A Comparative Study Assessing the Confidence of Doctors in Conventional vs Artificial Intelligence Assisted Radiological Diagnoses for Patient Care

Abstract

Ilfa Fida Puzhakkal, Shripal Shah, Supriya Nair, Parth K. Patel, Abhina George, Vidhi Adya, Arsene Koumbem and Drishya Kanhangad

Introduction: The integration of Artificial Intelligence (AI) into radiology has shown promise in enhancing diagnostic accuracy and efficiency, yet the confidence of doctors in AI-assisted diagnosis remains uncertain. AI's potential to streamline workflows and detect complex abnormalities is widely acknowledged, but skepticism persists regarding its reliability and the potential dis- ruption of traditional radiological practices. This study aims to assess global doctors' confidence in AI-assisted radiology and explore factors influencing their acceptance of AI technologies.

Methods: This descriptive cross-sectional survey involved 384 doctors from diverse clinical settings worldwide. A self-admin- istered questionnaire captured demographic data, confidence in AI versus conventional radiology, and perceptions of AI in clinical practice. Data were analyzed using descriptive statistics.

Results: The majority of participants (66.7%) expressed higher confidence in conventional radiologist-led diagnoses compared to AI-assisted interpretations. Confidence in AI tools averaged 5.35/10, with limited AI training (16.9%) and lack of trust (13%) as the primary challenges. Participants with more experience reported greater confidence in interpreting radiographs inde- pendently and relied less on radiologists. Common challenges in conventional radiology included delays (35%) and limited access to radiologists (26%). AI was seen as beneficial for routine cases but not yet trusted for complex diagnoses, with only 36.7% believing it will eventually surpass human expertise.

Conclusion: Doctors continue to favor conventional radiologist-led diagnostics over AI-assisted tools due to concerns about trust, reliability, and insufficient training. While AI holds potential for improving diagnostic accuracy and reducing time con- straints, widespread adoption requires overcoming significant barriers. Radiologists remain crucial in clinical decision-making, and AI will likely serve as a supplementary tool until confidence in its capabilities improves.

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