Doctors face unique challenges when implementing AI technology, as they lack the extensive compliance support available to larger healthcare systems. While AI promises significant benefits for efficiency and patient care, practitioners bear personal liability for AI-related decisions without the protective layers of institutional support.
Personal professional liability
Doctors remain fully accountable for all clinical decisions influenced by AI recommendations (1). Research reveals most UK doctors rate themselves as only slightly or moderately knowledgeable about AI, yet there’s an expectation to integrate AI into their practice. This knowledge gap creates risk exposure, particularly when 37% of doctors using AI reported use to write required reflective pieces for their portfolio, suggesting usage that may not align with professional standards.
The GMC’s research found doctors understand their responsibility for AI-assisted decisions in the current context, with many overriding AI recommendations (2). However, some practitioners acknowledge that, as AI evolves, responsibility may become less clear cut, with several doctors anticipating that accountability for AI decisions will ultimately be determined by the courts.
Regulatory gaps and professional guidance deficits
The BMA has issued warnings about substantial risks associated with AI use while regulations remain in a state of flux (3), creating challenges for small practices which must navigate compliance without dedicated support. The BMA emphasises that practices, as data controllers, need to understand the risks they’re taking on when using AI technologies.
Current frameworks place the burden of compliance squarely on individual practitioners. Under UK GDPR and the Data Protection Act 2018, small practices must ensure clarity around the use of patient data, complete Data Protection Impact Assessments before high-risk patient data processing, and maintain proper indemnity coverage; challenging tasks for small or solo doctor practices.
Practice vulnerabilities
Research on Healthcare AI governance reveals existing frameworks frequently target large academic medical centres, creating barriers for smaller healthcare organisations (4).
Individual practitioners face disproportionate barriers to compliance, including high administrative burdens and limited access to expertise (5). SME’s experience what researchers term the “tragic character” of contemporary compliance (6), where regulatory demands impose uncertain and costly obligations that disproportionately affect smaller operators.
Practical challenges are substantial. A leading medical defence organisation, reports that members frequently seek advice about whether they can be held liable when using AI (7). Key concerns include compliance with data protection legislation, patient consent, and whether AI usage is covered by professional indemnity insurance.
Liability and risk management
Academic analysis of AI liability suggests current frameworks may unfairly burden individual clinicians. Research argues that clinicians will likely shoulder liability via negligence claims for allowing defects in AI outputs to reach patients (8). The authors question whether this distribution is fair, just and reasonable proposing instead a risk-pooling approach to create shared responsibility between clinicians and tech companies.
This concern is acute for small practices, where individual doctors lack the institutional backing to challenge AI system failures or negotiate liability terms with technology vendors, solo practitioners bearing concentrated liability exposure for AI-related decisions.
Practical implementation challenges
AI compliance involves navigating multiple complex requirements simultaneously. They must ensure that AI tools are registered with the MHRA as Class I medical devices where applicable, maintain NHS standards compliance, and conduct thorough clinical safety assessments (3). Additionally, practitioners must verify that medical defence coverage extends to AI usage, for example only 42% of practitioners were aware of their medical defence union’s position on AI scribe usage.
The practical burden extends beyond initial implementation. Practices must establish ongoing monitoring systems, maintain documentation for regulatory audits, and stay current with rapidly evolving compliance requirements. Practitioners must personally manage these responsibilities alongside their clinical duties.
Future implications for practice sustainability
The compliance burden associated with AI adoption could limit adoption in private practice. Furthermore, the American Medical Association estimates that physician numbers in private practice have dropped 13% in the past decade, largely due to financial pressures and administrative burdens (9). Similar trends in the UK could see small practices struggle to implement compliance infrastructures necessary for safe AI implementation.
However, properly implemented AI systems could provide significant benefits for small practices. Research suggests that AI tools can reduce documentation time by up to 50% and achieve transcription accuracy of 95% (10). The challenge lies in ensuring that practices can access these benefits while maintaining appropriate safeguards.
It’s important to realise a one-size-fits-all regulatory approach may not serve the whole healthcare ecosystem effectively. Professional bodies, regulatory authorities, and technology developers must collaborate to create compliance frameworks that protect patient safety while enabling practices to harness AI without bearing disproportionate liability risks.
