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Introduction Empathic healthcare improves patient satisfaction with care, anxiety and pain, while reducing practitioner burnout. Artificial intelligence (AI) is continuously advancing and changing the context of empathy in healthcare. While AI may improve diagnostic accuracy or help streamline processes to reduce workload, there is a concern about how AI will impact human patient–practitioner relationships. However, patient and practitioner experiences of AI in healthcare are underexplored. We therefore aimed to synthesise the findings of qualitative studies which explore patient and practitioner experiences of empathy in AI-supported encounters in healthcare. Methods and analysis We will include any qualitative study in which patient or practitioner experiences of empathy with AI-assisted healthcare are explored. Secondary studies, quantitative studies and those exploring other stakeholders’ experiences will be excluded. The search will include records from database inception in any language. The search strategy is based on the Population, Phenomenon of Interest, Context framework, built around the keywords: artificial intelligence, empathy, healthcare professionals and patients. The following databases will be searched: MEDLINE, Scopus, APA PsycINFO and CINAHL. Additionally, grey literature searches in BASE and OpenAIRE. Forward and backward citation chasing will also be conducted. Records will be screened by two independent reviewers, data extraction will be conducted by one reviewer and checked by another. The risk of bias assessment will be conducted in duplicate using the Joanna Briggs Institute appraisal tool for qualitative studies. The results will be synthesised using thematic synthesis. The number of records identified from the search and the exclusions to reach the total number of included records will be presented in a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow diagram. The included studies will be listed, along with summaries of relevant study characteristics and risk of bias assessments. Confidence in the evidence will be assessed using the Grading of Recommendations Assessment, Development and Evaluation - Confidence in the Evidence from Reviews of Qualitative research framework. Ethics and dissemination The systematic review will include only previously anonymised data from primary studies. For this reason, ethical approval is not required for this study. Dissemination of the findings of the final systematic review will occur through publishing in a peer-reviewed journal. PROSPERO registration number CRD420261301427.
Conradi et al. (Mon,) studied this question.