitive burden on clinicians. Busy doctors do not have the time to wade through lengthy protocols or multiple websites in the middle of a clinic session. With our platform, a clinician can simply ask a question in plain English (or use voice input) and receive an instant, structured response, much like conversing with a knowledgeable colleague. Early user testing has shown that this natural Q&A format integrates evidence-based knowledge into decision-making seamlessly, without disrupting the clinical flow. By providing answers in a dialogue style, the system can ask clarifying questions – such as “are you dealing with a pediatric patient?” – to narrow down the advice, mimicking the back-and-forth of a specialist consultation. This interactive guidance helps prevent information overload by providing just-in-time, relevant insights rather than overwhelming the user with dense guideline documents. It is well established that if clinical decision support is too intrusive or time-consuming, clinicians will avoid using it (UK Parliament Committee, 2022).

Under the hood, our platform is built around authoritative data sources that NHS clinicians trust. We integrate content from the National Institute for Health and Care Excellence (NICE), the British National Formulary (BNF), and the NICE Clinical Knowledge Summaries (CKS). These databases are kept up to date so that when a guideline changes or a new drug is approved, our platform’s knowledge is refreshed accordingly. By confining the AI’s reference materials to vetted sources – and clearly citing them in each answer – we maximise clinical validity and transparency. Clinicians can see exactly which guideline or textbook the AI is quoting, allowing them to double-check details and build confidence in the evidence-based recommendations. This approach directly addresses concerns about transparency; research has shown that lack of explainability is a major reason clinicians may be wary of AI systems (JAMA, 2021). We have also introduced additional modes – such as quiz mode and brainstorm mode – to further support clinicians in learning and decision-making. Quiz mode transforms the platform into a teaching aid by quizzing users on topics like the management of diabetic ketoacidosis or diagnostic criteria for certain conditions, while brainstorm mode supports clinical reasoning by offering a list of differential diagnoses or management plans for complex cases. Both features are designed to reinforce learning in a low-stakes environment and ensure that no potential diagnosis is overlooked.

Expert perspectives and research insights

Implementing AI in healthcare requires meeting strict clinical standards, regulatory requirements, and earning the trust of professionals and patients. The NHSX Code of Conduct for data-driven health and care technology outlines 10 key principles – including transparency in data use, fairness in algorithms, and robust evidence of effectiveness – which are essential for any AI tool in the NHS (NHSX Code of Conduct, 2019). Research on explainable AI (XAI) in healthcare suggests that when clinicians understand how AI recommendations are generated, they are more likely to trust and use the technology (JAMA, 2021). Bodies such as the NHS Artificial Intelligence Advisory Board advocate that AI in medicine should be “clinician-led”, developed in close collaboration with end users to complement clinical judgement rather than replace it (NHS England, 2022).

Interoperability is another critical consideration. An AI solution will have limited impact if it operates in isolation; it must integrate seamlessly with existing systems. The Digital Health and Care Plan 2022 highlights the importance of ensuring that at least 90% of NHS trusts have modern, interoperable EHR systems by 2023, with open standards like FHIR enabling data sharing across platforms (Digital Health, 2022, NHS Digital, 2021). This interconnected infrastructure will allow AI tools to be embedded within clinicians’ workflows – for instance, a GP could query our platform directly from their consultation software. Alongside technical integration, ethical and equity considerations must be at the forefront. AI must be rigorously tested for biases and include fail-safes to prevent harm, while patients’ rights to know when AI is involved in their care must be respected. Initiatives such as the Topol Review have recommended investing in the workforce’s digital skills so that clinicians can effectively engage with and supervise AI systems (Guardian, 2019).

Future outlook

Looking ahead, the integration of AI into everyday NHS practice is poised to deepen. We anticipate a future where AI-driven support is as ubiquitous as the stethoscope – a natural component of every consultation, ward round, and care pathway. One major trend will be the seamless integration of AI tools into EHR systems. Instead of operating as standalone apps, AI capabilities will be embedded directly within the clinical software that clinicians use daily. Imagine opening a patient’s electronic record and having an AI assistant automatically summarise key aspects of their history or highlight potential drug interactions in real time. With over 90% of trusts expected to have modern EHRs by the end of 2023 (Digital Health, 2022), the stage is set for AI to overlay its functionalities on top of patient records.

Another key development will be enhanced mobile and remote access. As the NHS increasingly leverages mobile apps for both patients and clinicians, AI support will extend to smartphones and voice assistants, allowing care providers to access guidance during home visits, ambulance calls, or when away from a desktop. Early iterations already point to applications where AI triages remote monitoring data from wearable devices and alerts clinicians when intervention is needed. Predictive algorithms, for example, could detect early signs of deterioration – such as subtle changes in oxygen saturation – and prompt timely action (AHRQ PSNet, 2020). Furthermore, studies indicate that automation and AI can “give health and social care practitioners back time to care” by streamlining repetitive tasks (Health Foundation, 2022). Such efficiency gains, combined with tighter integration with clinical systems, point to a future where the NHS operates more responsively and effectively.

Adaptive and personalised AI is another promising frontier. While current systems largely apply standard models, the next generation of AI will be capable of learning from local outcomes and individual clinician preferences. Over time, such systems could offer tailored recommendations that reflect both evidence-based guidelines and the nuances of local practice. This evolution will require rigorous oversight and ongoing validation, but it offers the potential to transform AI from a static reference tool into a dynamic, continuously learning partner in clinical care. With national strategies – including the NHS Long Term Plan – increasingly emphasising digital innovation, it is only a matter of time before such adaptive AI becomes embedded in routine practice.

Conclusion

The challenges facing the NHS – from overloaded clinicians to fragmented digital systems and information overload – call for innovative solutions, and AI is emerging as a critical part of the answer. Evidence-based AI, when thoughtfully integrated into clinical workflows, can reduce cognitive burden, close knowledge gaps, and streamline care delivery. Our experience in developing our platform has reinforced a core principle: that providing guideline-aligned, real-time insights is not a tech luxury but a necessity in addressing the NHS’s evolving needs.

However, the journey to an AI-enhanced healthcare system must be navigated with care. It requires a commitment to ethics, transparency, and rigorous evaluation. AI tools must be continuously validated, integrated with existing systems, and developed in close collaboration with clinicians. As Sir Simon Stevens noted, the NHS aims to be “first out of the blocks” in safely harnessing AI (NHS England, 2019). Ultimately, a future where clinicians have more time to care – where innovative tools support rather than replace human judgement – is within reach.

We invite feedback and engagement from the clinical community. The success of any AI solution depends on close collaboration with those on the front lines. By integrating innovation responsibly into everyday practice, we can help the NHS overcome its current challenges and build a stronger, smarter system for the future.