From “assistant” to “autonomous”: legal liability and ethical traceability frameworks for generative AI in clinical misdiagnosis scenarios, with a special focus on paediatrics - Report - MDSpire
Advertisement
From “assistant” to “autonomous”: legal liability and ethical traceability frameworks for generative AI in clinical misdiagnosis scenarios, with a special focus on paediatrics
Clinical Report: Evolving Roles of Generative AI in Clinical Diagnosis
Background
The use of generative AI in clinical practice is rapidly evolving, moving from simple decision-support tools to systems capable of autonomous reasoning. This shift raises significant legal and ethical concerns, particularly regarding liability for clinical errors. Pediatric patients face unique challenges associated with their care.
Data Highlights
No numerical data provided in the source material.
Key Findings
Generative AI systems produce non-deterministic outputs that complicate existing liability frameworks.
The T-LEF allocates legal accountability based on the autonomy of AI systems from human oversight.
Automation bias can lead clinicians to over-rely on AI recommendations.
Pediatric care faces unique challenges, including weight-based dosing.
Clinical Algorithmic Audit Trails (CAAT) are proposed to enhance traceability in AI outputs.
Clinical Implications
Healthcare professionals must be aware of the potential for automation bias when using generative AI tools in clinical settings.
Conclusion
The integration of generative AI in clinical diagnosis presents both opportunities and challenges.
Google Trends data showed increased US search interest in vitamin A and cod liver oil during the 2025 measles outbreak, but normalized search data cannot determine actual product use, dosing behavior, or harms.
