Comprehensive Summary
Schaffernak et al. presents a study regarding the use of AI in clinical practice and analyzes the barriers that are preventing the further integration of AI-enabled clinical decision support systems (AI-CDSS). The research was performed qualitatively through collecting data from a wide range of ophthalmology professionals via online questionnaires and semi-structured interviews. These qualitative collections were prioritized on helping understand the sociotechnical influences that created barriers to entry for AI-CDSS, and their own personal experiences with using AI. After that, the interviews were transcribed and analyzed to find results where many professionals only had minimal experience with AI-CDSS, but with most of the ophthalmologists also agreeing that the use of AI in a clinical support setting would be valuable. As a result, the study found three main attributes that have been vocalized by ophthalmologists to be valuable in order to implement AI-CDSS - improvement in quality of care, facilitation of work, and efficiency gains.
Outcomes and Implications
The research presented by Schaffernak displays importance in demonstrating the reality of AI use in ophthalmologic practice, where many professionals would advocate for integration but simply don’t know how to. With regards to the medical setting, impact on work has been associated with five main categories, including challenges and risks regarding the challenge of using a new tool to such a great extent. In such a detail-oriented profession, it’s difficult to gauge the quality of care that a patient will receive if they are treated with AI; it will be much harder to identify professionalism and accuracy. As such, the authors predict a longer timeline before the AI systems can actually be integrated into the medical setting, as adaptations will have to be much more developed before they can actually present themselves as a viable tool for healthcare professionals.