Top 10 AI Prompts and Use Cases and in the Healthcare Industry in United Kingdom

AI is already moving from pilot projects into everyday NHS practice in Britain - from smarter triage and an envisioned “AI Navigation Assistant” to models that predict frequent A&E users and free up clinicians' time - and the upside is huge: fewer wrong queues, faster diagnoses and more personalised care.

Government and policy analysis show AI can pre-fill records, filter requests, prioritise patients and even power ambient scribing so clinicians spend more face‑time with people, but safe rollout depends on robust validation, model transparency and hard limits on bias (for example, skin‑cancer tools trained mainly on white skin can under‑perform for others).

NHS guidance stresses workforce confidence and post‑market surveillance, while expert reports recommend regional pilots and better DoS data to make navigation reliable; practical skills in prompt writing and tool selection will be essential - see the NHS England guidance on artificial intelligence and machine learning, the Institute for Global Policy report on an AI Navigation Assistant, and consider targeted training like the Nucamp AI Essentials for Work bootcamp to build those capabilities.

NHS England guidance on artificial intelligence and machine learning, Institute for Global Policy report: Preparing the NHS for the AI era - AI Navigation Assistant, Nucamp AI Essentials for Work bootcamp registration.

Bootcamp	AI Essentials for Work
Description	Gain practical AI skills for any workplace; learn tools, prompts and applications without a technical background.
Length	15 Weeks
Courses included	AI at Work: Foundations; Writing AI Prompts; Job Based Practical AI Skills
Cost	$3,582 (early bird) / $3,942 afterwards; 18 monthly payments
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“The NHS is going into winter busier than ever before and as ever, despite huge pressure and a potential ‘quad‑demic', our incredible staff are doing everything within their power to provide the best possible care to patients.” - Amanda Pritchard, NHS Chief Executive

Selection favoured real-world impact and evaluability: use cases were prioritised where NHS England's Phase‑4 lessons - safety, accuracy, effectiveness, value, fit with sites, implementation, scalability and sustainability - could be assessed in routine care, and where mixed‑methods evidence and patient involvement were feasible (NHS England Phase‑4 lessons from the AI in Health and Care Award: planning and implementing real-world AI evaluations).

Cases were chosen to reflect national strategy priorities such as workforce relief, better pathways and reduced inequality highlighted by recent policy briefings (Health Foundation briefing: priorities for an AI in health care strategy), and to show clear pathways for procurement, governance and data‑sharing.

Prompts were written to be pragmatic - testable against measurable outcomes, usable by clinicians and managers, and sensitive to patient characteristics - and the selection process insisted on co‑production with clinical leads and site partners so deployments wouldn't be judged before they'd had time to bed in (many Phase‑4 projects needed around two years to show real effects, a reminder that evaluation needs room to let a sapling take root).

Practical examples also targeted operational wins - such as resource optimisation for bed management - to prove efficiency gains that free up clinical time (Resource optimisation case study: how AI is helping UK healthcare cut costs and improve efficiency).

The NIHR-backed smart stethoscope work out of Imperial College shows how a familiar GP tool can be upgraded into an early‑warning system for heart failure: in a multicentre study using an ECG‑enabled stethoscope and AI, researchers in seven NHS sites in London tested recordings at four chest positions and - in about two minutes (and in later TRICORDER work, in some cases a 15‑second exam using a playing‑card‑sized device) - identified heart failure with around 91–92% sensitivity and ~80% specificity, meaning GPs could pick up cases much earlier and prioritise secondary‑care referrals rather than waiting for emergency admissions; see the NIHR evidence summary of AI-enabled stethoscope heart failure study and Imperial College TRICORDER primary care rollout and implementation plans.

The promise is practical: cheaper, quick exams that non‑experts can run, potentially reducing late, costly admissions and speeding access to lifesaving treatment - though researchers caution the tool should be used for symptomatic patients to avoid unnecessary testing.

Metric	Details
Initial study	>1,000 patients across 7 NHS hospitals/community centres (London)
Sensitivity	≈91–92%
Specificity	≈80%
TRICORDER rollout	AI stethoscopes deployed to primary care (100+ GP practices; later studies tested ~12,725 patients)

“Heart failure admission alone costs the UK over £2 billion annually, and an unacceptable 80% of these diagnoses are made during emergency admissions.” - Professor Nicholas Peters

At Addenbrooke's Hospital in Cambridge the Microsoft Research Project InnerEye has been taken from lab to ward: teams used the open‑source InnerEye toolkit and a local cloud deployment (OSAIRIS) so automated segmentations appear ready for review as clinicians open CT scans, turning hours of manual contouring into minutes and cutting the wait between referral and radiotherapy; peer‑reviewed evaluations and Microsoft reports highlight human‑level accuracy with major time savings (one study notes planning can be up to 13× faster) and real‑world gains such as roughly two‑and‑a‑half‑times faster overall task completion and about two in three contours usable without edits.

This UK collaboration shows how clinician co‑design, secure cloud routing and transparent open‑source tooling can bridge the “final mile” to clinical impact - see the Microsoft Research Project InnerEye research page and the InnerEye open‑source deep learning toolkit blog for technical details and NHS outcomes: Microsoft Research Project InnerEye research page and InnerEye open‑source deep learning toolkit blog.

Metric	Reported value
Radiotherapy planning speed	Up to 13× faster (peer‑reviewed)
Usable without edits	About 2 in 3 cases
Overall contouring time	Approximately 2.5× faster
First NHS deployer	Addenbrooke's Hospital (Cambridge)

“OSAIRIS is the first cloud-based AI technology to be developed and deployed within the NHS, which we will be able to share across the NHS for patient benefit.” - Dr Raj Jena, oncologist at Cambridge University Hospitals NHS Foundation Trust

HeartFlow's AI-driven FFRCT turns a standard coronary CT (CCTA) into a patient-specific 3D model that helps clinicians in England decide who truly needs invasive angiography and who can be managed non‑invasively - a national, real‑world win demonstrated in the NHS FISH&CHIPS analysis of 90,553 patients across 27 sites.

Two‑year data published via HeartFlow show a CCTA+FFRCT pathway cut invasive coronary angiography (ICA) needs, trimmed inappropriate ICAs by 16% and reduced follow‑up non‑invasive tests by about 12%, while identifying one extra appropriate PCI for every two negative catheterisations avoided; the platform also aims to deliver guideline‑directed pathways roughly 90 minutes after a CCTA. For NHS trusts looking to boost cath‑lab efficiency and clearer triage for suspected coronary artery disease, the FISH&CHIPS results and HeartFlow's FFRCT resources explain how a CCTA‑first strategy can translate into faster, more precise care and fewer unnecessary procedures - see the FISH&CHIPS summary and HeartFlow's FFRCT analysis for full details.

Metric	Value
Patients (FISH&CHIPS)	90,553
NHS sites	27 hospital sites (England)
Reduction in ICA	7% (CCTA+FFRCT vs CCTA)
Reduction in inappropriate ICA	16%
Reduction in secondary non-invasive tests	12%
Time to guideline pathway	≈90 minutes from CCTA to pathway

“New findings from the FISH&CHIPS study demonstrate that a CCTA+FFRCT pathway at a national level can positively impact individual patient care, improving the suspected coronary artery disease patient's journey.” - Dr. Timothy Fairbairn, Liverpool Heart and Chest Hospital NHS Foundation Trust

The Moorfields Eye Hospital partnership with DeepMind and Google Health has produced a striking UK‑focused example of predictive AI in practice: trained on a de‑identified Moorfields dataset of 2,795 patients from seven London sites, the model combines anatomical segmentation and raw 3D OCT volumes (each scan contains some 58 million voxels) to estimate the risk an eye will convert to sight‑threatening “wet” AMD within six months, matching - or in places outperforming - experienced retinal clinicians and offering clinicians a visual, explainable view of how tissues change over time; read the Nature Medicine paper for the peer‑reviewed results, Moorfields' summary of the work, and DeepMind's explainer on the system for technical detail and caveats (Nature Medicine PubMed paper on AI predicting AMD progression, Moorfields Eye Hospital news on AI for AMD, DeepMind explainer on using AI to predict retinal disease progression); the practical “so what?” is clear for the NHS: an early‑warning loop that can prioritise high‑risk fellow eyes, help design trials of preventative therapies and target scarce clinic slots - while remaining early research that needs prospective trials, demographic validation and careful balancing of false positives (for example, at 90% specificity the model's sensitivity is about 34%).

Metric	Value
Patients (Moorfields dataset)	2,795
Clinical sites (London)	7
Prediction window	6 months
Voxels per 3D OCT scan	≈58 million
Example operating point	90% specificity → 34% sensitivity

“Patients who have lost vision from wet AMD are often particularly worried that their “good eye” will become affected and, as a result, that they will become blind. We hope that this AI system can be used as an early warning system for this condition and thus help preserve sight.” - Pearse Keane, consultant ophthalmologist at Moorfields Eye Hospital

Tumour drug‑response prediction is moving from lab proof‑of‑concept to a clinically promising tool that could help NHS teams personalise oncology faster and cheaper: Stanford's SEQUOIA model extracts gene‑activity signals from routine H&E biopsy images - learning from 7,584 samples to predict the expression of more than 15,000 genes and even reproduce commercial breast‑cancer risk scores that normally require costly RNA tests - turning a stained slide into a visual map of molecular programs that can forecast outcomes and therapeutic sensitivity (Stanford Medicine article: SEQUOIA predicts gene signatures from biopsy images).

Parallel work shows AI can flag tumors likely to resist chemotherapy and reveal the molecular assemblies behind resistance, while reviews highlight how large‑scale clinical and genomic integration makes phenotype‑level drug‑response prediction increasingly feasible (ASBMB analysis: AI harnesses tumor genetics to predict treatment resistance, Molecular Cancer review: AI-driven precision cancer therapies).

The practical payoff for the UK is clear - a path to faster, cheaper stratification of who needs targeted sequencing or alternative regimens - but these tools still require prospective trials, regulatory clearance and rigorous validation on diverse populations before routine NHS adoption.

“This kind of software could be used to quickly identify gene signatures in patients' tumors, speeding up clinical decision‑making and saving the health care system thousands of dollars.” - Olivier Gevaert

A collaboration between UCL researchers and UCLH has turned live A&E feeds into a practical capacity‑planning tool that predicts how many arrivals will need a ward bed in four and eight hours, producing a probability distribution rather than a single daily figure and sending forecasts to planners four times a day; the system combines 12 time‑staged machine‑learning models (trained on UCLH data from May 2019–July 2021) that estimate each patient's admission probability from factors like age, arrival method and test results, then aggregates those to give a richer, near‑real‑time view of bed demand that reduced average error (central predictions were ~4 admissions off actual versus 6.5 for the conventional benchmark).

Designed to be adaptable - models were updated for pandemic shifts - the approach is a concrete example of operational AI that can help reduce cancelled surgeries and ease corridor care in a health system with known bed shortages (see the UCL tool, NHS England bed statistics, and an HDR UK forecasting project for related work).

Metric	Value
Prediction horizons	4 hours and 8 hours
Models	12 time‑staged ML models (0–12 hours)
Training data	May 2019 – July 2021 (UCLH)
Benchmark error	Central predictions ≈4 admissions off actual vs 6.5 for conventional method
Forecast frequency	Four times daily (email to planners)

“Our AI models provide a much richer picture about the likely demand on beds throughout the course of the day. They make use of patient data the instant this data is recorded. We hope this can help planners to manage patient flow – a complex task that involves balancing planned-for patients with emergency admissions. This is important in reducing the number of cancelled surgeries and in ensuring high-quality care.” - Dr Zella King

Yorkshire Ambulance Service has been at the forefront of turning prehospital judgement into data‑driven decisions: research programmes such as SINEPOST developed at the University of Sheffield aim to equip paramedics with a validated risk‑prediction tool to decide on‑scene whether a patient truly needs ED conveyance, while wider English trauma‑network audits probe how accurately major‑trauma triage operates in practice - work that matters because a Yorkshire study flagged up to 16.9% of ambulance transports as potentially avoidable, a staggering “one in six” trips that, if safely reduced, could free ambulance and A&E capacity across the UK. These models don't replace clinicians but summarise risk at the roadside, helping crews choose hospital, community care or watchful reassurance and thereby easing downstream bed pressure; read the SINEPOST validation and the English trauma‑network accuracy study for the methodological detail and clinical context (SINEPOST risk‑prediction tool validation (PLOS One), PubMed article on prehospital triage accuracy in English trauma networks), and note Yorkshire Ambulance's ongoing research commitments to translate those models into safer on‑scene choices (Yorkshire Ambulance Service clinical research media release).

Metric	Value / source
Potentially avoidable ambulance conveyances (Yorkshire)	Up to 16.9% (funded study summary)
SINEPOST funding	£178,816.49 (NIHR/HEE award)
SINEPOST publication	PLOS One, 2022 (development & validation)
English trauma triage accuracy study	Scand J Trauma Resusc Emerg Med, May 2024 (PubMed)

“The research team is very proud of its achievements in the last year. We have responded to the COVID-19 pandemic by working collaboratively with our local NHS partners in finding treatments and vaccines.” - Fiona Bell, Head of Research, Yorkshire Ambulance Service

UK pathology teams and NHS trusts can gain real traction from biopsy‑level AI for ulcerative colitis because it tackles the two biggest headaches in routine histology: subjectivity and scale.

Machine‑learning models trained on whole‑slide H&E images can now label each cell, segment tissue compartments and produce spatially‑contextualised, single‑cell maps that quantify neutrophils, plasma cells and other features linked to disease activity - effectively turning a stained slide into an objective visual map that flags active inflammation, predicts concurrent endoscopic activity and forecasts clinical outcomes, which helps standardise trial endpoints and routine reporting (CAP article: Revolutionizing IBD histology assessment through AI).

Early work even shows AI can predict molecular signals such as p53 mutations from H&E, offering a cheaper route to risk stratification, but these tools need regulatory clearance and prospective validation before nationwide NHS rollout; for clinicians and managers wanting practical next steps, see the Nucamp guide to using AI in UK healthcare for governance, validation and workforce upskilling (Nucamp AI Essentials for Work syllabus - Complete guide to using AI in UK healthcare).

An international effort during the pandemic produced practical risk tools that matter for UK surgical services: the Lancet cohort and follow‑on CovidSurg work showed patients who have SARS‑CoV‑2 around the time of an operation face dramatically higher postoperative risk - the multi‑centre Lancet snapshot (1,128 patients from 235 hospitals) reported a 30‑day mortality near 24% and about half of patients developing pulmonary complications - and that prompted a machine‑learning CovidSurg mortality score built on a far larger dataset (8,492 patients across 756 hospitals) to help teams decide whether to delay or proceed.

The model uses readily available inputs (age, ASA score, the Revised Cardiac Risk Index and preoperative respiratory support) so UK surgical teams can have a rapid, evidence‑based conversation about risk, plan enhanced postoperative care, or prioritise “cold” pathways; see the NIHR summary of the CovidSurg risk tool and Birmingham's coverage of the Lancet findings for practical detail and UK context.

For planners the vivid take‑away is stark: operations that would normally carry under 1% mortality were shown, in infected patients, to jump into double digits - a reminder that careful stratification and perioperative testing remain essential.

Metric	Value / source
Lancet cohort	1,128 patients; 235 hospitals; 24 countries
Overall 30‑day mortality (Lancet)	≈23.8%
Pulmonary complications	≈50% of perioperative SARS‑CoV‑2 cases
CovidSurg model development	8,492 patients; 756 hospitals; 69 countries
Key predictors (CovidSurg)	Age; ASA score; Revised Cardiac Risk Index; preop respiratory support

“We would normally expect mortality for patients having minor or elective surgery to be under 1%, but our study suggests that in SARS‑CoV‑2 patients these mortality rates are much higher in both minor surgery (16.3%) and elective surgery (18.9%).” - Aneel Bhangu; “There's now an urgent need for investment … to ensure that as surgery restarts patient safety is prioritised.” - Dmitri Nepogodiev

Salesforce Health Cloud (Patient 360) promises a tidy “single source of truth” for UK trusts by pulling EHRs, wearables, claims and social‑care data into one timeline so clinicians and care managers can see medications, lab results and remote‑monitoring alerts at a glance - a flight‑deck view that helps prioritise follow‑ups and automate routine outreach.

Built‑in AI and workflow tools (including Agentforce for autonomous patient and staff assistants) can power risk stratification, appointment nudges and 24/7 patient engagement while FHIR/MuleSoft connectors ease EHR integration and help meet UK data‑governance needs like GDPR; practical implementation guides walk through security, consent and phased rollouts for NHS settings.

For operational teams wrestling with siloed systems, the real benefit is measurable: unified records that cut administrative friction and free clinicians to focus on care rather than paperwork - see a practical Patient 360 overview and an implementation guide for technical and regulatory detail.

Edition / Feature	Notable capability	Price (reported)
Enterprise	Agentforce (autonomous AI agents), clinical & insurance data models	$325 / user / month
Unlimited	More storage, predictive & generative AI	$500 / user / month
Einstein 1 (Service or Sales)	Einstein AI, Data Cloud, Copilot features	$700 / user / month

“The future of care is proactive, personalized, and connected. Health Cloud is the platform to build that future.” - Amit Khanna, SVP at Salesforce Health

Move from promise to practice by treating AI as a regulated service, not a shiny gadget: follow NHS information‑governance rules (complete a legally required DPIA, minimise training data, document controllers/processors) and use the UK Government's AI Playbook principles to lock in human oversight, security and transparency - the Playbook even recommends posting

10 principles

where teams can see them as a daily reminder of what safe use looks like.

Clinicians and managers should insist on clear evaluation plans, explainable outputs for patients, and robust procurement checks (statistical accuracy claims tested, MHRA and ICO guidance followed) while building multidisciplinary teams that include IG, legal and user‑research experts.

Beginners and operational staff can get practical, job‑focused skills to write better prompts, assess tools and run safe pilots by upskilling on structured courses - see the NHS information governance guidance on AI, the UK Government AI Playbook, and consider practical training like the Nucamp AI Essentials for Work bootcamp (AI skills for any workplace, 15 weeks) to translate governance into usable, confidence‑building skills for everyday NHS work.

Bootcamp	Key facts
Nucamp AI Essentials for Work bootcamp - AI at Work training (15 weeks)	15 weeks; courses: AI at Work: Foundations, Writing AI Prompts, Job‑Based Practical AI Skills; early bird $3,582; registration link available