Large language model-based virtual patients (LLM-VPs) are an emerging simulation tool for health professions education, but their design and integration into curricula are not well characterized. This scoping review mapped how LLM-VPs are being used for simulated clinical learning across health professions. Following a protocol registered on OSF, we searched MEDLINE, EMBASE, CENTRAL, Scopus, and Web of Science to 11 April 2025, per PRISMA-ScR guidelines, and included 21 studies that used LLMs to generate virtual patients for simulated clinical encounters. Data were extracted on technical design, fidelity domains, curricular integration, human factors, and Technology Acceptance Model constructs, and synthesized narratively. Most studies (n = 11) were pilot or feasibility evaluations with small samples (median 21) and used GPT-based models with dynamic text chat. Integration was limited to 10 studies that operated as pilots, 7 as electives, and 3 as core curricular components. The outcomes focused on Level 2 learning (clinical reasoning and preclinical OSCE performance), with predominantly self-report assessments. No studies reported Level 3 or 4 outcomes. Fidelity was strongest in cognitive, socio-cultural, and emotional domains, and 11 studies reported hallucinations or inaccurate outputs. LLM-VPs appear feasible and well-received but remain early-stage, underscoring the need for fidelity-aligned design and more rigorous, longitudinal evaluations.

Background: Medication reconciliation (MedRec) has the potential to improve patient safety by enhancing the continuity of medication information across settings. MedRec involves 3 core tasks: the creation of a best possible medication history, the identification of medication discrepancies among medication lists, and the resolution of medication discrepancies. While artificial intelligence (AI) has the potential to improve MedRec, existing reviews have not identified the ways in which researchers have used AI to facilitate MedRec tasks and their constituent subtasks or the level of automation achieved. Objective: This scoping review aimed to map how previous research has applied AI to MedRec tasks and subtasks and assess the extent of automation achieved. Methods: We searched MEDLINE, Embase, Web of Science, IEEE Xplore, and Compendex in June 2024 for studies that used AI to support a MedRec task or subtask, excluding entirely rule-based tools or studies focused on other aspects of medication management. After screening 2345 unique records, we conducted backward citation searching of studies included at the full-text stage, identifying an additional 795 unique records. We used a 4-stage model of human information processing as a structural lens to guide our considerations of automation, mapping the core tasks of MedRec onto this model. Results: A total of 94 studies met the inclusion criteria. All studies addressed subtasks related to the creation of a best possible medication history. Only 2.1% (n=2) of the studies also addressed the identification of discrepancies. Thus, the highest stage of automated information processing achieved was information analysis, although most studies (92/94, 97.9%) only automated information acquisition steps. Most studies (67/94, 71.3%) used free-text clinical notes from the electronic health record, although a significant proportion (21/94, 22.3%) used images of pills or images of other medication-related items. Studies using text-based data used a variety of machine learning methods (eg, recurrent neural networks, conditional random fields, support vector machines, and transformers), whereas those that leveraged images typically used convolutional neural networks. Most studies (61/94, 64.9%) used publicly available data from benchmarking datasets (eg, n2c2 2022) and were strictly model development studies, with only 1.1% (n=1) being usability studies. Conclusions: This is the first review to consider the role of AI in the automation of MedRec tasks, offering a basis for prioritizing future development efforts. Current applications of AI to automate MedRec tasks are preliminary, with most work focusing on the extraction of medication information and limited to proof-of-concept model development. Future work should consider addressing infrastructural barriers to the AI-based automation of MedRec tasks (eg, data incompleteness in sources of medication information) and exploring approaches to automate discrepancy resolution. Beyond developing models, there is also a need to implement them in tools and evaluate them in real-world contexts.