Building Trust in Radiology AI: Challenges in Data, Bias, and Accountability.
Authors
Affiliations (4)
Affiliations (4)
- MIT Sloan School of Management, Massachusetts Institute of TechnologyUnited States.
- Department of Hospital AdministrationPostgraduate Institute of Medical Education and ResearchChandigarhIndia.
- Indian Institute of ManagementCalcuttaWest BengalIndia.
- Department of Diagnostic & Interventional RadiologyAll India Institute of Medical Sciences JodhpurRajasthanIndia.
Abstract
Artificial intelligence (AI) is rapidly transforming radiology, with algorithms now assisting in workflow prioritization, lesion detection, and clinical decision support. While the global medical AI market continues to expand, there is a need to ensure trust, safety, and accountability. This commentary highlights key ethical and technical challenges that must be addressed to safeguard trust in radiology's AI-driven future. Central issues include the need for transparent real data for training datasets, as models trained on data may fail when applied to diverse clinical settings. Bias and lack of transparency in algorithms further complicate clinical adoption. In addition, cybersecurity vulnerabilities and privacy risks underscore the need to treat AI systems as regulated clinical infrastructure with robust safeguards. Safe integration of AI in radiology demands not only technological innovation but also human oversight in clinical decision-making.