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Received — 28 November 2025 ⏭ Favourites

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

✇Journal of Medical Internet Research
By: Alexander Borg · Jonathan Schiött · William Ivegren · Cidem Gentline · Viking Huss · Anna Hugelius · Benjamin Jobs · Fabricio Espinosa · Mini Ruiz · Samuel Edelbring · Carina Georg · Gabriel Skantze · Ioannis Parodis
28 November 2025 at 06:00
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.

Harnessing Single-Cell RNA-Seq for Computational Drug Repurposing in Cancer Immunotherapy

✇Omics In Lung
By: Olivia J Cheng · T T T Tran · Y Ann Chen · Aik Choon Tan
27 November 2025 at 19:00

Pharmaceuticals (Basel). 2025 Nov 20;18(11):1769. doi: 10.3390/ph18111769.

ABSTRACT

Immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment and show notable success in some cancer types such as non-small cell lung cancer, melanoma and colorectal cancers, while they demonstrate relatively low response rate in others, such as esophageal cancers. Due to the heterogeneous nature of the tumor microenvironment and patient-to-patient variability, there remains a need to improve ICI response rates. Combining ICIs with therapies that can overcome resistance is a promising strategy. Compared to de novo drug development, drug repurposing offers a faster and more cost-effective approach to identifying such combination candidates. A variety of computational drug repurposing tools leverage genomics and/or transcriptomic data. As single-cell RNA sequencing (scRNA-seq) technology becomes available, it enables precise targeting of cancer-driving cellular components. In this review, we highlight current computational drug repurposing tools utilizing scRNA-seq data and demonstrate the application of two such tools, scDrug and scDrugPrio, on an esophageal squamous cell carcinoma dataset to identify potential drug candidates for combination with ICI therapy to enhance treatment response. scDrug focuses on predicting tumor cell-specific cytotoxicity, while scDrugPrio prioritizes drugs by reversing gene signatures associated with ICI non-responsiveness across diverse tumor microenvironment cell types. Together, this review underscores the importance of a multi-faceted approach in computational drug repurposing and highlights its potential for identifying drugs that enhance ICI treatment. Future work can expand the application of these strategies to multi-omics and spatial transcriptomics datasets, as well as personalized patient samples, to further refine drug repurposing involving ICI therapy.

PMID:41305010 | PMC:PMC12655618 | DOI:10.3390/ph18111769

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

✇MRD
By: Kristin Løge Aanestad · Marie Austdal · Oddmund Nordgård · Gunnar Mellgren · Satu Oltedal · Marie L Austbø · Ylva H Vignes · Thomas Helland · Kristin Jonsdottir · Tone H Lende · Emilius A M Janssen · Bjørnar Gilje · Kjersti Tjensvoll · PBCB study group
27 November 2025 at 19:00

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

Harnessing Single-Cell RNA-Seq for Computational Drug Repurposing in Cancer Immunotherapy

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Olivia J Cheng · T T T Tran · Y Ann Chen · Aik Choon Tan
27 November 2025 at 19:00

Pharmaceuticals (Basel). 2025 Nov 20;18(11):1769. doi: 10.3390/ph18111769.

ABSTRACT

Immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment and show notable success in some cancer types such as non-small cell lung cancer, melanoma and colorectal cancers, while they demonstrate relatively low response rate in others, such as esophageal cancers. Due to the heterogeneous nature of the tumor microenvironment and patient-to-patient variability, there remains a need to improve ICI response rates. Combining ICIs with therapies that can overcome resistance is a promising strategy. Compared to de novo drug development, drug repurposing offers a faster and more cost-effective approach to identifying such combination candidates. A variety of computational drug repurposing tools leverage genomics and/or transcriptomic data. As single-cell RNA sequencing (scRNA-seq) technology becomes available, it enables precise targeting of cancer-driving cellular components. In this review, we highlight current computational drug repurposing tools utilizing scRNA-seq data and demonstrate the application of two such tools, scDrug and scDrugPrio, on an esophageal squamous cell carcinoma dataset to identify potential drug candidates for combination with ICI therapy to enhance treatment response. scDrug focuses on predicting tumor cell-specific cytotoxicity, while scDrugPrio prioritizes drugs by reversing gene signatures associated with ICI non-responsiveness across diverse tumor microenvironment cell types. Together, this review underscores the importance of a multi-faceted approach in computational drug repurposing and highlights its potential for identifying drugs that enhance ICI treatment. Future work can expand the application of these strategies to multi-omics and spatial transcriptomics datasets, as well as personalized patient samples, to further refine drug repurposing involving ICI therapy.

PMID:41305010 | DOI:10.3390/ph18111769

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