Ranks distinct AI gain and problem claims from the published record. Scores reward impact, independent source strength, scale, confidence, and recency.
This peer-reviewed review summarizes recent AI and radiomics work in systemic sclerosis-associated ILD, the leading cause of disease-related mortality in systemic sclerosis, where visual HRCT scoring is reader-dependent. It reports that deep-learning UIP probability and whole-chest quantitative biomarkers stratify FVC decline and survival, and that AI-derived HRCT parameters correlate with DLCO/TLC and support pattern classification across CTD-ILD subtypes.
Researchers conducted exploratory focus groups at a large academic health center in New York City with 17 patients with schizophrenia spectrum disorders, 7 caregivers, and 4 providers to discuss a hypothetical AI companion tool. Thematic analysis identified five themes about accessibility, uncertainty about role, trust, supplementing human care, and limiting use to low-risk support.
This narrative review examined how AI and complementary digital health tools are being used across pre-travel, peri-travel, and post-travel care, covering machine learning risk models, decision-support systems, large language models, wearables, telemedicine, and outbreak surveillance infrastructure.
In a 2026 prospective cohort study, 168 third-year medical students were grouped by voluntary use of an LLM-powered virtual standardized patient system for extracurricular history-taking practice versus routine instruction alone. After propensity score matching to 40 pairs, the LLM-VSP group scored higher on an end-of-term Objective Structured Clinical Examination with real standardized patients.
By August 2026, a Histopathology study re-examined fallopian tube tissue from 19 BRCA1/2 carriers who had undergone risk-reducing salpingo-oophorectomy around age 40. Using deeper sections cut at 150 μm intervals and a deep learning model to support STIC detection, the team found occult STIC or HGSC in all patients who later developed peritoneal HGSC despite having no STIC or HGSC at initial diagnosis.
Researchers prompted ChatGPT, DeepSeek, and Gemini with standardized musculoskeletal complaints for Lynchburg, VA and Trumbull, CT, and judged whether recommended physicians were currently practicing locally in the relevant specialty and whether phone numbers were correct. By the August 13, 2026 publication date, ChatGPT was appropriate in all 17 recommendations, while Gemini and DeepSeek were appropriate in 43% and 40% of recommendations respectively.
On August 13, 2026, a peer-reviewed article in Diagnosis reported results from a two-day workshop of 18 interdisciplinary experts from three countries who sought to identify knowledge gaps in applying AI in clinical settings, using medical imaging as the primary use case.
In a routine-care sample of 1790 patients with major depressive disorder, panic disorder, and social anxiety disorder, researchers built multimodal machine learning models from pretreatment clinical, sociodemographic, and genetic data to predict clinically meaningful improvement after internet-delivered cognitive behavioral therapy, testing them with nested cross-validation and temporal validation in 356 held-out patients as of the August 2026 publication.
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