UNASSIGNED: Adverse drug reactions (ADRs) are a significant healthcare concern. They are often documented as free text in electronic health records (EHRs), making them challenging to use in clinical decision support systems (CDSS). The study aimed to develop a text mining algorithm to identify ADRs in free text of Dutch EHRs.
UNASSIGNED: In Phase I, our previously developed CDSS algorithm was recoded and improved upon with the same relatively large dataset of 35 000 notes (Step A), using R to identify possible ADRs with Medical Dictionary for Regulatory Activities (MedDRA) terms and the related Systematized Nomenclature of Medicine Clinical Terms (SNOMED-CT) (Step B). In Phase II, 6 existing text-mining R-scripts were used to detect and present unique ADRs, and positive predictive value (PPV) and sensitivity were observed.
UNASSIGNED: In Phase IA, the recoded algorithm performed better than the previously developed CDSS algorithm, resulting in a PPV of 13% and a sensitivity of 93%. For The sensitivity for serious ADRs was 95%. The algorithm identified 58 additional possible ADRs. In Phase IB, the algorithm achieved a PPV of 10%, a sensitivity of 86%, and an F-measure of 0.18. In Phase II, four R-scripts enhanced the sensitivity and PPV of the algorithm, resulting in a PPV of 70%, a sensitivity of 73%, an F-measure of 0.71, and a 63% sensitivity for serious ADRs.
UNASSIGNED: The recoded Dutch algorithm effectively identifies ADRs from free-text Dutch EHRs using R-scripts and MedDRA/SNOMED-CT. The study details its limitations, highlighting the algorithm\'s potential and significant improvements.

UNASSIGNED: In the past few years, the use of artificial intelligence in healthcare has grown exponentially. Prescription of antibiotics is not exempt from its rapid diffusion, and various machine learning (ML) techniques, from logistic regression to deep neural networks and large language models, have been explored in the literature to support decisions regarding antibiotic prescription.
UNASSIGNED: In this narrative review, we discuss promises and challenges of the application of ML-based clinical decision support systems (ML-CDSSs) for antibiotic prescription. A search was conducted in PubMed up to April 2024.
UNASSIGNED: Prescribing antibiotics is a complex process involving various dynamic phases. In each of these phases, the support of ML-CDSSs has shown the potential, and also the actual ability in some studies, to favorably impacting relevant clinical outcomes. Nonetheless, before widely exploiting this massive potential, there are still crucial challenges ahead that are being intensively investigated, pertaining to the transparency of training data, the definition of the sufficient degree of prediction explanations when predictions are obtained through black box models, and the legal and ethical framework for decision responsibility whenever an antibiotic prescription is supported by ML-CDSSs.

UNASSIGNED: In recent years, the implementation of artificial intelligence (AI) in health care is progressively transforming medical fields, with the use of clinical decision support systems (CDSSs) as a notable application. Laboratory tests are vital for accurate diagnoses, but their increasing reliance presents challenges. The need for effective strategies for managing laboratory test interpretation is evident from the millions of monthly searches on test results\' significance. As the potential role of CDSSs in laboratory diagnostics gains significance, however, more research is needed to explore this area.
UNASSIGNED: The primary objective of our study was to assess the accuracy and safety of LabTest Checker (LTC), a CDSS designed to support medical diagnoses by analyzing both laboratory test results and patients\' medical histories.
UNASSIGNED: This cohort study embraced a prospective data collection approach. A total of 101 patients aged ≥18 years, in stable condition, and requiring comprehensive diagnosis were enrolled. A panel of blood laboratory tests was conducted for each participant. Participants used LTC for test result interpretation. The accuracy and safety of the tool were assessed by comparing AI-generated suggestions to experienced doctor (consultant) recommendations, which are considered the gold standard.
UNASSIGNED: The system achieved a 74.3% accuracy and 100% sensitivity for emergency safety and 92.3% sensitivity for urgent cases. It potentially reduced unnecessary medical visits by 41.6% (42/101) and achieved an 82.9% accuracy in identifying underlying pathologies.
UNASSIGNED: This study underscores the transformative potential of AI-based CDSSs in laboratory diagnostics, contributing to enhanced patient care, efficient health care systems, and improved medical outcomes. LTC\'s performance evaluation highlights the advancements in AI\'s role in laboratory medicine.

Digital health, an emerging scientific domain, attracts increasing attention as artificial intelligence and relevant software proliferate. Pharmacogenomics (PGx) is a core component of precision/personalized medicine driven by the overarching motto \"the right drug, for the right patient, at the right dose, and the right time.\" PGx takes into consideration patients\' genomic variations influencing drug efficacy and side effects. Despite its potentials for individually tailored therapeutics and improved clinical outcomes, adoption of PGx in clinical practice remains slow. We suggest that e-health tools such as clinical decision support systems (CDSSs) can help accelerate the PGx, precision/personalized medicine, and digital health emergence in everyday clinical practice worldwide. Herein, we present a systematic review that examines and maps the PGx-CDSSs used in clinical practice, including their salient features in both technical and clinical dimensions. Using Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines and research of the literature, 29 relevant journal articles were included in total, and 19 PGx-CDSSs were identified. In addition, we observed 10 technical components developed mostly as part of research initiatives, 7 of which could potentially facilitate future PGx-CDSSs implementation worldwide. Most of these initiatives are deployed in the United States, indicating a noticeable lack of, and the veritable need for, similar efforts globally, including Europe.

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.

Artificial intelligence (AI) and machine learning (ML) research within medicine has been exponentially increasing over the last decade, with studies showcasing the potential of AI/ML algorithms to improve clinical practice and outcomes. Ongoing research and efforts to develop AI-based models have expanded to aid in the identification of inborn errors of immunity (IEI). The utilization of larger electronic health record (EHR) datasets, coupled with advances in phenotyping precision and enhancements in ML techniques, has the potential to significantly improve the early recognition of IEI, thereby increasing access to equitable care. In this review, we provide a comprehensive examination of AI/ML for IEI, covering the spectrum from data preprocessing for AI/ML analysis to current applications within immunology, and address the challenges associated with implementing clinical decision support systems (CDSS) to refine the diagnosis and management of IEI.

Emergency Medicine Informatics (EMI) is a rapidly advancing field that utilizes information technology to enhance the delivery of emergency medical services. This comprehensive literature review explores the key components, benefits, challenges, and future directions of EMI. By integrating Electronic Health Records, Clinical Decision Support Systems, telemedicine, data analytics, interoperability, and patient monitoring systems, EMI has the potential to significantly improve patient outcomes and operational efficiency in emergency departments. However, the implementation of these technologies faces several obstacles, including interoperability issues, data security concerns, usability challenges, and high costs. This review highlights how these technologies are transforming emergency care, discusses the barriers to their implementation, and provides perspectives on potential solutions and future progress in the field.

暂无摘要。

Background: Open-source artificial intelligence models (OSAIMs) are increasingly being applied in various fields, including IT and medicine, offering promising solutions for diagnostic and therapeutic interventions. In response to the growing interest in AI for clinical diagnostics, we evaluated several OSAIMs-such as ChatGPT 4, Microsoft Copilot, Gemini, PopAi, You Chat, Claude, and the specialized PMC-LLaMA 13B-assessing their abilities to classify scoliosis severity and recommend treatments based on radiological descriptions from AP radiographs. Methods: Our study employed a two-stage methodology, where descriptions of single-curve scoliosis were analyzed by AI models following their evaluation by two independent neurosurgeons. Statistical analysis involved the Shapiro-Wilk test for normality, with non-normal distributions described using medians and interquartile ranges. Inter-rater reliability was assessed using Fleiss\' kappa, and performance metrics, like accuracy, sensitivity, specificity, and F1 scores, were used to evaluate the AI systems\' classification accuracy. Results: The analysis indicated that although some AI systems, like ChatGPT 4, Copilot, and PopAi, accurately reflected the recommended Cobb angle ranges for disease severity and treatment, others, such as Gemini and Claude, required further calibration. Particularly, PMC-LLaMA 13B expanded the classification range for moderate scoliosis, potentially influencing clinical decisions and delaying interventions. Conclusions: These findings highlight the need for the continuous refinement of AI models to enhance their clinical applicability.

Aim: Evaluation of the performance of ChatGPT-4.0 in providing prediagnosis and treatment plans for cardiac clinical cases by expert cardiologists. Methods: 20 cardiology clinical cases developed by experienced cardiologists were divided into two groups according to preparation methods. Cases were reviewed and analyzed by the ChatGPT-4.0 program, and analyses of ChatGPT were then sent to cardiologists. Eighteen expert cardiologists evaluated the quality of ChatGPT-4.0 responses using Likert and Global quality scales. Results: Physicians rated case difficulty (median 2.00), revealing high ChatGPT-4.0 agreement to differential diagnoses (median 5.00). Management plans received a median score of 4, indicating good quality. Regardless of the difficulty of the cases, ChatGPT-4.0 showed similar performance in differential diagnosis (p: 0.256) and treatment plans (p: 0.951). Conclusion: ChatGPT-4.0 excels at delivering accurate management and demonstrates its potential as a valuable clinical decision support tool in cardiology.
Have you ever wondered if an artificial intelligence (AI) program could help doctors figure out what the problem is when someone has heart complaints? Our research examined this by testing an AI program called ChatGPT-4.0 on clinical cases. We wanted to see if it could help doctors by giving good advice on what might be wrong with patients who have heart issues and what should be done to help them. To test this, we used ChatGPT-4.0 to look at 20 different stories about patients with heart problems. These stories were made to cover a variety of common heart conditions faced by heart doctors. Then, we asked 18 heart doctors to check if the advice from ChatGPT-4.0 was good and made sense. What we found was quite interesting! Most of the time, the doctors agreed that the computer gave good advice on what might be wrong with the patients and how to help them. This means that this smart computer program could be a helpful tool for doctors, especially when they are trying to figure out tricky heart problems. But, it\'s important to say that computers like ChatGPT-4.0 are not ready to replace doctors. They are tools that can offer suggestions. Doctors still need to use their knowledge and experience to make the final call on what\'s best for their patients. In simple terms, our study shows that with more development and testing, AI like ChatGPT-4.0 could be a helpful assistant to doctors in treating heart disease, making sure patients get the best care possible.