Artificial Intelligence–Informed Patients and the Transformation of Doctor–Patient Communication: Evidence from a Mixed-Methods Study in Georgian Outpatient Clinics
DOI:
https://doi.org/10.5281/zenodo.19118367Keywords:
patient-centred care, Patient Empowerment, Doctor-patient relationship, Digital health literacy, Clinical communication, Artificial intelligence in healthcare, doctor–patient communication, digital health literacy, AI-informed patients, mixed-methods studyAbstract
Background:
The rapid expansion of artificial intelligence (AI)–driven digital tools capable of generating health-related explanations is transforming how individuals seek and interpret medical information. Increasingly, patients arrive at clinical consultations after interacting with conversational AI systems, symptom checkers, and other algorithm-based health platforms. While internet-based health information has influenced doctor–patient communication for more than two decades, generative AI technologies represent a qualitatively different development because they synthesize complex medical knowledge and provide interactive responses that resemble clinical dialogue. Despite growing public use of AI health tools, empirical evidence examining how AI-generated health information affects real doctor–patient interactions remain limited.
Objective:
This study aimed to investigate how AI-generated health information influences patient behaviour, physician perceptions, and communication dynamics during outpatient clinical consultations.
Methods:
An exploratory convergent mixed-methods study was conducted between November 2024 and January 2025 in two outpatient clinics in Kutaisi, Georgia. Quantitative data were collected through structured questionnaires completed by 127 adult patients, examining patterns of digital and AI-based health information use prior to consultations. In parallel, 45 physicians completed structured questionnaires and participated in semi-structured interviews exploring their experiences with AI-informed patients. Quantitative data were analysed using descriptive statistics. Qualitative interview data were analysed using thematic analysis to identify recurring communication patterns and physician experiences.
Results:
Among 127 surveyed patients, 68% reported using AI-based health tools prior to consultation, while 76% reported searching for health information online more generally. Among 45 participating physicians, 87% reported encountering AI-informed patients at least monthly and 53% reported such encounters weekly. Physicians evaluated AI-generated patient information as partially accurate in 62% of cases, completely accurate in 18%, and misleading in 20%. Approximately 60% of physicians reported that consultations involving AI-informed patients required additional time to interpret or contextualize algorithm-generated information. Only 42% of patients disclosed their use of AI tools during consultations, indicating a substantial communication gap between patient behaviour and physician awareness.
Conclusions:
AI-generated health information is reshaping the informational environment of clinical encounters. Rather than replacing physicians as sources of medical knowledge, AI technologies appear to redefine the physician’s role toward interpreting and contextualizing externally generated health information. These findings highlight the importance of integrating digital health literacy and AI communication competencies into medical education and healthcare practice to support effective physician–patient communication in the era of AI-informed patients.
Keywords: Artificial intelligence in healthcare; doctor–patient communication; AI-informed patients; digital health literacy; health information seeking; mixed-methods research.
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Copyright (c) 2026 MARI RODONAIA, IRINE PKHAKADZE, GIORGI PKHAKADZE, TAMAR TALAKVADZE
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