Legal and health leaders in the UK have reopened a key question: can artificial intelligence help identify breach of duty and causation in medical negligence claims more clearly, more quickly, and more fairly? The issue touches every part of the claims journey, from early triage to expert evidence and settlement. Advocates point to tools that can read vast clinical records, map timelines, and compare care against guidelines. Critics warn about bias, shaky data, and black-box reasoning that courts and patients cannot test. The debate matters because breach and causation sit at the core of clinical negligence law, and they often decide outcomes. If AI can sharpen those tests without undermining fairness, it could reduce costs and delays, and improve access to justice for both patients and clinicians.
Context and Timing
On 27 October 2025, a Legal Futures blog raised the question of whether AI can help identify breach and causation in medical negligence claims. That discussion reflects a wider UK debate that includes law firms, insurers, clinicians, patient groups, and technologists. It sits within established legal tests in England and Wales, and within UK data and evidence rules that govern any new tool used in litigation.
How breach of duty works under UK law
Courts in England and Wales assess breach by asking whether a clinician acted in line with a responsible body of medical opinion, often described through the Bolam test, and whether that opinion stands up to logical analysis, as set out in Bolitho. In consent cases, courts apply Montgomery, which sets a patient-focused standard for information disclosure and shared decision-making. These tests rely on expert evidence, clinical guidelines, contemporaneous records, and the specific facts of each case.
Lawyers and experts often review thousands of pages of notes, imaging, lab results, and guidelines in force at the material time. They must pinpoint which decisions matter, when clinicians took them, and what information they had. That process can take months, and delays can compound stress and cost for all parties.
Where AI can support breach analysis
AI tools can extract and structure data from mixed records, including handwritten notes, PDFs, and imaging reports. Natural language processing can flag key events, missed observations, drug interactions, or time gaps in monitoring. Timeline tools can align entries across systems and reveal inconsistencies. Guideline mapping systems can link recorded decisions to clinical protocols, NICE guidance, and local policies in place at the time, which can help experts frame opinions.
Firms already use analytics to triage files and identify claims that need close review. In principle, AI can also surface comparable practice patterns from large, anonymised datasets to show what responsible clinicians did in similar scenarios. That kind of benchmarking could help experts apply the Bolam/Bolitho framework with better evidence and fewer blind spots. Any such tool must show its sources, date-stamp guidelines, and record model versions to support scrutiny.
Causation: the hardest test for machines and humans
Causation turns on a counterfactual: would the harm have occurred “but for” the breach? In some clinical contexts, courts also consider material contribution where science supports it. These tests demand careful, fact-specific analysis and clear reasoning about timelines, physiology, and risk. Experts explain how earlier diagnosis, different monitoring, or another intervention would probably have changed the outcome.
AI can support this by modelling clinical pathways and time-to-treatment windows drawn from published studies. It can visualise how delays or missed observations align with known risk curves. It can highlight earlier red flags and estimate likely outcome differences based on cohort evidence. Those outputs can give structure to expert reasoning, but they do not replace it. Courts need transparent logic, not a score without explanation.
Transparency, bias, and explainability
AI systems learn from historical data that can encode bias. If records underreport symptoms from certain groups, a model may miss risk signals for those patients. If the training set reflects one hospital’s practices, the tool may generalise poorly. Lawyers and courts need to know what data trained the model, what validation the developer ran, and how often the model yielded errors on edge cases.
Explainability matters. A clinician-expert must understand and challenge an AI-supported conclusion. Model cards, audit trails, and clear feature explanations can help. Simple methods often beat opaque ones for litigation tasks because they allow scrutiny. A tool that assigns causation probabilities without showing the drivers will carry little weight in evidence, even if it performs well in the lab.
Evidence rules, experts, and admissibility
The Civil Procedure Rules govern how parties use expert evidence in clinical negligence claims. Experts owe a duty to the court and must explain the basis for their opinions. Parties can use technology to support analysis, but the court will expect reliable methods and transparent reasoning. Novel techniques attract higher scrutiny. Parties should disclose the tool’s methodology, validation, and limitations, and they should preserve an audit trail that shows how the tool processed case data.
Judges assess relevance and reliability, not brand or hype. A tool that speeds up disclosure review may serve as internal support only. A tool that underpins an expert’s opinion must withstand cross-examination. That means the expert must understand the tool well enough to defend its outputs and accept responsibility for the opinion.
Data protection and patient confidentiality
Patient records contain sensitive data. Any AI pipeline must comply with UK GDPR and the Data Protection Act 2018. Parties must identify a lawful basis, follow data minimisation, and apply strong security controls. Pseudonymisation reduces risk but does not remove obligations. Cross-border processing and cloud hosting require careful controls and contractual safeguards. The Information Commissioner’s Office has issued guidance on AI and data protection that practitioners can apply to model training and case-by-case analysis.
Health data often sits across multiple systems. Parties must plan secure extraction, access control, and retention schedules from the outset. Clear data maps and role-based access can reduce risk and support court directions on disclosure.
Efficiency, cost, and access to justice
Clinical negligence cases often involve large volumes of records and complex timelines. AI review and timeline tools can reduce manual effort, which can lower costs and speed up case assessment. Faster triage can help claimant firms screen weak cases earlier and focus on meritorious ones. Defendants can identify key issues faster, shape offers, and avoid late surprises. Better data can support earlier neutral evaluation and narrower expert instructions, which can reduce the need for multiple reports.
NHS Resolution reports have highlighted the financial and human costs of clinical negligence. Streamlined processes that still protect fairness could ease pressure on the system. Any gains must not come at the expense of accuracy or patient trust. Rigorous validation and independent oversight can help balance speed with justice.
Practical steps for responsible adoption
Firms that plan to use AI should set clear use cases: record sorting, timeline building, guideline mapping, or literature retrieval. They should run pilot projects with defined success metrics, compare performance against skilled human review, and document error rates. They should train staff to spot AI failure modes, and they should keep human experts in the loop for any judgement calls on breach and causation.
Procurement teams should demand transparency from vendors: training data sources, validation studies, bias testing, security certifications, and update policies. In-house governance should record model versions, configuration settings, and prompts for large language models. Parties should agree protocols at the outset of litigation to avoid disputes about tool outputs later in the process.
What fairness looks like for patients and clinicians
Fairness means accurate, timely answers grounded in sound evidence and clear reasoning. Patients deserve clarity on what went wrong and why. Clinicians deserve a fair reading of the record and the standards that applied at the time. AI can support both aims if teams use it to illuminate facts rather than to short-circuit judgement. Tools that capture context, mark uncertainty, and surface alternative explanations can improve the quality of expert debate rather than narrow it.
Courts value reasoning, not just results. An AI-assisted workflow that documents each step—from record ingestion to guideline mapping and counterfactual analysis—can help experts present opinions that the court can test. That approach supports just outcomes and reduces the risk of over-reliance on unverified outputs.
Wrap-Up
The renewed focus on AI and medical negligence reflects a practical need: breach and causation drive outcomes, costs, and confidence in the system. AI can add real value when it speeds up record analysis, aligns facts with guidelines, and frames causation timelines with evidence from studies and registries. Strong governance, clear documentation, and respect for expert judgement must guide every deployment. Data protection rules, transparency, and fairness principles set the guardrails. Courts will reward parties that use technology to clarify issues rather than obscure them. Over the next few years, careful pilots, open validation, and cross-disciplinary oversight can turn promise into practice. If the sector gets these steps right, AI can help deliver faster, clearer, and fairer resolution of
