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Machine learning (ML) is increasingly employed to diagnose medical conditions, with algorithms trained to assign a single label using a black-box approach. We created an ML approach using deep learning that generates outcomes that are transparent and in line with clinical, diagnostic rules. We demonstrate our approach for autism spectrum disorders (ASD), a neurodevelopmental condition with increasing prevalence.

This study aims to facilitate the creation of quality standardized nursing statements in South Korea's hospitals using algorithmic generation based on the International Classifications of Nursing Practice (ICNP) and evaluation through Large Language Models.

Blockchain has emerged as a potential data-sharing structure in healthcare because of its decentralization, immutability, and traceability. However, its use in the biomedical domain is yet to be investigated comprehensively, especially from the aspects of implementation and evaluation, by existing blockchain literature reviews. To address this, our review assesses blockchain applications implemented in practice and evaluated with quantitative metrics.

We present a proof-of-concept digital scribe system as an Emergency Department (ED) consultation call-based clinical conversation summarization pipeline to support clinical documentation, and report its performance.

 While clinical practice guidelines recommend that oncologists discuss goals of care with patients who have advanced cancer, it is estimated that less than 20% of individuals admitted to the hospital with high-risk cancers have end-of-life discussions with their providers. While there has been interest in developing models for mortality prediction to trigger such discussions, few studies have compared how such models compare with clinical judgment to determine a patient's mortality [...]

 Predicting 30-day hospital readmissions is crucial for improving patient outcomes, optimizing resource allocation, and achieving financial savings. Existing studies reporting the development of machine learning (ML) models predictive of neurosurgical readmissions do not report factors related to clinical implementation.

 Clinical informatics (CI) has reshaped how medical information is shared, evaluated, and utilized in health care delivery. The widespread integration of electronic health records (EHRs) mandates proficiency among physicians and practitioners, yet medical trainees face a scarcity of opportunities for CI education.

 Despite the evidence suggesting the potential of electronic prescribing (e-prescribing), this system also faces challenges that can lead to inefficiency and even failure. This study aimed to evaluate physicians' perspectives on the efficiency, effectiveness, opportunities, and challenges associated with the e-prescribing system.