Core attributes of the TC.

<div><p>This paper introduces the Team Card (TC) as a protocol to address harmful biases in the development of clinical artificial intelligence (AI) systems by emphasizing the often-overlooked role of researchers’ positionality. While harmful bias in medical AI, particularly in Clinical...

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Bibliographic Details
Main Author:	Lesedi Mamodise Modise (20826978) (author)
Other Authors:	Mahsa Alborzi Avanaki (18268255) (author), Saleem Ameen (20826981) (author), Leo A. Celi (4856749) (author), Victor Xin Yuan Chen (20826984) (author), Ashley Cordes (19730998) (author), Matthew Elmore (6787874) (author), Amelia Fiske (12369388) (author), Jack Gallifant (16827848) (author), Megan Hayes (2171125) (author), Alvin Marcelo (13919054) (author), Joao Matos (14158398) (author), Luis Nakayama (17043453) (author), Ezinwanne Ozoani (20826987) (author), Benjamin C. Silverman (20826990) (author), Donnella S. Comeau (20826993) (author)
Published:	2025
Subjects:	Biotechnology Developmental Biology Science Policy Hematology Biological Sciences not elsewhere classified studies across business organizational contexts demonstrate limited framing neglects embedding epistemic diversity diversity improves outcomes create epistemic limitations mitigate bias effectively address unconscious bias address biases stemming medical artificial intelligence clinical artificial intelligence solving — must div >< p influence ai design address harmful biases researchers ’ positionality complexity </ p clinical decision support effective ai systems harmful bias medical ai amplifying biases clinical settings clinical care reflection — experiences — unexamined subjectivities tcs establish steps taken research teams research practices paper introduces overlooked role overall performance making quality inequitable applications including innovation improve fairness frequently attributed enhancing governance empirical basis demo </ data quality collaborative processes actively cultivated
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Core attributes of the TC.

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