The New Gravity: How Generative AI Remapped Innovation and What Academia Must Do Next.
7 affiliations
- 1Department of Dermatology, Oregon Health & Science University, Portland, OR, United States.
- 2Faculty of Medicine and Health, University of New South Wales (UNSW Sydney), Sydney, NSW, Australia.
- 3Department of Dermatology, Case Western Reserve University School of Medicine, Cleveland, OH, United States.
- 4Healthcare, Microsoft, Redmond, WA, United States.
- 5Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA, 02115, United States.
- 6Broad Institute of MIT and Harvard, Cambridge, MA, United States.
- 7Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States.
Abstract
Unlabelled
Artificial intelligence is rapidly reshaping clinical research, diagnostics, health system operations, and medical education. Frontier generative AI development is increasingly concentrated in private industry, whose computational infrastructure, proprietary datasets, and capital exceed those of most academic institutions. Although not designed exclusively for medicine, these systems increasingly influence clinical applications. This shift raises a question for academic medical centers (AMCs): What role remains when leading AI systems emerge outside universities? This viewpoint examines industry's growing role in generative AI, clarifies AMCs' responsibilities, and proposes a framework for AI governance and literacy across undergraduate, graduate, and continuing medical education (CME). Rather than compete with industry on computational scale, AMCs should reinforce functions for which AMCs have distinctive strengths. These include (1) mechanistic inquiry linking algorithmic outputs to biological and pathophysiologic understanding; (2) interdisciplinary synthesis integrating clinical, ethical, legal, and sociotechnical perspectives; (3) independent and methodologically rigorous validation of clinical AI tools; and (4) institutional stewardship of ethical governance for patient populations that are often underrepresented in commercial datasets. As nonprofit institutions training clinicians, AMCs have a distinct role in safeguarding transparency, fairness, and accountability in AI-enabled health care. We position this 4-role framework to refine AI governance and translational medicine approaches, clarifying which responsibilities AMCs should retain while collaborating with industry. The approach includes longitudinal AI literacy training across undergraduate medical education, graduate medical education, and CME, standardized pathways for evaluating clinical AI, stronger conflict-of-interest safeguards, and complementary partnerships with industry and regulators. We also outline educational assessment methods and safety-monitoring processes, including competency-based assessment across undergraduate medical education, graduate medical education, and CME, postdeployment monitoring, and clear escalation triggers. Embedding verification habits and governance into training and practice can help ensure that generative AI supports clinical reasoning while preserving trust, equity, and scientific rigor. This viewpoint addresses academic leaders, curriculum committees, residency directors, faculty developers, and accreditors. Safe clinical AI requires sustained academic leadership.
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BibTeX
@article{akbarialiabad2026gravity,
title = {{The New Gravity: How Generative AI Remapped Innovation and What Academia Must Do Next.}},
author = {Hossein Akbarialiabad and Ayman Grada and Sancy A Leachman and David Rhew and Alireza Haghighi},
journal = {JMIR medical education},
year = {2026},
month = oct,
doi = {10.2196/89741},
url = {https://europepmc.org/article/MED/42896100},
note = {CC BY},
}
RIS
TY - JOUR
TI - The New Gravity: How Generative AI Remapped Innovation and What Academia Must Do Next.
AU - Hossein Akbarialiabad
AU - Ayman Grada
AU - Sancy A Leachman
AU - David Rhew
AU - Alireza Haghighi
PY - 2026
DA - 2026/10/19
JO - JMIR medical education
DO - 10.2196/89741
UR - https://europepmc.org/article/MED/42896100
AB - Unlabelled Artificial intelligence is rapidly reshaping clinical research, diagnostics, health system operations, and medical education. Frontier generative AI development is increasingly concentrated in private industry, whose computational infrastructure, proprietary datasets, and capital exceed those of most academic institutions. Although not designed exclusively for medicine, these systems increasingly influence clinical applications. This shift raises a question for academic medical centers (AMCs): What role remains when leading AI systems emerge outside universities? This viewpoint examines industry's growing role in generative AI, clarifies AMCs' responsibilities, and proposes a framework for AI governance and literacy across undergraduate, graduate, and continuing medical education (CME). Rather than compete with industry on computational scale, AMCs should reinforce functions for which AMCs have distinctive strengths. These include (1) mechanistic inquiry linking algorithmic outputs to biological and pathophysiologic understanding; (2) interdisciplinary synthesis integrating clinical, ethical, legal, and sociotechnical perspectives; (3) independent and methodologically rigorous validation of clinical AI tools; and (4) institutional stewardship of ethical governance for patient populations that are often underrepresented in commercial datasets. As nonprofit institutions training clinicians, AMCs have a distinct role in safeguarding transparency, fairness, and accountability in AI-enabled health care. We position this 4-role framework to refine AI governance and translational medicine approaches, clarifying which responsibilities AMCs should retain while collaborating with industry. The approach includes longitudinal AI literacy training across undergraduate medical education, graduate medical education, and CME, standardized pathways for evaluating clinical AI, stronger conflict-of-interest safeguards, and complementary partnerships with industry and regulators. We also outline educational assessment methods and safety-monitoring processes, including competency-based assessment across undergraduate medical education, graduate medical education, and CME, postdeployment monitoring, and clear escalation triggers. Embedding verification habits and governance into training and practice can help ensure that generative AI supports clinical reasoning while preserving trust, equity, and scientific rigor. This viewpoint addresses academic leaders, curriculum committees, residency directors, faculty developers, and accreditors. Safe clinical AI requires sustained academic leadership.
SN - 2369-3762
ER -