Is Artificial Intelligence Better Than Human Clinicians in Predicting Patient Outcomes?

Area of Science:

• Medical Informatics
• Artificial Intelligence in Healthcare
• Clinical Prediction Models

Background:

• Artificial intelligence (AI), particularly deep learning, has revolutionized medical imaging diagnostics.
• Patient outcome prediction remains a significant challenge due to its focus on future, uncertain events.
• The comparative performance of AI models versus human clinicians in outcome prediction is under-explored.

Purpose of the Study:

• To highlight the challenges in patient outcome prediction compared to AI-driven diagnostics.
• To emphasize the potential of human intuition and insight as predictive information sources.
• To advocate for joint investigation of human and AI predictive capabilities for synergistic outcomes.

Main Methods:

• Literature review comparing AI-based and human clinical prediction performance.
• Conceptual framework for integrating human insight with AI in predictive modeling.
• Discussion on the complementary roles of AI and clinicians in outcome prediction.

Main Results:

• AI has achieved breakthroughs in medical imaging but faces unique challenges in outcome prediction.
• Human intuition and insight represent valuable, potentially underutilized, predictive data.
• A significant gap exists in the literature comparing AI and human performance in patient outcome prediction.

Conclusions:

• Patient outcome prediction requires novel approaches beyond current AI diagnostic successes.
• Integrating human clinical expertise with AI offers a promising path toward enhanced predictive accuracy.
• Future research should focus on achieving a human-AI symbiosis for superior patient outcome prediction.