MedED AI Apoch #123 AI Needs Clinical Supervision: Never Trust Until You Review the Decision

This morning, I experienced something that shook my trust in AI-based clinical interpretation tools—specifically, a startling inconsistency in how ChatGPT-4.0 responded to identical clinical prompts at two different times of the day. I share this not as a criticism, but as a vital reflection for all of us in healthcare who are actively integrating AI into diagnostics, education, and patient care.

The Encounter

At 8:00 a.m., I uploaded pelvic X-ray images into ChatGPT and prompted it to interpret the findings. The case involved a patient with a clear history of trauma—a road traffic accident—with significant clinical suspicion of pelvic fractures. Visibly, there was a notable gap at the pubic symphysis and radiological changes pointing toward possible hip dislocation and acetabular disruption.

Yet to my utter surprise, ChatGPT did not acknowledge the most glaring abnormalities. The right hip dislocation was missed. The disruption of the pelvic ring was overlooked. Even after repeated prompting, it continued to provide misleading reassurances—emphasizing normal alignment and no acute fracture.

At 12:00 p.m., I re-entered the same conversation thread, uploaded the same X-ray images, and asked the same questions. This time, ChatGPT suddenly identified multiple fractures: the posterior hip dislocation, acetabular fracture, and potential pelvic ring instability.

What changed? The timing? The server load? The “mood” of the algorithm?

These inconsistencies raise significant concerns.

Points to Ponder for the Medical Community:

• Should AI tools used in clinical care have “supervised” or “checked” status, like junior doctors?

• What is the threshold for AI-generated diagnosis to be considered “clinically safe”?

• Should AI interpretation of radiology be “time-stamped” for reliability?

• Can we risk integrating AI in trauma or emergency care without secondary human verification?

• Are we unknowingly anthropomorphizing AI—expecting it to “wake up better” later in the day?

• Is ChatGPT (or similar LLMs) overloaded at times, affecting accuracy and focus?

Reflections from This Experience

1. AI is a Tool, Not a Clinician It must never substitute clinical judgement or radiological expertise.

2. Verification is Vital Even the most advanced models can falter. Double-check. Always.

3. Trust But Validate Use AI for support, not as the final word—especially in high-stakes, life-impacting decisions.

4. Training and Awareness Matter Medical professionals should be trained not just in how to use AI, but how to audit and challenge it.

Key Learning Outcome This incident reminded me that AI is only as powerful as the clinician who uses it wisely. Blind trust can be dangerous. Critical thinking, clinical context, and professional oversight remain irreplaceable.

Let us continue to embrace AIbut with our eyes wide open.

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