Editorial: Explainable artificial intelligence for trustworthy and human-centric healthcare: methods, evaluation, and clinical impact
Abstract
Artificial intelligence can support healthcare only when its outputs can be assessed in the context of the decisions they inform. A prediction may be accurate yet difficult to scrutinize; an explanation may be persuasive yet provide little evidence that the prediction is dependable. Explainable artificial intelligence (XAI) therefore raises a practical question: what must an explanation enable a clinician, patient, or healthcare organization to understand and do? Answering this question requires connecting computational methods with evaluation, clinical knowledge, and the circumstances of use. This Research Topic brings together 15 contributions addressing that connection across clinical prediction, imaging, physiological signals, biomedical networks, language models, rehabilitation, and healthcare processes. Collectively, the papers illustrate an expanding range of approaches to transparency. They also suggest that trustworthy and human-centric healthcare requires more than the availability of an explanation: it requires evidence that explanations support appropriate reliance, including the ability to question an output and recognize its limitations.Two systematic reviews establish this perspective. Aravindkumar et al. (2026) synthesize 36 studies across imaging, diagnosis, and rehabilitation, identifying recurring relationships between explanation methods and clinical tasks alongside gaps in validation and reporting. Ud Din et al. (2026), reviewing 51 studies involving…
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Evidence Hub reads this as a case-control, done in people. Its evidence level is 4 of 5 (Low · observational). Observational designs can show an association but not prove cause. Reported size: 1,062 participants or records.
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