Patient Perceptions of Artificial Intelligence–Supported Shared Decision-Making in UK Primary Care for Multiple Long-Term Conditions: Qualitative Study

AI-Simulated Patients for Training Shared Decision-Making: Feasibility Study in Medical Education

Background Shared decision-making (SDM) is a key element of patient-centered care; however, opportunities for structured and scalable SDM training remain limited in both medical education and clinical practice. Advances in AI have enabled chatbot-based simulations that may support repeated practice and provide automated feedback on SDM-related communication behaviors. Objective This study aimed to (1) evaluate the agreement between AI-generated and human ratings of SDM performance, (2) compare SDM-related performance and attitudes between medical students and physicians, and (3) explore changes in communicative self-efficacy (CSE) following chatbot-based SDM practice. Methods We conducted a feasibility study using a pre-post mixed methods design. Medical students and physicians practiced SDM in a 20-minute chat-based interaction with an AI-simulated patient, followed by automated feedback on SDM performance. Prior to the interaction, participants’ attitudes toward SDM (IcanSDM) and their CSE (Self-Efficacy in Patient-Centeredness Questionnaire [SEPCQ-24-GER]) were assessed; CSE was reevaluated after the intervention. SDM performance was measured using the validated Observing Patient Involvement in Decision Making (OPTION-12) scale, with the AI evaluating participants’ performance from the patient’s perspective. These AI-generated ratings were then compared with human ratings to evaluate agreement and rating quality. To further investigate discrepancies between AI and human ratings, a qualitative analysis of selected cases was conducted. Additional measures included demographics, perceived authenticity, and benefits of the interaction. Quantitative analyses included group comparisons, pre-post analyses, and psychometric evaluation of the OPTION-12 scale in an AI-mediated setting. The study was preregistered on the Open Science Framework. Results A total of 58 participants were included in the analysis, 22 (37.9%) of whom were students, 34 (58.2%) were physicians, and 2 did not declare. Both groups reported similarly positive attitudes toward SDM, but medical students (n=22; mean 28.67, SD 4.32) achieved higher OPTION-12 performance scores than physicians (n=34; mean 22.79, SD 8.14). CSE showed a slight decrease following the intervention. Agreement between AI-generated and human OPTION-12 total scores was high (intraclass correlation coefficient=0.86, df=2,1; P<.001), although AI ratings were systematically higher. The OPTION-12 scale in the AI setting demonstrated good internal consistency (Cronbach α=0.88; mean interitem correlation r=0.43). Participants perceived the AI-simulated interaction as authentic and potentially useful for SDM training. Conclusions This feasibility study provides preliminary and exploratory insights into the use of AI-simulated patients for SDM training in medical education. The findings contribute to the contextual validation of the OPTION-12 scale in AI-mediated consultations and provide valuable insights into participants’ attitudes toward SDM. However, while findings suggest potential for supporting communication skills development and structured feedback, conclusions are limited by the study design and sample size. Further research with larger samples and controlled designs is needed to establish effectiveness and generalizability.

Artificial Intelligence-Driven Clinical Decision Support in Pakistan: Implications for Primary Care Practice

AI-integrated Clinical Decision Support Systems (AI-CDSS) consolidate patient data with medical expertise to aid healthcare practitioners in deciding their approach. They promote guideline compliance, enhance treatment safety, and lead to more accurate diagnoses. This narrative review examines the potential role and emerging applications of AI-CDSS in strengthening primary healthcare systems by reviewing existing literature and evidence to evaluate current implementations, benefits, and limitations of AI-CDSS in the local healthcare context, with a particular focus on gaps to be addressed in the Pakistani healthcare landscape. Currently, although Pakistan is only utilizing AI-CDSS at a small scale and limited to tertiary care settings, these efforts encompass multiple domains such as drug safety, diagnostic help, and treatment planning. Findings have shown positive results and advocate for implementing AI-CDSS systems on a larger scale, which can reduce the burden on the healthcare system in Pakistan. Unfortunately, multiple concerns, such as user adoption and warning fatigue, must be dealt with before implementing a large-scale application of this system. Our research proposes a framework for the sustainable integration of AI-CDSS into Pakistan's healthcare system, with a focus on scaling solutions to primary care environments such as Basic Health Units and rural health centers. Overall, AI-CDSS holds significant potential to enhance accessibility, quality, and capacity of primary healthcare in Pakistan, provided that existing systemic and operational barriers are effectively addressed.

DETERMINANTS OF ARTIFICIAL INTELLIGENCE-BASED CLINICAL DECISION SUPPORT SYSTEMS ADOPTION AMONG MENTAL HEALTH PROFESSIONALS: THE ROLES OF EMPATHY, ACCOUNTABILITY AND TRUST

This article shows how the Technology Acceptance Model and Technology Trust are combined in the Unified Theory of Acceptance and Use of Technology (UTAUT) and how the Perceived Empathy Model (PEM) and Perceived Clarity of Accountability Model (PCA) are extensions of these models.

Public perceptions of AI-driven decision-making in healthcare: A structural equation modeling approach

Artificial intelligence (AI) is increasingly integrated into healthcare to support diagnostics, decision-making, and administrative processes. However, the successful implementation of AI depends not only on technical performance but also on public perceptions of its helpfulness, riskiness, and fairness. This study examines public perceptions of automated decision-making (ADM) in healthcare. Data were drawn from the first wave of an ongoing longitudinal survey panel. The final sample consisted of 3,915 respondents and was analyzed with structural equation modeling. Perceptions of ADM in healthcare as helpful, risky, and fair were treated as the dependent variables. AI literacy, familiarity with different forms of AI, confidence in clinicians'ability to distinguish AI- from human-generated content, use of conversational agents for health information, and use of traditional digital health information sources were included as exogenous. Greater familiarity with different forms of AI, higher confidence in the clinician's ability to recognize AI-generated content, and use of conversational agents for health information were associated with greater perceived helpfulness. Use of conversational agents was associated with lower perceived risk, whereas greater familiarity with AI and greater reliance on traditional health information sources were associated with higher perceived risk. Perceptions of ADM as fair were most strongly predicted by confidence in the clinician's ability, with additional small positive associations with AI familiarity, AI literacy, and use of conversational agents. Public perceptions of ADM in healthcare are shaped by technological familiarity, use of conversational agents, and confidence in human oversight. Overall, ADM's perceived helpfulness and fairness are driven more by trust in healthcare professionals than by trust in the technology itself.

Advancing Human-Centered AI in Clinical Decision Support: Sociocognitive Human-in-the-Loop Study in HIV Care

By extending a sociocognitive framework through empirical grounding in HIV clinical practice, this study offers design insights for developing AI-powered CDSS that are trustworthy, context-aware, and capable of supporting actionable decision-making in HIV care settings and beyond.

['Shared Steps & Checks': A pragmatic model for shared decision making in mental health care].