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AI-Enhanced Social Robotic Versus Computer-Based Virtual Patients for Clinical Reasoning Training in Medical Education: Observational Crossover Cohort Study

Background: Virtual patient (VP) simulations can be used to practice clinical reasoning (CR) in controlled learning environments. Traditional computer-based VP platforms often lack the authenticity and interactivity required for effective CR training. Artificial intelligence (AI)–enhanced social robotic VPs can enhance realism and engagement; however, quantitative evidence comparing them with conventional VP platforms remains limited. Objective: We compared medical students’ experience of an AI-enhanced social robotic versus a conventional computer-based VP platform regarding the extent to which the design characteristics of the respective platform facilitate CR skill training. Methods: This observational crossover cohort study involved 178 sixth-semester medical students at Karolinska Institutet, Stockholm, Sweden (response rate: 42.3%; 178 of 421 invited students; Spring 2024-Spring 2025), who experienced both a large language model–enhanced social robotic VP platform supporting dialogue (social artificial intelligence–enhanced robotic interface [SARI]) and a conventional computer-based VP platform (virtual interactive case [VIC]) during their clinical rotation within rheumatology. Platform order was determined by clinical rotation scheduling. VP design was evaluated using a validated questionnaire across 5 domains: authenticity, professional approach, coaching quality, learning effects, and overall judgment. Students’ CR training preferences were assessed using categorical responses and a Visual Analogue Scale, where a lower score favored SARI and a score of 5 indicated equal preference between platforms. Results: SARI outperformed VIC across all 5 VP design domains. Students rated SARI higher for authenticity (median 4.0, IQR 3.5-4.5 vs 3.0, IQR 2.5-3.5; P<.001 professional approach iqr vs>P<.001 coaching quality iqr vs>P<.001 learning effect iqr vs>P<.001 and overall judgment vs iqr>P<.001 students strongly preferred sari for cr training vs odds ratio ci>P<.001 with visual analogue scale scores confirming this preference iqr>P<.001 preferences were consistent across most subgroups prior vp experience and platform order in the difference was not significant that is students with vs or ci>P=.11) and students first introduced to VIC (55% vs 45%; OR 1.5; 95% CI 0.7-2.9; P=.33). Conclusions: Our findings provide the first quantitative evidence that AI-enhanced social robotic VPs offer superior design characteristics than conventional computer-based platforms for CR training in medical education. These results support the use of AI-driven social robots for VP simulations to better prepare medical students for real clinical encounters, and warrant future research on objective CR skill outcomes and long-term transfer to clinical practice. Unlike previous qualitative studies examining each platform separately, this study provides the first quantitative comparison of design characteristics between AI-enhanced social robotic and conventional computer-based VPs.

Monitoring of circulating tumor DNA allows early detection of disease relapse in patients with operable breast cancer

Mol Oncol. 2025 Nov 27. doi: 10.1002/1878-0261.70170. Online ahead of print.

ABSTRACT

Breast cancer is known for late recurrences, yet current follow-up lacks radiological or blood-based monitoring for systemic relapse. This study evaluated circulating tumor DNA (ctDNA) monitoring for early detection of systemic relapse after curative treatment. In this case-control study of 70 patients with operable breast cancer (35 with relapse and 35 without relapse), blood samples were collected every 6-12 months during a median 8.3-year follow-up. ctDNA was analyzed by targeted DNA sequencing using Oncomine™ Breast cfDNA Research Assay v2, and results were compared to genetic analysis of tumor and metastasis biopsies. ctDNA was detected at relapse in 19 of 35 (54%) patients with disease relapse and preceded clinical or radiological relapse detection in 17, with a median lead time of 10.3 months. In 13 (68%) patients, there was concordance with tumor mutations, and in seven patients, there was also concordance with metastasis. Among the relapse-free patients, seven were ctDNA-positive postsurgery, and only one of them had a match among the tumor variants. These findings suggest serial ctDNA analysis may enable earlier detection of systemic relapse in patients with operable breast cancer.

PMID:41307327 | DOI:10.1002/1878-0261.70170

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