Acceptability Of Artificial Intelligence By General Practitioners: Analysis Of Affordances And Paradoxes In The Screening Of Neurodegenerative Diseases

Aims: This study analyses the acceptability of artificial intelligence (AI) to general practitioners, particularly in the early detection of neurodegenerative diseases. Methods: An exploratory qualitative method was adopted, with nine semi-directive interviews conducted with doctors, interns and an AI designer. Analysis of the data produced a coding grid structured around an original theoretical framework in information systems (IS) that combines the concepts of affordances and paradoxes, concepts that are commonly used in Management Sciences to understand technology adoption. Results: The results show that doctors have ambivalent perceptions of AI. Positive affordances include the reduction of errors, improved efficiency and support for complex diagnoses. AI is also seen as an opportunity to democratize medical knowledge, make practice more accessible, and improve productivity by allowing more patients to be treated in less time. However, the negative affordances raise several concerns, such as the biases that could affect the decision-making process, the loss of skills, increased dependence on technology, as well as the dehumanization of the doctor-patient relationship and the question of responsibility in the event of error. The study also identifies several paradoxes. For example, AI can both reduce and increase errors or improve skills while running the risk of degrading them because of technological dependence. These paradoxes are key to understanding the dynamics of AI acceptability. Conclusions: The research shows the importance of involving healthcare professionals in the development of AI solutions, improving their technical training and developing new affordances that can overcome paradoxes. By integrating affordances and paradoxes, it proposes an original theoretical framework for studying the integration of AI in general medicine. Future studies should extend the sample to obtain new insights.