Designing Explainable AI for Healthcare Reviews: Guidance on Adoption and Trust

Researchers conducted a mixed-methods study evaluating an explainable AI system for analyzing healthcare reviews, surveying 60 participants and conducting expert interviews. The study found strong demand for AI transparency in healthcare decision-making, with 82% of respondents saying they want to understand AI classification reasoning and 84% considering explainability important for trust.

• →82% of survey participants agreed the AI system saves time in reviewing healthcare providers, with 78% saying it highlights essential information.
• →84% of respondents considered it important to understand why AI classifies reviews in certain ways, with 82% saying explanations would increase their trust.
• →45% of participants preferred combined text-and-visual explanations over other explanation formats.
• →Key requirements identified include accuracy, clarity, simplicity, responsiveness, data credibility, and unbiased processing.
• →The research provides actionable design guidance for creating layered, audience-aware explanations in healthcare AI systems.