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OBJECTIVE: To evaluate the quality of recommendations provided by ChatGPT regarding inguinal hernia repair.
METHODS: ChatGPT was asked 5 questions about surgical management of inguinal hernias. The chat-bot was assigned the role of expert in herniology and requested to search only specialized medical databases and provide information about references and evidence. Herniology experts and surgeons (non-experts) rated the quality of recommendations generated by ChatGPT using 4-point scale (from 0 to 3 points). Statistical correlations were explored between participants\' ratings and their stance regarding artificial intelligence.
RESULTS: Experts scored the quality of ChatGPT responses lower than non-experts (2 (1-2) vs. 2 (2-3), p<0.001). The chat-bot failed to provide valid references and actual evidence, as well as falsified half of references. Respondents were optimistic about the future of neural networks for clinical decision-making support. Most of them were against restricting their use in healthcare.
CONCLUSIONS: We would not recommend non-specialized large language models as a single or primary source of information for clinical decision making or virtual searching assistant.
UNASSIGNED: Оценить качество рекомендаций языковой модели (ЯМ) ChatGPT по лечению паховой грыжи.
UNASSIGNED: ChatGPT было задано 5 вопросов о хирургическом лечении паховых грыж. Чат-боту отведена роль эксперта в области герниологии и предложено провести поиск только в специализированных медицинских базах данных, предоставив информацию об источниках и уровне их доказательности. Эксперты в области герниологии и общие хирурги (не эксперты) оценили качество рекомендаций, полученных с помощью ChatGPT, по 4-балльной шкале (от 0 до 3 баллов). Изучены статистические закономерности между оценками респондентов и их мнением относительно перспектив использования искусственного интеллекта.
UNASSIGNED: Качество ответов ChatGPT экспертами оценено ниже (2 [1—2] балла), чем не экспертами (2 [2—3]), (p<0,001). Чат-бот не справился с предоставлением достоверных ссылок на источники и указанием уровня доказательности, а также сфальсифицировал половину приведенных ссылок. Респонденты с оптимизмом смотрят на будущее нейросетей как инструмента принятия клинических решений; большинство из них выступают против ограничения их использования в здравоохранении.
UNASSIGNED: Основываясь на результатах данного исследования, в настоящее время нельзя рекомендовать применение неспециализированных ЯМ в качестве единственного или основного источника информации для принятия решения или виртуального помощника по поиску медицинской информации.

Objectives: This article describes how a home visit solution was developed in a co-design process between patients in treatment for severe spasticity, their caregivers and hospital nurses. The solution was developed using a participatory design approach and was based on the identified needs of the participants. Methods: We developed a home visit solution through an iterative process and a collective \'reflection-in-action\' approach with patients, caregivers and healthcare professionals. Results: The study revealed the complexities of establishing new routines around home visits. The solution included a new workflow for the nurses and a new route and appointment planning tool. Conclusion: Through a participatory design approach, the users developed a home visit solution that minimised disruption to patients\' daily lives and facilitated a dialogue between the nurses and the caregivers about the treatment and the patients\' spasticity, which helped to adjust the treatment in line with the patient´s needs.

BACKGROUND: With recent advancements in the treatment of chronic lymphocytic leukemia (CLL) and mantle cell lymphoma (MCL), healthcare specialists may face challenges making treatment and management decisions based on latest evidence for the optimal care of patients with these conditions. This study aimed to identify specific knowledge, skills, and confidence gaps impacting the treatment of CLL and MCL, to inform future educational activities.
METHODS: Hematologists and hemato-oncologists (HCPs, n = 224) from France (academic settings), Germany, and the United States (academic and community settings) responded to a 15-minute quantitative needs assessment survey that measured perceived knowledge, skills, and confidence levels regarding different aspects of treatment and management of CLL and MCL patients, as well as clinical case questions. Descriptive statistics (cross tabulations) and Chi-square tests were conducted.
RESULTS: Four areas of educational need were identified: (1) sub-optimal knowledge of treatment guidelines; (2) sub-optimal knowledge of molecular testing to inform CLL/MCL treatment decisions; (3) sub-optimal skills when making treatment decisions according to patient profile (co-morbidities, molecular testing results); and (4) challenges balancing the risk of toxicities with benefits of treatment. Over one-third of the respondents reported skill gaps when selecting suitable treatment options and prescribing therapies and reported a lack in confidence to initiate and manage treatment. Larger gaps in knowledge of guidelines and skills in patient assessment were identified in MCL, compared to CLL.
CONCLUSIONS: This study suggests the need for continuing medical education specifically to improve knowledge of treatment guidelines, and to assist clinicians in developing skills and confidence when faced with clinical decision-making scenarios of patients with specific comorbidities and/or molecular test results, for example, through case-based learning activities.

OBJECTIVE: Systematic reviews and medical guidelines are widely used in clinical practice. However, these are often not up-to-date and focussed on the average patient. We therefore aimed to evaluate a guideline add-on, TherapySelector (TS), which is based on monthly updated data of all available high-quality studies, classified in specific patient profiles.
METHODS: We evaluated the TS for the treatment of hepatitis C (HCV) in an international cohort of patients treated with direct-acting antivirals between 2015 and 2020. The primary outcome was the number of patients receiving one of the two preferred treatment options of the HCV TS, based on the highest level of evidence, cure rate, absence of ribavirin-associated adverse effects, and treatment duration.
RESULTS: We enrolled 567 patients. The number of patients treated with one of the two preferred treatment options according to the HCV TS ranged between 27% (2015) and 60% (2020; p < 0.001). Most of the patients received a regimen with a longer treatment-duration (up to 34%) and/or addition of ribavirin (up to 14%). The effect on the expected cure-rate was minimal (1-6% higher) when the first preferred TherapySelector option was given compared to the actual treatment.
CONCLUSIONS: Medical decision-making can be optimised by a guideline add-on; in HCV its use appears to minimise adverse effects and cost. The use of such an add-on might have a greater impact in diseases with suboptimal cure-rates, high costs or adverse effects, for which treatment options rely on specific patient characteristics.

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Artificial intelligence (AI) in the form of ChatGPT has rapidly attracted attention from physicians and medical educators. While it holds great promise for more routine medical tasks, may broaden one\'s differential diagnosis, and may be able to assist in the evaluation of images, such as radiographs and electrocardiograms, the technology is largely based on advanced algorithms akin to pattern recognition. One of the key questions raised in concert with these advances is: What does the growth of artificial intelligence mean for medical education, particularly the development of critical thinking and clinical reasoning? In this commentary, we will explore the elements of cognitive theory that underlie the ways in which physicians are taught to reason through a diagnostic case and compare hypothetico-deductive reasoning, often employing illness scripts, with inductive reasoning, which is based on a deeper understanding of mechanisms of health and disease. Issues of cognitive bias and their impact on diagnostic error will be examined. The constructs of routine and adaptive expertise will also be delineated. The application of artificial intelligence to diagnostic problem solving, along with concerns about racial and gender bias, will be delineated. Using several case examples, we will demonstrate the limitations of this technology and its potential pitfalls and outline the direction medical education may need to take in the years to come.

BACKGROUND: Guidelines must be interpreted comprehensively and correctly to standardize the clinical process. However, this process is challenging and requires interpreters to have a medical background and qualifications. In this study, the accuracy of ChatGPT3.5 in answering clinical questions related to the 2019 guidelines for severe acute pancreatitis was evaluated.
RESULTS: An observational study was conducted using the 2019 guidelines for severe acute pancreatitis. The study compared the accuracy of ChatGPT3.5 in English versus Chinese and found that it was more accurate in English (71%) than in Chinese (59%) (P value: 0.203). Additionally, the study assessed the accuracy of ChatGPT3.5 in answering short-answer questions versus true/false questions and found that it was more accurate in answering short-answer questions (76%) than in answering true/false questions (60%) (P value: 0.405).
CONCLUSIONS: For clinicians managing severe acute pancreatitis, ChatGPT3.5 may have potential value. However, it should not be relied upon excessively for clinical decision making.

This review aims to assess the effectiveness of AI-driven CDSSs on patient outcomes and clinical practices. A comprehensive search was conducted across PubMed, MEDLINE, and Scopus. Studies published from January 2018 to November 2023 were eligible for inclusion. Following title and abstract screening, full-text articles were assessed for methodological quality and adherence to inclusion criteria. Data extraction focused on study design, AI technologies employed, reported outcomes, and evidence of AI-CDSS impact on patient and clinical outcomes. Thematic analysis was conducted to synthesise findings and identify key themes regarding the effectiveness of AI-CDSS. The screening of the articles resulted in the selection of 26 articles that satisfied the inclusion criteria. The content analysis revealed four themes: early detection and disease diagnosis, enhanced decision-making, medication errors, and clinicians\' perspectives. AI-based CDSSs were found to improve clinical decision-making by providing patient-specific information and evidence-based recommendations. Using AI in CDSSs can potentially improve patient outcomes by enhancing diagnostic accuracy, optimising treatment selection, and reducing medical errors.