Beyond the Screen: The Impact of Generative Artificial Intelligence (AI) on Patient Learning and the Patient-Physician Relationship

Abstract

The rapid advancement of generative artificial intelligence (AI), exemplified by tools like ChatGPT, has transformed the healthcare landscape, particularly in patient education and the patient-physician relationship. While AI in healthcare has traditionally focused on data analysis and predictive analytics, the rise of generative AI has introduced new opportunities and challenges in patient interactions, information dissemination, and the overall dynamics of patient care. This narrative review explores the dual impact of generative AI on healthcare, examining its role in enhancing patients' understanding of medical conditions, promoting self-care, and supporting healthcare decision-making. Additionally, the review considers the potential risks, such as the erosion of trust in the patient-physician relationship and the spread of misinformation, while addressing ethical implications and the future integration into clinical practice. A comprehensive literature search, conducted using databases like PubMed, MEDLINE, Scopus, and Google Scholar, included studies published between 2010 and 2024 that discussed the role of generative AI in patient education, engagement, and the patient-physician relationship. Findings show that generative AI tools significantly enhance patient health literacy by making complex medical information more accessible, personalized, and interactive, thus empowering patients to take a more active role in managing their healthcare. However, risks such as misinformation and the undermining of the patient-physician relationship were also identified, with case studies highlighting both positive and negative outcomes. To fully harness the potential of AI in healthcare, it is essential to integrate these tools thoughtfully, ensuring they complement rather than replace the personalized care provided by physicians. Future research should focus on addressing ethical challenges and optimizing AI's role in clinical practice to maintain trust, communication, and the quality of patient care.

Keywords: ethical implications of ai; generative ai; healthcare decision-making; patient education; patient-physician relationship.

Figure 1. PRISMA 2020 flow diagram for new systematic reviews which included searches of databases and registers only

The PRISMA flow diagram illustrates the systematic process used to identify, screen, and include studies for this review. An initial 357 records were identified through database searches. Following the removal of 50 duplicates, 307 records were screened, with 200 excluded due to irrelevance or failure to meet inclusion criteria. Among the 107 reports sought for retrieval, 5 could not be retrieved. Of the 102 reports assessed for eligibility, 20 were excluded - 15 for not meeting inclusion criteria and 5 as duplicates at this stage. Ultimately, 82 studies were included in the review, forming the foundation for the study. PRISMA, Preferred Reporting Items for Systematic Reviews and Meta-Analyses

Figure 2. AI Behavioral Change Support System (ABCSS)

The ABCSS is a hypothetical model designed to integrate behavioral health theory into AI-driven health interventions. It aims to promote healthy behaviors and support self-management of chronic conditions by incorporating tailored BCTs. The model leverages established frameworks, including the TTM, HBM, and SCT, to provide personalized, dynamic, and contextually relevant support for users. This model would be well-suited for applications such as managing chronic diseases, improving medication adherence, and encouraging lifestyle modifications through generative AI platforms. AI, Artificial Intelligence; ABCSS, AI Behavioral Change Support System; BCT, Behavior Change Technique; TTM, Transtheoretical Model; HBM, Health Belief Model; SCT, Social Cognitive Theory