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AI won't save us…yet: reimagining AI's potential to prioritize people
Lekshmi Santhosh1, Leah J Witt1,2
1Division of Pulmonary, Critical Care, Allergy & Sleep Medicine, University of California, San Francisco, San Francisco, CA, United States.
ATS Scholar
|February 21, 2026
Summary
Artificial intelligence (AI) shows promise for healthcare tasks but fails to address systemic issues in medical education and the U.S. healthcare system. True transformation requires AI to focus on people-centric challenges and equity.
Area of Science:
- Healthcare innovation
- Medical education technology
- Artificial intelligence applications
Background:
- Artificial intelligence (AI) is frequently promoted as a solution for healthcare challenges like administrative burden and diagnostic accuracy.
- However, current AI applications often overlook deeper systemic problems within the U.S. healthcare system and medical education.
Purpose of the Study:
- To critically evaluate the limitations of AI in addressing fundamental issues in healthcare and medical education.
- To identify key areas where AI falls short, particularly in people-focused aspects.
Main Methods:
- Qualitative analysis of current AI trends in healthcare and medical education.
- Identification of five critical areas where AI's impact is insufficient or potentially detrimental.
Main Results:
- AI struggles with systemic healthcare dysfunctions, health equity, physician workload, medical education, and workforce crises.
- Current AI trajectory may worsen existing problems rather than solve them.
Conclusions:
- AI alone cannot fix the U.S. healthcare system or medical education without addressing systemic flaws.
- Transformational change requires AI to be developed with clinical experts and patients, focusing on equity and workforce sustainability.
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