Having a good grasp of NHS Hot Topics is incredibly important for your UK Medical School Interviews, in particular, the idea of the modernisation of medicine through Artificial Intelligence (AI).
The NHS is often described as overstretched, and the government sees Artificial Intelligence (AI) as a central part of the solution. The July 2025 10-Year Health Plan for England put AI at the heart of a deliberate shift from analogue to digital working, making it one of the most important NHS hot topics for the 2026 interview cycle.
AI is a growing field in healthcare, and knowledge of its current use and future potential could set you apart from other candidates at your UK Medical School Interviews.
This comprehensive guide will teach you everything you need to know about Artificial Intelligence, how it can benefit the healthcare sector and the ethical considerations associated with this.
AI in Medicine and the NHS: What You Need to Know for 2026
Artificial Intelligence (AI) is transforming healthcare by automating administrative tasks, improving diagnostics and streamlining operations. By 2026 the NHS has moved well beyond pilots, with AI scribes in everyday GP and hospital use and AI imaging deployed in stroke and cancer pathways.
The UK government has made AI a central pillar of the 2025 10-Year Health Plan for the NHS, building on real-world use that has expanded rapidly since 2023, which is why AI is now one of the most important NHS hot topics for your interview.
As of 2025-26, AI is already drafting clinical notes for doctors (ambient scribes), reading mammograms and CT head scans, powering virtual wards, and flagging patients at risk of chronic illness.
AI offers faster data analysis, streamlined hospital operations, and earlier disease identification, but it raises concerns about bias, data privacy, and patient trust.
The use of AI in healthcare raises ethical issues, including data privacy, bias, accountability, and the balance between AI and clinical judgment.
Artificial Intelligence, or AI, is a self-learning technology that allows computers to do tasks previously required to be done by humans. It describes computers having the ability to “think like humans”, and execute tasks such as pattern recognition, decision making and judgement.
Artificial intelligence, or AI, is when a computer or machine can perform actions that would be thought to require human intelligence.
As of 2025-26, the UK government has made AI a core enabler of the 10-Year Health Plan for the NHS (published July 2025), which commits to scaling technology such as AI scribes to free clinicians from administrative burden. This built on earlier dedicated AI funding, including the NHS AI Diagnostic Fund.
What Is the Current Role of AI in Medicine and the NHS?
The relation AI has to medicine is becoming increasingly important as technology is implemented to reduce waiting times, give more personalised care and much more.
One of the main goals of the NHS Long Term Plan is prevention and more accurate diagnosis by 2030, and AI is the gateway to making this change.
Below are some of how AI is changing the medical field, and how this impacts the doctor-patient relationship!
AI in medicine is a developing field, with limited current use deployed within the NHS but vastly invested in by private healthcare fields.
The scope for AI application in the medical profession includes:
Treatment - AI can be used in the development of personalised treatment plans for patients, taking into account their comorbidities and past medical history.
Surgery- Engineers are working on AI Robots capable of performing surgeries, such as routine, non-complex operations. Through reduced human error, these may improve patient’s surgical recovery outcomes.
Drug Discovery- AI can be used in identifying new drug targets and screening drug candidates.
Admin - Operational efficiency within a healthcare system can be used by automating administrative tasks using AI.
The 2025 10-Year Health Plan describes AI as a core enabler of NHS reform, with AI scribes singled out to "liberate staff from their current burden of bureaucracy and administration". Citing the plan, rather than older one-off funding announcements, is a strong, current fact to bring to your MMI interview.
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The single biggest real-world change since this guide was first written is the rollout of AI scribes, also called ambient voice technology (AVT). These tools listen to a consultation, then automatically draft the clinical notes, letters and codes for the clinician to check and approve. The aim, set out explicitly in the 2025 10-Year Health Plan, is to cut the documentation burden so doctors and nurses can spend more time with patients.
Adoption has been fast. By 2025-26, ambient scribing products are in use across dozens of NHS trusts and the majority of GP practices in England, supported by national guidance from NHS England on safe deployment. A Great Ormond Street Hospital trial reported that clinicians spent far less time on paperwork and more time making eye contact with families. This is an ideal example to cite at interview because it is concrete, current, and shows AI augmenting rather than replacing the doctor.
In my experience as a GP, the day-to-day appeal of these tools is obvious: the administrative load is one of the main drivers of burnout, and anything that returns minutes to each appointment is valuable. The caution I would raise at interview is around accuracy, consent (patients must be told a tool is listening) and the risk of automation bias, where a busy clinician simply signs off an AI-generated note without reading it carefully.
AI use in the NHS has expanded rapidly and is now embedded in several real pathways. Key live examples in 2025-26 include AI scribes, AI imaging in stroke and breast screening, and remote monitoring. Specific areas where AI tools are deployed include:
Support radiologists through screening X-ray images for red flags or concerning features. This helps focus the radiologist's time on more complex cases or ambiguous scans.
Virtual Wards - Provide hospital-level care and treatment from healthcare staff, whilst patients are still at home. AI facilitates this through its smart monitoring of patient’s health parameters.
AI algorithms are being used to screen mass amounts of patient data to highlight patients at risk of developing heart disease and other chronic illnesses.
AI Supporting clinical decision making.
AI can access a breadth of information about treatment plans, as well as patient history rapidly - for this reason, it gives doctors context to make well-informed decisions about their patients.
For example, organisations like DrDoctor have products that assist with streamlining information (patient details, family history, ensuring all information is on one database) - freeing up time to see more severe patients in clinics. This aids doctors and nurses with the triage of different patients, as AI can flag up concerns automatically.
Radiology & AI
AI imaging tools are now used across NHS radiology to speed up and standardise scan analysis. In acute stroke care, software such as Brainomix 360 analyses CT head scans in minutes to flag major strokes; a Lancet Digital Health evaluation of more than 450,000 patients across 107 English hospitals, published in December 2025, found thrombectomy rates roughly doubled at sites using the technology. Other tools auto-contour radiotherapy scans, leaving clinicians to check and refine rather than draw by hand.
This speeds up diagnosis and allows doctors to spend more time with their patients.
Analysing mammograms - supporting radiologists in their assessments as well as increasing the time spent with their patients - directly benefiting the doctor-patient relationship.
Virtual wards & AI
The MDT (multidisciplinary team) can remotely monitor a patient’s health while being cared for from home. For example, a remote monitoring Hub was set up in the North West region to support patients with COPD, heart failure, type 2 diabetes and COVID-19.
It is available 365 days of a year and supports around 5500 people daily. The results have shown an increase in capacity and a reduction in hospital admissions for chronic patients.
Prostate Cancer Screening & AI
In cancer screening, the NHS launched the EDITH trial (Early Detection using Information Technology in Health) on World Cancer Day, 4 February 2025. Backed by around £11 million via the NIHR, it invites nearly 700,000 women to test five AI platforms across 30 screening sites. The trial is assessing whether AI can safely replace one of the two radiologists who currently read every mammogram, potentially freeing over 100,000 clinician hours a year while keeping a human in the loop.
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A system of scanning a patient’s heart, and creating a personalised 3D model to observe blood flow, immediately identifying blockages. This process would normally be carried out with a coronary angiogram, a type of X-ray used to examine blood vessels (a dye is injected into the specific area, illuminating the vessels). Using HeartFlow is, therefore, less invasive, and faster and makes visualising the blood vessels easier.
AI in the NHS: The 10-Year Health Plan and Government Policy
The NHS 10-Year Health Plan and the Analogue-to-Digital Shift
The most important policy document to know is the 10-Year Health Plan for England, published in July 2025. It sets out three big shifts: from hospital to community, from sickness to prevention, and from analogue to digital. AI sits at the centre of the digital shift, described as a core enabler that will, among other things, scale AI scribes to free staff from bureaucracy and support faster diagnosis.
For interviews, citing the 10-Year Health Plan signals that your knowledge is current rather than recycled from older 2023 funding headlines. A balanced candidate will note both the ambition (a single patient record, more AI-supported diagnostics, prevention at scale) and the realism required: the NHS has a long history of struggling with IT delivery, and the plan depends on funding, workforce digital skills, robust data governance and public trust to succeed.
The UK government has announced its commitment to the integration of AI into the NHS.
In hopes of modernising healthcare, the government has allocated a substantial investment of £21 million to support the incorporation of AI tools and technologies within the NHS.
This finance aims to fund the development of new and upcoming AI systems which can benefit patient care in the NHS, as well as integrate existing AI tools into the current NHS infrastructure.
These steps aim to bring the NHS into the modern technological age, an area which has long been neglected within the NHS budget.
AI in Medicine: Pros and Cons (Benefits and Risks)
For your medical school interview, you may be asked to weigh up the use of artificial intelligence in healthcare.
This will require a background level of knowledge of the advantages and disadvantages of this technology.
Advantages of Artificial Intelligence in Healthcare
Faster data analysis - AI allows the processing and analysis of vast amounts of medical data, providing improved diagnostic times.
Streamlining of hospital operations - AI can be used to automate administrative tasks. This reduces the administrative burden on healthcare workers, allowing them to focus on patient care.
Earlier identification of at-risk patients - AI can be used to rapidly analyse patient databases and compare their characteristics to scoring systems, like Q-RISK, to identify patients at risk of disease earlier.
Telemedicine - AI can be used within online platforms to allow patients to access medical advice, as well as allowing remote consultations.
Drug Discovery & Personalisation of Treatment - AI can be used to identify potential medications for a patient’s condition and predict their efficacy, saving time and resources based on the patient’s characteristics.
Disadvantages of Artificial Intelligence in Healthcare
Bias- Given that AI systems are trained using data, it means that AI systems are very liable to bias. Given that there is historical medical negligence in some communities, this may mean that the medical suggestions or treatment provided by AI are discriminatory.
Security and data privacy - AI systems require data from patients to learn and complete their required tasks. If these systems are storing and processing large amounts of personal data, there is a risk of cyberattacks or data breaches. Data protection is an integral concept - the safeguarding of sensitive and personal data that is collected, processed and stored by AI systems. This can be patient details, their history and any other confidential information.
Patient Trust - Many patients struggle with the use of technology and prefer to see doctors face-to-face. This is a common theme with many patients feeling that Artificial Intelligence is not trustworthy enough for it to be used in their healthcare.
Foundation Models and Patient Data: The Foresight Case
A live 2025 example that crystallises the data-privacy debate is Foresight, a generative AI "foundation model" trained on de-identified records from around 57 million people in England, developed with UCL and King's College London. The idea is that it learns the patterns of medical timelines, almost like an auto-complete for health events, to help predict and prevent disease at population scale.
However, NHS England paused the programme in 2025 after GP leaders and privacy experts raised concerns about whether patients had been adequately consulted, and whether even "anonymised" data could in theory be re-identified. The Information Commissioner's Office was asked to review it. Foresight is an excellent interview example because it captures the central tension: the enormous potential of AI trained on NHS data versus the duty to maintain confidentiality, lawful processing and public trust.
How Is AI Regulated? The MHRA AI Airlock
Strong candidates can also speak to regulation. Most clinical AI is classed as "AI as a Medical Device" (AIaMD) and is regulated by the MHRA. In 2024 the MHRA launched the AI Airlock, the world's first regulatory sandbox for AIaMD: a controlled environment where developers and regulators jointly test novel or higher-risk AI safely. Its pilot closed in 2025, a second phase followed, and the findings are shaping a dedicated AI regulatory framework expected in 2026.
Mentioning the MHRA, regulation and the human-in-the-loop principle shows the interviewer you understand that AI is not deployed unchecked. Two ideas are worth knowing: accountability (a named clinician remains responsible for the decision) and post-market surveillance (an AI model can "drift" as real-world data changes, so it must be monitored after approval, not just before).
Many of the ethical concerns around artificial intelligence in the NHS and healthcare relate to the above disadvantages.
Data Privacy - if large AI companies are obtaining patients’ healthcare data, there are ethical concerns that data could be misused, breached or sold without patient consent.
Bias - As mentioned above, due to the data AI uses, it could lead to discriminatory results from the AI. These may lead to perpetuation or exacerbation of existing healthcare biases and discrepancies.
Accountability - If an AI misdiagnoses a patient, who is held accountable, and how can lessons be learned?
Complexity - AI algorithms are highly complex, and so understanding the decision-making made by an AI is incredibly difficult. As their ethical duty, doctors and healthcare professionals must be able to understand and challenge decisions made by an AI.
Clinical Oversight- Should AI override a doctor’s clinical judgement of a situation?
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How to Prepare for AI in Medicine Interview Questions
The best way to prepare for any medical school interview question is to practise, after getting a good grasp of the content they could ask you about.
For artificial intelligence, it’s important that you explain to your examiner:
What artificial Intelligence is
How it is used currently, and how could it be used?
The benefits and drawbacks of AI in the healthcare field
The ethical concerns regarding AI use in the NHS
To impress your examiner, consider how you can link the use of AI to the 6 core values of the NHS. Contemplate whether AI in healthcare helps the NHS embody these values, or whether it contradicts them.
AI in Medicine Interview Question and Model Answer
Do you think AI should be allowed to make clinical decisions without supervision from senior medical professionals?
The potential of AI in healthcare is certainly exciting, offering the possibility of faster and more accurate diagnosis and treatment recommendations. However, fully autonomous clinical decision-making by AI systems raises important concerns that need to be carefully considered.
On the one hand, AI has the ability to analyse huge amounts of patient data and detect patterns that humans may miss. Recent advances in deep learning have enabled algorithms to match or even surpass human performance in specialised tasks like reading radiology scans. Allowing AI greater autonomy could improve efficiency and reduce human errors.
However, AI lacks human judgement, empathy and the ability to deal with nuanced or unexpected situations. Take the example of IBM's Watson for Oncology - while it has ingested vast amounts of cancer data and guidelines, doctors found that its treatment recommendations were often not tailored to individual patients' needs or took important quality-of-life factors into account. Its suggestions needed to be heavily supplemented by experienced oncologists.
In my opinion, AI should not fully replace doctors' skills and experience but complement them - forming an effective doctor-AI partnership that enhances healthcare while ensuring patient wellbeing remains the priority. AI is able to learn through data, which can certainly provide valuable insights and recommendations, but the expertise, empathy, and ethical considerations that human medical professionals bring to the table are irreplaceable.
With continued research and transparency from technology companies, AI in medicine can greatly improve care, but the human touch must not be lost. Humans understand patient anxieties and preferences in a more holistic manner.
What do you understand by Artificial Intelligence?
Do you know of any ways of how Artificial Intelligence can help improve patient care?
To what extent do you think it is true that AI will take over the roles of doctors?
Is it ethical to use AI to help with coursework during your medical degree?
How does AI impact the process of diagnosing medical conditions, and what are the potential advantages and disadvantages for healthcare professionals and patients?
Can you provide an example of a specific AI application in healthcare that has improved patient outcomes?
Discuss the role of healthcare professionals in ensuring the ethical use of AI in medicine.
(Hard) What do you know about the concept of biases in medicine, does AI worsen this?
Explain the concept of data privacy in AI-driven healthcare. Why is this an issue?
How do you envision the future integration of AI in medical education, and what benefits can it bring to medical students and doctors?
(Hard) Describe a scenario where an AI system's decision conflicts with a healthcare professional's clinical judgment. How would you address this situation ethically?
Given the potential for AI to automate administrative tasks, how do you see this affecting the roles of healthcare workers in the future, and what ethical considerations are involved in this transformation?
AI in medicine is the use of self-learning computer systems to perform tasks that normally need human intelligence, such as reading scans, drafting notes, flagging at-risk patients and supporting diagnosis. In the NHS in 2025-26 it is used to augment clinicians, not replace them: a doctor stays responsible for the final decision (the human-in-the-loop principle).
How is AI currently used in the NHS in 2026?
As of 2025-26 the NHS uses AI for ambient scribing (AI tools that draft consultation notes), AI imaging in acute stroke (e.g. Brainomix 360 reading CT scans in minutes), the EDITH breast-screening trial testing AI on mammograms, virtual wards with smart remote monitoring, and algorithms that flag patients at risk of chronic disease. The 2025 10-Year Health Plan makes AI central to NHS reform.
What are the pros and cons of AI in medicine?
Pros: faster scan and data analysis, earlier diagnosis, less administrative burden through AI scribes, support for personalised treatment, and greater capacity. Cons: algorithmic bias from skewed training data, data-privacy and consent risks, the "black box" problem where decisions are hard to explain, automation bias (over-trusting the AI), and unresolved questions of accountability when an AI gets something wrong.
Is AI going to replace doctors?
No. The consensus, and the safest interview line, is that AI augments doctors rather than replacing them. AI is strong at pattern recognition and repetitive tasks, but lacks clinical judgement, empathy, and the ability to handle nuanced or unexpected situations. The human-in-the-loop principle keeps a named clinician accountable for decisions. AI is best seen as a tool that frees doctors to spend more time on patient care.
What is the NHS 10-Year Health Plan and how does it involve AI?
The 10-Year Health Plan for England, published in July 2025, sets out three shifts: hospital to community, sickness to prevention, and analogue to digital. AI is a core enabler of the digital shift, with the plan committing to scale AI scribes to cut bureaucracy and to expand AI-supported diagnosis. Citing it shows interviewers your knowledge is current.
What are AI scribes or ambient voice technology?
AI scribes, also called ambient voice technology, listen to a consultation and automatically draft the clinical notes, letters and codes for the clinician to review and approve. By 2025-26 they are used across many NHS trusts and most GP practices in England, governed by NHS England guidance. The aim is to reduce documentation time and clinician burnout, freeing up time to care.
What is the EDITH AI breast-screening trial?
EDITH (Early Detection using Information Technology in Health) is an NHS trial launched on World Cancer Day, 4 February 2025, backed by around £11 million via the NIHR. It invites nearly 700,000 women to test five AI platforms across 30 sites, assessing whether AI can safely replace one of the two radiologists who read each mammogram, potentially saving over 100,000 clinician hours a year.
What is the NHS Foresight AI model and why was it controversial?
Foresight is a generative AI foundation model trained on de-identified records from around 57 million people in England, developed with UCL and King's College London to predict population health needs. NHS England paused it in 2025 after GP leaders and privacy experts questioned whether patients were adequately consulted and whether anonymised data could be re-identified, prompting an Information Commissioner's Office review. It is a strong example of the data-privacy debate.
How is AI in healthcare regulated in the UK?
Most clinical AI is classed as AI as a Medical Device (AIaMD) and regulated by the MHRA. In 2024 the MHRA launched the AI Airlock, the world's first regulatory sandbox for AIaMD, where developers and regulators safely test novel or higher-risk tools. Its pilot closed in 2025 and findings are shaping a dedicated AI framework expected in 2026. Key principles are accountability and post-market surveillance.
What are the ethical issues with AI in medicine?
The main ethical concerns are algorithmic bias (AI trained on skewed data can worsen health inequalities), data privacy and consent, accountability (who is liable if AI gets it wrong), the "black box" problem of unexplainable decisions, automation bias (clinicians over-trusting AI), and whether AI should ever override clinical judgement. The principle that a named clinician stays responsible underpins all of these.
Who is accountable if an AI makes a mistake in healthcare?
In current NHS practice a named clinician remains accountable for the decision, even when AI supports it, which is the heart of the human-in-the-loop principle. AI tools are decision-support, not decision-makers. This is one reason the MHRA regulates clinical AI as a medical device and emphasises post-market surveillance, so liability and patient safety are not left to the algorithm alone.
What is algorithmic bias and why does it matter in medicine?
Algorithmic bias is when an AI system produces unfair results because its training data under-represents or skews certain groups. In medicine this can worsen existing inequalities, for example a tool trained mainly on one population may perform worse for others. It matters because biased AI can lead to misdiagnosis or unequal treatment, so diverse data, testing and ongoing monitoring are essential safeguards.
How should I answer an AI in medicine interview question?
Structure your answer: define AI briefly, give a current NHS example (AI scribes, the EDITH trial or stroke AI), then weigh benefits against risks and ethics (bias, privacy, accountability, the black box). Conclude that AI augments rather than replaces doctors, with a human kept in the loop, and link it to the NHS core values. Showing current, balanced knowledge impresses examiners most.
Is it ethical to use AI like ChatGPT during a medical degree?
Using AI as a study aid can be acceptable, but submitting AI-generated work as your own is academic misconduct and undermines the skills you must build. The deeper concern is patient safety: large language models can produce confident but wrong information ("hallucinations"), so they must never replace verified clinical sources. A good answer shows integrity, awareness of limitations, and respect for professional standards.
What are good AI in medicine examples to cite at a medical school interview?
Strong 2025-26 examples include AI scribes (ambient voice technology) rolled out across GP practices and trusts, the EDITH AI breast-screening trial, AI stroke imaging such as Brainomix 360, the NHS 10-Year Health Plan's analogue-to-digital shift, the paused Foresight foundation model (for data ethics), and the MHRA AI Airlock (for regulation). Concrete, current examples show genuine wider reading.
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