AI in Healthcare: The “Standard of Care” in the Age of Algorithmic Diagnostics

Introduction
The integration of artificial intelligence (AI) into UK healthcare is no longer speculative—it is already transforming practice. From radiology to pathology, AI-driven diagnostic tools are being deployed at scale, promising faster and more accurate decision-making. Yet as algorithms increasingly influence clinical judgments, critical legal and ethical questions emerge:
How does the law define the “standard of care” when a machine contributes to—or even dictates—a diagnosis?

Who bears liability when an AI system errs?
Can existing negligence frameworks withstand the challenges posed by opaque, adaptive algorithms?
This thought piece explores the implications of AI for healthcare regulation and governance in the UK. Drawing on legislation, case law, NHS policy documents, and academic research, it argues that the UK’s current regulatory frameworks are insufficient for managing AI’s unique risks—particularly around liability, transparency, and patient consent. Without urgent reform, the NHS risks a crisis of accountability where neither clinicians nor developers can be held responsible for algorithmic misdiagnoses.

AI and the Evolving “Standard of Care”
Medical negligence law in the UK hinges on the “standard of care”—the level of skill and diligence expected of a reasonable practitioner. The Bolam test (Bolam v Friern Hospital Management Committee) and its refinement in Bolitho set out that a clinician’s actions are defensible if they align with a responsible body of medical opinion. These precedents, however, were never designed for the complexities introduced by AI.

1. The Black-Box Problem
Deep learning systems often operate as “black boxes.” Their decision-making processes are not easily understood, even by their developers (Leslie, 2019). This creates a problem for courts: if a clinician relies on an AI tool and the diagnosis proves incorrect, how can the decision be judged reasonable under Bolam or Bolitho if the AI itself cannot explain its rationale?

2. Dynamic Algorithms vs. Static Regulation

Many AI systems adapt post-deployment, learning from new data and drifting from their original programming (McKee & Wouters, 2023). The Medicines and Medical Devices Act 2021 offers some oversight, but does not address this adaptivity. If an AI system learns new patterns and “drifts” into error, accountability becomes diffuse: should responsibility fall to the developer, clinician, or trust?

3. A Liability Vacuum
The Consumer Protection Act 1987 imposes strict liability for defective products, but defining a “defect” in the context of algorithmic reasoning is complex. Is the defect in the software, the training data, or the decision to use it? Without clarification, AI systems risk creating a liability vacuum—where both clinicians and developers deny responsibility (Froomkin et al., 2019).

Informed Consent in the Age of AI
The Montgomery ruling (Montgomery v Lanarkshire Health Board, 2015) reshaped the law on informed consent. Patients must be informed of material risks and reasonable alternatives from their own perspective. But what counts as “material” when an AI system is involved?
Should patients be told their diagnosis was AI-assisted?
Must clinicians disclose algorithmic limitations, training biases, or comparative error rates?
If AI recommends a treatment outside NICE guidelines, does the clinician have a duty to override it?
Currently, NHS guidance is vague. The NHSX report Artificial Intelligence: How to Get It Right (2019) flags ethical concerns but offers little legal clarity. The GDPR includes a “right to explanation” for automated decisions, yet many AI systems remain technically opaque (Zhang & Zhang, 2023).

The UK’s Regulatory Patchwork
The UK’s AI Regulation White Paper (CP 1019, 2024) proposes five high-level principles—safety, transparency, fairness, accountability, and contestability—but stops short of enforceable legislation. In contrast, the EU’s AI Act and the US FDA’s AI precertification programme impose more rigorous standards. Without concrete regulatory reform, the UK risks falling behind.

Key Gaps:
No AI-specific case law on clinical negligence
Ambiguity in applying strict product liability to dynamic algorithms
Unclear standards for transparency and explainability in medical AI
Inconsistent patient information protocols in AI-assisted care
Recommendations for Policy and System Leaders

To build trust and ensure safe innovation, the following reforms are urgently needed:

Clarify the Standard of Care for AI-Supported Diagnosis
The Bolam test should be updated to reflect whether reliance on AI is reasonable and when clinicians must independently verify AI outputs.

Reform Product Liability Frameworks
Update the Consumer Protection Act 1987 to account for adaptive software and introduce a shared liability model across clinicians, developers, and trusts.

Mandate Explainability in High-Stakes AI
Regulators should require explainable AI systems for use in life-altering diagnostics, even if this compromises some model performance.

Standardise AI Disclosure in Informed Consent
NHS protocols must explicitly require clinicians to disclose when AI has influenced diagnosis or treatment, along with known risks or limitations.

Include EDI Risk Mitigation in AI Oversight
Bias in training data can exacerbate health inequalities. Regulatory guidance should mandate demographic audits and impact assessments of AI tools used across the NHS.

Conclusion
The integration of AI into UK healthcare is inevitable, but without a robust legal and ethical framework, risks to patients, professionals, and public trust will grow. The UK must proactively shape how AI fits into clinical responsibility, patient rights, and system safety. That requires moving beyond voluntary principles to enforceable reforms that define the standard of care for a new era.

References
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Copyright (c) King Advisory, 2025.