Workflow analysis and evaluation of a next-generation phenotyping tool: A qualitative study of Face2Gene

The diagnosis of rare genetic disorders often involves prolonged delays, with facial features serving as key diagnostic clues. Next-Generation Phenotyping (NGP) tools, such as Face2Gene, utilize Artificial Intelligence (AI)-driven algorithms to analyze patient photographs and list differential diagnoses based on facial dysmorphism. Despite their growing use and proven clinical value, their integration into clinical workflows remains poorly understood. This study evaluates Face2Gene's implementation into routine clinical care with barriers and facilitators to successful adoption. We conducted a literature review, followed by in-depth interviews with 15 geneticists across university hospitals in Germany. Results showed an overall positive appraisal of the tool among clinicians with emphasis on its usability. Key workflow barriers comprised IT integration and patient consent process. Despite being an additional step in the diagnostic pathway, Face2Gene has been effectively incorporated into geneticists' diagnostic routines, facilitating decision processes, and potentially expediting diagnoses for some patients. Our findings contribute to the existing literature on NGP technologies by demonstrating that effective integration of Face2Gene can enhance clinicians' efficiency and quality of work. To maximize impact of NGP technologies in genetic medicine, future implementation efforts should strive for clinicians' acceptance particularly through user-friendly design and sustained organizational support in course of workflow implementation. Study registration: German Register for Clinical Trials (DRKS) DRKS00032436.