In a groundbreaking evaluation of artificial intelligence-powered mobile health applications "A Comprehensive Comparison and Evaluation of AI-Powered Healthcare Mobile Applications’ Usability", researchers have found significant usability and transparency gaps in leading mHealth tools, highlighting the limitations of current AI-driven healthcare apps and call for urgent design improvements to enhance trust and patient safety.
None of the systems provided confidence levels, rationale for diagnoses, or links to clinical guidelines that could help users understand AI-generated recommendations. These transparency gaps were also flagged by expert evaluators, who identified failures in explainability heuristics as high-severity usability issues.
The study recommends several design enhancements. Developers should implement confidence indicators that show how certain the AI is about its recommendations. They should also provide optional explanations that clarify how user inputs influence the system’s outputs. Progressive disclosure, where users can toggle between summary and detailed views, is proposed as a solution to balance simplicity with transparency. Additionally, improvements in error prevention, personalization, and multilingual support are emphasized to broaden accessibility.
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