A patient-centered approach to inclusive AI in healthcare: potential benefits, challenges, and recommendations

Abstract Purpose Artificial Intelligence (AI) has transformed various sectors, including healthcare, where it holds promises for improving early detection, personalized care, and decision-making. However, despite its potential, AI systems often exacerbate health inequalities, particularly among underrepresented communities, due to biased algorithms, limited accessibility, and data exclusion. This current study aims to examine inclusive AI in healthcare grounded on a patient-centered approach. Methods We conducted a literature review following PRISMA 2020 guidelines, searching across online databases for publications from diverse geographic and cultural contexts. Articles were assessed for inclusion based on specific criteria, and data on healthcare, AI type, inclusivity frameworks, implementation methods, and SWOT analysis were extracted. The quality and risk of bias of included articles were assessed with the JBI Checklist for Expert Opinion; and the PROBAST tool. Results Findings from the study suggest that various AI types were discussed, including machine learning, chatbots, and convolutional neural networks. Several key frameworks for ensuring inclusive AI were reported in the included articles, such as ACCEPT-AI, Bias Mitigation, and human rights values frameworks. However, some articles did not mention specific inclusivity frameworks. Our findings suggest that AI in healthcare has the potential to improve health outcomes but must be implemented inclusively and transparently to avoid exacerbating inequalities. Conclusions This review emphasizes the need for ethical, regulated, and collaborative approaches to mitigate bias, enhance accessibility, and ensure patient-centered solutions. Future efforts should focus on stakeholder-inclusive design and standardized frameworks for equitable AI in healthcare. Registration This literature review was not registered.