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Filippo Cademartiri: Can AI Reduce Diagnostic Errors in Low-Resource Settings?
Filippo Cademartiri/LinkedIn
Feb 12, 2026, 11:10

Filippo Cademartiri: Can AI Reduce Diagnostic Errors in Low-Resource Settings?

Filippo Cademartiri, Consultant Advanced Cardiovascular Imaging at UPMC Salvator Mundi International Hospital, shared on LinkedIn about a recent article by Ihsan Ayyub Qazi et al, published in Nature Health:

”Can AI reduce diagnostic errors in low-resource settings?

A randomized trial says: yes — if physicians are trained first.

Diagnostic errors are a major cause of preventable harm, especially in low- and middle-income countries (LMICs).

This rigorous randomized controlled trial in Pakistan tested whether large language model (LLM) access improves physician diagnostic reasoning — but only after structured AI-literacy training .

Study Design

60 licensed physicians

All completed a 20-hour AI-literacy curriculum
Randomized to:

  • LLM (GPT-4o) + conventional resources
  • Conventional resources only

6 structured clinical vignettes
Primary outcome: expert-graded diagnostic reasoning score

The Results Are Striking

LLM group: 71.4%
Control group: 42.6%

+27.5 percentage point improvement (P < 0.001)
No increase in time per case

Even more interesting:

LLM alone scored 82.9%
But in 31% of cases, physicians + LLM outperformed the LLM alone

That’s not replacement. That’s complementarity.

Key Insights

  1. The largest gains were seen in less experienced physicians
  2. Prior LLM familiarity did not guarantee better performance
  3. Structured AI training likely explains why this trial differs from earlier negative studies

Important Caveats

Vignette-based, not real patients
Single country
Only GPT-4o tested
Cannot isolate training vs access effects

Why This Matters

This is one of the first RCTs showing that AI plus trained clinicians significantly improves diagnostic reasoning in a resource-limited setting — without slowing workflow.

The lesson is clear:

  • AI alone is powerful.
  • Untrained AI use is risky.
  • But trained human–AI collaboration may be transformative.

In global health, that’s not hype.

That’s potentially lifesaving.”

Title: Large language model diagnostic assistance for physicians in a lower-middle-income country: a randomized controlled trial

Authors: Ihsan Ayyub Qazi, Ayesha Ali, Asad Ullah Khawaja, Muhammad Junaid Akhtar, Ali Zafar Sheikh, Muhammad Hamad Alizai

Read the Full Article on Nature Health

Filippo Cademartiri: Can AI Reduce Diagnostic Errors in Low-Resource Settings?

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AI Ali Zafar Sheikh Asad Ullah Khawaja Ayesha Ali diagnostic errors Filippo Cademartiri Health Hematology Hemostasis Hemostasis News Hemostasis Today Ihsan Ayyub Qazi LMIC Medicine Muhammad Hamad Alizai Muhammad Junaid Akhtar Pakistan
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