The Impact of Artificial Intelligence on Internal Medicine Physicians: A Survey of Procedural and Non-procedural Specialties.

BACKGROUND

Artificial intelligence (AI) is increasingly being integrated into various aspects of healthcare, including internal medicine. However, the impact of AI on physicians across different internal medicine specialties remains unclear. This study assesses AI's adoption, utilization, and perceived impact among procedural and non-procedural internal medicine physicians.

METHODS

A comprehensive survey questionnaire was designed to cover current AI use, perceived impact on diagnostic accuracy, treatment decisions, patient outcomes, challenges, ethical concerns, and future expectations. The survey was distributed to a diverse sample of internal medicine physicians across various specialties, including procedural (e.g., interventional cardiology, gastroenterology) and non-procedural (e.g., endocrinology, rheumatology) fields. Responses were analyzed using descriptive statistics, chi-square tests, t-tests, and logistic regression.

RESULTS

The survey received responses from 22 internal medicine physicians, with 64% (n=14) representing procedural specialties and 36% (n=8) representing non-procedural specialties. Sixty-eight percent (n=15) of respondents reported using AI tools in their practice, with higher adoption rates among procedural specialties (n=11, 79%) compared to non-procedural specialties (n=4, 50%). Surveyed physicians reported that AI improved diagnostic accuracy (n=12, 80%), treatment decisions (n=10, 67%), and patient outcomes (n=13, 87%). However, 55% (n=12) of respondents expressed concerns about the interpretability and transparency of AI algorithms. Non-procedural specialists were more likely to perceive AI as a threat to their job security (n=3, 38%) than procedural specialists (n=3, 21%). The most common challenges to AI adoption were lack of training (n=16, 73%), cost (n=13, 59%), and data privacy concerns (n=11, 50%).

CONCLUSION

This study assesses the perceived impact of AI on internal medicine physicians, highlighting the differences between procedural and non-procedural specialties. The findings underscore the need for specialty-specific considerations in developing and implementing AI tools. While AI can potentially improve diagnostic accuracy, treatment decisions, and patient outcomes, addressing challenges such as lack of training, cost, and data privacy concerns is crucial for widespread adoption. Moreover, the study emphasizes the importance of ensuring the interpretability and transparency of AI algorithms to foster trust among physicians. As AI continues to evolve, it is essential to engage internal medicine physicians across specialties in the development process to create AI tools that effectively complement their expertise and improve patient care. Further research should focus on developing best practices for AI integration in internal medicine and evaluating the long-term impact on patient outcomes and healthcare systems.