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Peer-reviewedCC BY-NC-NDThe Indian journal of radiology & imaging

Building Trust in Radiology AI: Challenges in Data, Bias, and Accountability.

  • Ranjan Pal1
  • Mahesh Devnani2
  • Bodhibrata Nag3
  • Binit Sureka4
4 affiliations
  1. 1MIT Sloan School of Management, Massachusetts Institute of TechnologyUnited States.
  2. 2Department of Hospital AdministrationPostgraduate Institute of Medical Education and ResearchChandigarhIndia.
  3. 3Indian Institute of ManagementCalcuttaWest BengalIndia.
  4. 4Department of Diagnostic & Interventional RadiologyAll India Institute of Medical Sciences JodhpurRajasthanIndia.
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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.

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Cite

BibTeXRIS
BibTeX
@article{pal2026building,
  title = {{Building Trust in Radiology AI: Challenges in Data, Bias, and Accountability.}},
  author = {Ranjan Pal and Mahesh Devnani and Bodhibrata Nag and Binit Sureka},
  journal = {The Indian journal of radiology \& imaging},
  year = {2026},
  month = jul,
  doi = {10.1055/s-0046-1825829},
  url = {https://europepmc.org/article/MED/42921644},
  note = {CC BY-NC-ND},
}
RIS
TY  - JOUR
TI  - Building Trust in Radiology AI: Challenges in Data, Bias, and Accountability.
AU  - Ranjan Pal
AU  - Mahesh Devnani
AU  - Bodhibrata Nag
AU  - Binit Sureka
PY  - 2026
DA  - 2026/07/23
JO  - The Indian journal of radiology & imaging
DO  - 10.1055/s-0046-1825829
UR  - https://europepmc.org/article/MED/42921644
AB  - 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.
SN  - 0971-3026
SN  - 1998-3808
ER  -