Ranks distinct AI gain and problem claims from the published record. Scores reward impact, independent source strength, scale, confidence, and recency.
Between January 2024 and January 2026, a health district in Australia implemented a digital wound model of care combining an AI-enabled app with a virtual command centre across four hospitals and five community health centres. A post-implementation evaluation surveyed and interviewed 94 patients, 75 frontline clinicians, 9 senior wound nurses and a product manager, reviewing governance minutes to assess acceptability and perceived benefit.
Researchers developed and validated an M-protein screening model using routine laboratory indicators from 5217 participants across three Chinese hospitals. They compared eight machine learning algorithms and selected a logistic regression model incorporating sex, age, total protein, albumin, albumin/globulin ratio, and hemoglobin, achieving an AUC of 0.843 in training and 0.843, 0.801, and 0.800 in internal and two external validations with a five-tier risk stratification.
This January 2024 IEEE Access review surveys generative AI in healthcare, describing models including ChatGPT, DALL-E, Bard, and seven healthcare-customized LLMs such as Med-PaLM, BioGPT, and DeepHealth. It catalogs applications from medical imaging and drug discovery to personalized treatment, simulation and training, clinical trial optimization, and medical chatbots, and details four real-world scenarios employing GAI: visual snow syndrome diagnosis, molecular drug optimization, medical education, and dentistry.
A July 2026 review in Die Urologie describes artificial intelligence moving from research into everyday clinical practice and hospital care, with generative AI and large language models now used alongside established image-analysis tools for documentation, knowledge management, patient communication, and workflow optimization, plus AI-assisted radiological and pathological interpretation and risk stratification.
This narrative review from Chinese Medical Journal examines how AI in laboratory medicine has evolved from conventional machine learning on structured results to deep learning and large language models that handle unstructured clinical text. It compares four paradigms and reviews evidence across blood cell morphology, autoverification, infectious risk stratification, urinalysis interpretation, decision support, and report generation.
A Communications Medicine overview published October 10 2023 examines large language models as text-processing AI tools that gained wide attention after ChatGPT's November 2022 release, assessing their near-human ability to answer, summarize and translate and their emerging use in clinical practice, medical research and medical education.
A systematic review published 12 September 2026 searched three databases to 1 January 2025 and included 29 studies that directly compared machine learning CVD risk predictions with the Framingham Risk Score in healthy adults. Twenty-three studies reported improved predictive ability, often by adding sociodemographic predictors absent from FRS or costly diagnostics such as CT angiography.
This scoping review examined 23 studies published between January 2000 and September 2025 on AI in orthodontic diagnosis, treatment planning, appliance design, and teledentistry. It found AI-assisted systems can improve diagnostic precision and reduce clinical workload, and that remote monitoring platforms can cut in-person appointments while maintaining standards and improving compliance.
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