Journal of Medical Systems最新文献

{"title":"Could Empathy Be Taught? The Role of Advanced Technologies to Foster Empathy in Medical Students and Healthcare Professionals: A Systematic Review.","authors":"Giorgio Li Pira, Chiara Ruini, Francesca Vescovelli, Rosa Baños, Sara Ventura","doi":"10.1007/s10916-025-02144-9","DOIUrl":"10.1007/s10916-025-02144-9","url":null,"abstract":"<p><p>The physician-patient relationship relies mostly on doctors' empathetic abilities to understand and manage patients' emotions, enhancing patient satisfaction and treatment adherence. With the advent of digital technologies in education, innovative empathy training methods such as virtual reality, simulation training systems, mobile apps, and wearable devices, have emerged for teaching empathy. However, there is a gap in the literature regarding the efficacy of these technologies in teaching empathy, the most effective types, and the primary beneficiaries -students or advanced healthcare professionals-. This study aims to address this gap through a literature review following PRISMA guidelines. A comprehensive literature search was conducted in the PsychINFO, Scopus, PubMed, and Web of Science databases using specific keywords. Inclusion criteria for articles were established, and two researchers independently rated the selected articles, resolving any disagreements by consensus. Out of 1137 articles screened, a total of 14 articles were included in this review with a total of 1285 participants, who received empathic training integrated with the use of digital technologies. Only 9 articles defined the construct of empathy, focusing on cognitive, affective, clinical, or cultural aspects. Empathy was assessed with various methods and promoted through various digital technologies, including wearables (e.g. HMDs, SymPulse™ armband) and non-wearable devices (computer monitors, Mobile Apps, Kinect System). Participants were primarily medical students (68.1%), with few healthcare workers (31.9%) and nurses (2.9%). All digital technologies effectively promoted empathy among the target population except for 3 studies that involved advanced career healthcare workers. This review highlights the potential efficacy of digital technologies in fostering empathy among medical students, though not as effectively among advanced healthcare professionals. These insights have implications for designing targeted educational programs that address the distinct needs of healthcare professionals at varying career stages. Limitations and future research directions are also discussed.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"49 1","pages":"6"},"PeriodicalIF":3.5,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11729101/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142978633","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Future of Parenthood? Examining the Promise and Complexity of Pregnancy Robots in Reproductive Health.","authors":"Rajmohan Seetharaman","doi":"10.1007/s10916-025-02147-6","DOIUrl":"https://doi.org/10.1007/s10916-025-02147-6","url":null,"abstract":"<p><p>Advancements in reproductive technology are now approaching an unprecedented frontier: the pregnancy robot, a potential artificial womb capable of carrying a fetus from fertilization to birth. This innovation, by simulating the natural uterine environment, could redefine pregnancy and parenthood, offering transformative benefits for maternal and infant health. The pregnancy robot promises safer pathways for individuals with medical risks, LGBTQ + couples, and single parents, while also reducing the risks of complications like preeclampsia and preterm birth. It builds on the foundations laid by assisted reproductive technologies (ART), aiming to make the process more accessible and inclusive. However, the introduction of such technology brings with it complex ethical and social questions, including potential impacts on maternal-child bonding, gender roles, and societal norms surrounding motherhood. Further concerns revolve around equitable access, as socioeconomic divides may restrict this technology to those with financial resources, and the risk of cultural and religious opposition. Legal, regulatory, and environmental considerations must also be addressed to responsibly integrate this technology. This article discusses the profound promise and ethical challenges of pregnancy robots, highlighting the need for thoughtful implementation and collaborative dialogue to ensure that the technology, if realized, can benefit all facets of society and pave the way for a more inclusive future in reproductive health.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"49 1","pages":"4"},"PeriodicalIF":3.5,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142971231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Contribution of Video Directly Observed Therapy (VDOT) to Tuberculosis Treatment in Brazil.","authors":"Luana Michelly Aparecida Costa Dos Santos, Alan Maicon de Oliveira, Guilherme José Aguilar, Luiz Ricardo Albano Dos Santos, Wilbert Dener Lemos Costa, Dantony de Castro Barros Donato, Marco Andrey Cipriani Frade, Valdes Roberto Bollela","doi":"10.1007/s10916-025-02145-8","DOIUrl":"https://doi.org/10.1007/s10916-025-02145-8","url":null,"abstract":"<p><p>Directly Observed Treatment (DOT) involves the administration of medication and in-person monitoring by a healthcare team alongside the patient to improve adherence to tuberculosis (TB) treatment. Implementing DOT requires healthcare professionals, transportation, time dedication, and presence at the patient's residence. Meeting all these conditions is not always possible to address the high demand of TB patients who would benefit from this supervision. Video Directly Observed Therapy (VDOT) is a telecare platform created to remotely oversee the treatment of TB patients, allowing them to daily record medication intake, as prescribed, using their cell phones, clarify doubts, and facilitate communication with the healthcare team providing support. The medication intake data is recorded and accessible to healthcare professionals who monitor adherence remotely. The investigation reported in this work was conducted in Ribeirão Preto, Brazil, from May 5, 2020, to May 4, 2023, across five healthcare units. During the study period, 26,253 medication intake videos were collected and sent from 259 patients. Regarding the outcome reported by supervisors, only 9.7% of patients had their VDOT follow-up terminated due to abandonment, while the Cure/Treatment completion rate was 57.5%. Regarding adherence to the established treatment period for TB and for each patient in their specific situation, 67% of patients completed more than 66% of the treatment. The data suggested good acceptance of the system by both patients and healthcare professionals. VDOT may be a promising tool that complements TB DOT, extending its reach to improve adherence and reduce abandonment rates.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"49 1","pages":"3"},"PeriodicalIF":3.5,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142950078","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Symbolic AI Approach to Medical Training.","authors":"Alessio Bottrighi, Federica Grosso, Marco Ghiglione, Antonio Maconi, Stefano Nera, Luca Piovesan, Erica Raina, Annalisa Roveta, Paolo Terenziani","doi":"10.1007/s10916-024-02139-y","DOIUrl":"10.1007/s10916-024-02139-y","url":null,"abstract":"<p><p>In traditional medical education, learners are mostly trained to diagnose and treat patients through supervised practice. Artificial Intelligence and simulation techniques can complement such an educational practice. In this paper, we present GLARE-Edu, an innovative system in which AI knowledge-based methodologies and simulation are exploited to train learners \"how to act\" on patients based on the evidence-based best practices provided by clinical practice guidelines. GLARE-Edu is being developed by a multi-disciplinary team involving physicians and AI experts, within the AI-LEAP (LEArning Personalization of AI and with AI) Italian project. GLARE-Edu is domain-independent: it supports the acquisition of clinical guidelines and case studies in a computer format. Based on acquired guidelines (and case studies), it provides a series of educational facilities: (i) navigation, to navigate the structured representation of the guidelines provided by GLARE-Edu, (ii) automated simulation, to show learners how a guideline would suggest to act, step-by-step, on a specific case, and (iii) (self)verification, asking learners how they would treat a case, and comparing step-by-step the learner's proposal with the suggestions of the proper guideline. In this paper, we describe GLARE-Edu architecture and general features, and we demonstrate our approach through a concrete application to the melanoma guideline and we propose a preliminary evaluation.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"49 1","pages":"2"},"PeriodicalIF":3.5,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11717836/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142950075","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and Validation of a Neurosurgical Phantom for Simulating External Ventricular Drain Placement.","authors":"Jesse A M van Doormaal, Tim Fick, Ernest Boskovic, Eelco W Hoving, Pierre A J T Robe, Tristan P C van Doormaal","doi":"10.1007/s10916-024-02133-4","DOIUrl":"10.1007/s10916-024-02133-4","url":null,"abstract":"<p><p>This study aimed to develop and validate a cost-effective, customizable patient-specific phantom for simulating external ventricular drain placement, combining image segmentation, 3-D printing and molding techniques. Two variations of the phantom were created based on patient MRI data, integrating a realistic skin layer with anatomical landmarks, a 3-D printed skull, an agarose polysaccharide gel brain, and a ventricular cavity. To validate the phantom, 15 neurosurgeons, residents, and physician assistants performed 30 EVD placements. The effectiveness of the phantom as a training tool was assessed through a standardized user experience questionnaire, which evaluated the physical attributes, realism, and overall satisfaction. The mechanical properties of the phantom brain were quantified by measuring catheter insertion forces using a linear force tester to compare them to those experienced in real brain tissue. The study participants successfully completed EVD placements with a 76.7% optimal placement rate, which aligns with rates observed in clinical practice. Feedback highlighted the anatomical accuracy of the phantom and its value in enhancing surgical skills, though it also identified areas for improvement, particularly in the realism of the skin layer. Mechanical testing demonstrated that the insertion forces required were comparable to those encountered in actual brain tissue. The developed phantom offers a realistic, low-cost, and adaptable model for EVD simulation. This tool is particularly beneficial for both training and research, with future enhancements planned to improve the realism of the skin and incorporate more anatomical features to increase the fidelity of the simulation.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"49 1","pages":"1"},"PeriodicalIF":3.5,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11698783/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142921918","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating of BERT-based and Large Language Mod for Suicide Detection, Prevention, and Risk Assessment: A Systematic Review.","authors":"Inbar Levkovich, Mahmud Omar","doi":"10.1007/s10916-024-02134-3","DOIUrl":"10.1007/s10916-024-02134-3","url":null,"abstract":"<p><p>Suicide constitutes a public health issue of major concern. Ongoing progress in the field of artificial intelligence, particularly in the domain of large language models, has played a significant role in the detection, risk assessment, and prevention of suicide. The purpose of this review was to explore the use of LLM tools in various aspects of suicide prevention. PubMed, Embase, Web of Science, Scopus, APA PsycNet, Cochrane Library, and IEEE Xplore-for studies published were systematically searched for articles published between January 1, 2018, until April 2024. The 29 reviewed studies utilized LLMs such as GPT, Llama, and BERT. We categorized the studies into three main tasks: detecting suicidal ideation or behaviors, assessing the risk of suicidal ideation, and preventing suicide by predicting attempts. Most of the studies demonstrated that these models are highly efficient, often outperforming mental health professionals in early detection and prediction capabilities. Large language models demonstrate significant potential for identifying and detecting suicidal behaviors and for saving lives. Nevertheless, ethical problems still need to be examined and cooperation with skilled professionals is essential.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"48 1","pages":"113"},"PeriodicalIF":3.5,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11685247/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142909798","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Applications and Future Prospects of Medical LLMs: A Survey Based on the M-KAT Conceptual Framework.","authors":"Ying Chang, Jian-Ming Yin, Jian-Min Li, Chang Liu, Ling-Yong Cao, Shu-Yuan Lin","doi":"10.1007/s10916-024-02132-5","DOIUrl":"10.1007/s10916-024-02132-5","url":null,"abstract":"<p><p>The success of large language models (LLMs) in general areas have sparked a wave of research into their applications in the medical field. However, enhancing the medical professionalism of these models remains a major challenge. This study proposed a novel model training theoretical framework, the M-KAT framework, which integrated domain-specific training methods for LLMs with the unique characteristics of the medical discipline. This framework aimed to improve the medical professionalism of the models from three perspectives: general knowledge acquisition, specialized skill development, and alignment with clinical thinking. This study summarized the outcomes of medical LLMs across four tasks: clinical diagnosis and treatment, medical question answering, medical research, and health management. Using the M-KAT framework, we analyzed the contribution to enhancement of professionalism of models through different training stages. At the same time, for some of the potential risks associated with medical LLMs, targeted solutions can be achieved through pre-training, SFT, and model alignment based on cultivated professional capabilities. Additionally, this study identified main directions for future research on medical LLMs: advancing professional evaluation datasets and metrics tailored to the needs of medical tasks, conducting in-depth studies on medical multimodal large language models (MLLMs) capable of integrating diverse data types, and exploring the forms of medical agents and multi-agent frameworks that can interact with real healthcare environments and support clinical decision-making. It is hoped that predictions of work can provide a reference for subsequent research.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"48 1","pages":"112"},"PeriodicalIF":3.5,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142894930","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Letter to the Editor: How Useful are Current Chatbots Regarding Urology Patient Information? Comparison of the Ten Most Popular Chatbots' Responses About Female Urinary Incontinence.","authors":"Lorenzo Novara, Alice Antonioni, Lorenzo Vacca, Eleonora Rosato, Riccardo Lombardo, Cosimo De Nunzio","doi":"10.1007/s10916-024-02131-6","DOIUrl":"10.1007/s10916-024-02131-6","url":null,"abstract":"","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"48 1","pages":"111"},"PeriodicalIF":3.5,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142828916","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Challenges of Establishing Assurance Labs for Health Artificial Intelligence (AI).","authors":"Jesse M Ehrenfeld, Keith F Woeltje","doi":"10.1007/s10916-024-02127-2","DOIUrl":"10.1007/s10916-024-02127-2","url":null,"abstract":"","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"48 1","pages":"110"},"PeriodicalIF":3.5,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142785921","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Garbage In, Garbage Out? Negative Impact of Physiological Waveform Artifacts in a Hospital Clinical Data Warehouse.","authors":"Frederick H Kuo, Mohamed A Rehman, Luis M Ahumada","doi":"10.1007/s10916-024-02128-1","DOIUrl":"10.1007/s10916-024-02128-1","url":null,"abstract":"<p><p>Hospitals around the world are deploying increasingly advanced systems to collect and store high-resolution physiological patient data for quality improvement and research. However, data accuracy, completeness, consistency, and contextual validity remain issues. This report highlights a data artifact known as waveform clipping in our hospital's physiological data capture system that went unnoticed for years, limiting data analysis and delaying several research projects. We aim to raise awareness in the medical informatics community about the importance of careful system setup, ongoing data validation, and close cooperation between clinicians and data scientists.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"48 1","pages":"109"},"PeriodicalIF":3.5,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142716444","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}