From “assistant” to “autonomous”: legal liability and ethical traceability frameworks for generative AI in clinical misdiagnosis scenarios, with a special focus on paediatrics

Category	Detail
Condition	Generative AI in Clinical Diagnosis
Key Mechanisms	Integration of Large Language Models (LLMs) into clinical workflows, addressing liability complexities and accountability frameworks.
Target Population	Pediatric patients, including neonates to adolescents.
Care Setting	Clinical decision support and regulatory frameworks.

Generative AI blurs the line between decision support and autonomous diagnostic reasoning.
Existing regulatory frameworks inadequately address the non-deterministic outputs of LLMs.
The proposed Three-Tiered Liability Escalation Framework (T-LEF) allocates accountability based on system autonomy.
Clinical Algorithmic Audit Trails (CAAT) are proposed to enhance traceability and accountability.
Pediatric applications of generative AI present unique risks due to under-representation in training data.

Utilize LLMs with caution, ensuring independent verification of clinical recommendations.

Implement the T-LEF to allocate liability among AI developers, healthcare institutions, and clinicians.

Be aware of automation bias and the potential for generative AI to produce clinically deceptive outputs.

Pediatric patients, particularly vulnerable due to developmental factors.

Weight- and development-based dosing must be carefully managed in AI recommendations.

Ensure transparency in AI system outputs and maintain clinician oversight.
Adopt structured Safe Harbor provisions to encourage compliant innovation.
Conduct regular audits of AI systems to assess their impact on clinical decision-making.

Clinical Scorecard: Evolving Roles of Generative AI in Clinical Diagnosis: Legal Accountability and Ethical Frameworks, Focusing on Pediatric Applications