A strategic audit and comparative analysis between frontier LLMs, evaluating logical consistency and clinical reliability in zero-shot medical environments.
ChatGPT
Gemini
Protocol
Measuring "Zero-Shot" accuracy to determine the model's innate safety without specialized instructions or context tuning.
Strategic Intent
Establishing a benchmark for clinical risk mitigation and the effectiveness of autonomous AI guardrails.
Auditing the AI's ability to handle ambiguity, prioritizing models that identify missing data over hallucinations.
Assessing the integration of SBAR protocols and international clinical guidelines within core AI logic.
Verifying proactive warning systems and identification of contraindications in complex workflows.
The reality highlighted by this audit is that these models, despite their immense intelligence, remain "Generalist" and lack Clinical Context unless strictly governed by a rigorous medical engineering protocol.
This critical gap emphasized the necessity to develop a specialized clinical prompt engineering framework to govern medical outputs and ensure patient safety.
INTRODUCING THE FRAMEWORK