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Evaluation of AI-Assisted MRI in Early Cancer Detection: benefits, barriers and drivers for Radiologist Work Efficiency and Trust in AI
| dc.contributor.author | Shinde, Dhawal | |
| dc.date.accessioned | 2025-06-27T08:26:42Z | |
| dc.date.available | 2025-06-27T08:26:42Z | |
| dc.date.issued | 2025-05 | |
| dc.description.abstract | AI in MRI is becoming a revolutionary technique in early cancer diagnosis, thereby fulfilling the need for improving diagnostic tests and radiologists' productivity. However, apprehension about legal responsibility, data bias, and the impact of AI on the radiologists' mental load were also identified. Even though it aided in diagnosis enhancement and lessened tedious tasks, pressure due to validation was a disadvantage of AI. Thus, focusing on this, the primary aim of this research was to assess the use of AI in MRI, emphasizing benefits, barriers, and adoption determinants regarding radiologists' trust and integration into their work. The main research question studied how diagnosis precision, usability, and interpretability levels from AI systems affect radiologists' acceptance and perception of these systems. In the current research, quantitative and qualitative data were collected through a survey of participants and interviews of oncology and diagnostic imaging employees. The study revealed that radiologists accept AI systems relatively when their outputs are explainable. Training, familiarization and forum support were established as other key adoption influences. Therefore, AI should assist radiologists, and further development should concentrate on making AI tools more interpretable, ethical, and easily integrated into clinical practice to guarantee actors' trust, safety, and performance in the diagnostics sphere. | |
| dc.identifier.uri | https://hdl.handle.net/20.500.14136/254 | |
| dc.title | Evaluation of AI-Assisted MRI in Early Cancer Detection: benefits, barriers and drivers for Radiologist Work Efficiency and Trust in AI | |
| dc.type | Thesis |