Abstract
BACKGROUND: Image-driven specialisms such as radiology and pathology are at the forefront of medical artificial intelligence (AI) innovation. Many believe that AI will lead to significant shifts in professional roles, so it is vital to investigate how professionals view the pending changes that AI innovation will initiate and incorporate their views in ongoing AI developments.
OBJECTIVE: Our study aimed to gain insights into the perspectives and wishes of radiologists and pathologists regarding the promise of AI.
METHODS: We have conducted the first qualitative interview study investigating the perspectives of both radiologists and pathologists regarding the integration of AI in their fields. The study design is in accordance with the consolidated criteria for reporting qualitative research (COREQ).
RESULTS: In total, 21 participants were interviewed for this study (7 pathologists, 10 radiologists, and 4 computer scientists). The interviews revealed a diverse range of perspectives on the impact of AI. Respondents discussed various task-specific benefits of AI; yet, both pathologists and radiologists agreed that AI had yet to live up to its hype. Overall, our study shows that AI could facilitate welcome changes in the workflows of image-driven professionals and eventually lead to better quality of care. At the same time, these professionals also admitted that many hopes and expectations for AI were unlikely to become a reality in the next decade.
CONCLUSIONS: This study points to the importance of maintaining a "healthy skepticism" on the promise of AI in imaging specialisms and argues for more structural and inclusive discussions about whether AI is the right technology to solve current problems encountered in daily clinical practice.
Original language | English |
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Article number | e52514 |
Journal | JMIR Human Factors |
Volume | 11 |
DOIs | |
Publication status | Published - 21 Nov 2024 |
Keywords
- Humans
- Artificial Intelligence
- Qualitative Research
- Radiologists
- Pathologists
- Interviews as Topic/methods
- Attitude of Health Personnel
- Female
- Male
- Adult