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Published on: July 11, 2025
Artificial intelligence in pathology: Perceived diagnostic utility and challenges from a cross-sectional survey in
Manupriya Sharma1, Kavita Kumari2, Navpreet3
1Department of Pathology and Laboratory Medicine, All India Institute of Medical Sciences, Bilaspur, Himachal Pradesh, India.
Background:
Artificial intelligence (AI) is increasingly recognized as a transformative force in pathology. While international surveys have documented optimism toward AI, concerns about training and ethics, data from India are limited.
Aim:
This study aimed to assess the perceived diagnostic utility of AI among pathologists in Northern India, their emotional responses, ethical concerns, and expectations for integration.
Materials And Methods:
A cross-sectional, questionnaire-based survey was conducted between July and December 2024 among practicing pathologists in Northern India. The instrument comprised 21 items covering four domains: diagnostic utility of AI, perceptions and emotional responses, ethical and regulatory concerns, and collaboration with AI professionals. Responses were collected using Likert scale. Data were analyzed using descriptive statistics and Chi-square/Fisher's exact tests, with P < 0.05 considered significant.
Results:
A total of 138 pathologists responded (mean age 38.5 ± 8.2 years; 66.7% female; 78.3% academic). Mitotic figure detection received strongest endorsement, with over 70.0% rating AI's potential as strong to very strong. Other tasks-tumor margin detection, immunostain/genetic panel suggestions, and IHC result evaluation were viewed favorably. Older respondents (≥40 years) were significantly more likely to value AI for diagnostic suggestions (87.5 vs. 72.2%, P = 0.041). Overall, 82.0% believed that AI would transform pathology, yet only 06.5% had formal training despite 92.8% expressing interest. Ethical concerns were common, and only 5.1% respondents felt that current regulations were adequate.
Conclusions:
Pathologists in Northern India view AI with cautious optimism, particularly for pattern-recognition tasks. However, persistent gaps in training, infrastructure, and regulatory clarity remain barriers.
