Medicine & Health

Using 1,825,948 chemotherapy appointment records from the Dana-Farber Cancer Institute, this study builds a hierarchical machine learning pipeline that first predicts cancellations and then no-shows, achieving F1-scores of 0.76 and 0.82 and improving minority-class performance by 7–10 points over a single-stage multinomial baseline; it further uses semi-supervised learning to infer no-show reasons from short-notice cancellations (weighted F1 of 0.57 and 0.54) and applies counterfactual simulation to evaluate interventions, finding that standard reminders are less effective than previously reported while provider consistency and commitment-based scheduling can reduce cancellations and no-shows.

This work initialized and biased the HIDDEN GEM structure-guided generative virtual screening workflow with screening data from a focused DNA-encoded library against the 53BP1 tandem Tudor domain (UNCDEL003, 58,080 compounds), nominated 57 purchasable compounds from the roughly 37-billion-compound Enamine REAL Space, and validated 14 as active hits by TR-FRET displacement (3 with IC50 ≤50 µM and 11 with IC50 ≤100 µM), with the AI-nominated hits showing greater chemical diversity, improved drug-likeness, and off-the-shelf purchasability relative to the initial DEL hits.

This study developed a graph-based QSAR modeling pipeline integrating assay data preprocessing, fingerprint and molecular graph feature representations, and benchmarking of classical machine learning, graph neural networks, graph transformers, and their consensus ensembles, applied to predict Caspase-3/7 activation, mitochondrial membrane potential disruption, and FDA drug-induced liver injury, where Graphormer achieved the highest F1 of 0.79 and the full consensus model achieved the highest AUC of 0.69 on DILI prediction, surpassing the previous best model with AUC 0.63 and F1 0.65, and identified structural motifs associated with dual activation and cell-line-specific responses through fragment enrichment analysis.

A family nurse practitioner (FNP) program had 98 students use a generative AI persona prompt in Claude Sonnet to create a virtual patient on a video-conferencing platform, and after a 30-minute prebriefing, about 75 minutes of scenario work, and a 45-minute PEARLS-based debriefing, the most common one-word reaction was “helpful” (n = 83), 95% said the activity facilitated their learning about SDOH (n = 82), 78% chose the two highest comfort levels for screening for and addressing SDOH (average 4.1/5), the SDOH quiz average was 94% (n = 98), and 54% answered the question on specific strategies to address SDOH correctly.

This preprint benchmarks eight open-source thigh muscle MRI segmentation tools on the MyoSegmenTUM, AIPS and Sheffield base datasets plus derived pathological and augmented out-of-distribution sets, combining Dice, Jaccard, Hausdorff, boundary IoU and inter-slice Dice ratio with qualitative usability review, and finds that domain-specific U-Net models, especially MuscleMap, generally outperformed foundation-model and newer general-purpose approaches, that adding SAM variants usually degraded rather than improved segmentation quality, and that most tools showed reduced accuracy on pathological cases.

This systematic review searched PubMed and Embase (July 9, 2025) and included 23 studies to assess generative AI, mainly ChatGPT 3.5/4, in total hip and knee arthroplasty across patient communication and education (n=19), clinical documentation (n=2), and clinical decision support (n=2): blinded ratings found FAQ responses comparable to surgeon-written answers in accuracy, clarity, and completeness with better readability; consent documents showed better readability and completeness than surgeon versions; operative-report extraction reached 97.5%–100% agreement; decision support showed higher accuracy for surgical candidacy but low specificity for outcome prediction, alongside fabricated citations and limited patient trust.

The study compared disease-trajectory embeddings from Delphi-2M, a transformer trained only on the health records of about 400,000 UK Biobank participants, with genome-wide genetic correlations for 19 neurological and psychiatric disorders, finding moderate convergence across the 171 disorder pairs (Mantel r = 0.33, p < 1×10⁻⁴), with both measures keeping sixteen disorders closer to their own diagnostic category and making the same three exceptions — multiple sclerosis, migraine, and essential tremor sat closer on average to psychiatric disorders — so clinical trajectories and genetic architecture draw the same boundary and break it in the same places.

The study presents a sparsely supervised AI framework in which frozen ultrasound foundation model (USFM, ViT-B) embeddings of 768 dimensions per frame are passed to a lightweight bidirectional LSTM temporal head that outputs a 0–1 frame-quality score, trained with Gaussian soft labels that peak at expert-marked key frames and decay smoothly with frame distance (width σ = 5 frames); in subject-level five-fold cross-validation on 18 subjects and 323 ultrasound videos spanning nine controlled acquisition sweep types (about 45,500 frames), it selected a usable frame in 82.2% of sweeps containing key frames with a mean minimum distance of 3.07 frames, exceeding the strongest training-free baseline (USFM feature cosine similarity, 49.2%) and a hard-label model (71.9%).

This study deployed the 3.8B-parameter open-weight small language model Phi4-mini (via Ollama) within an institutional firewall, combined with deterministic regular expression post-processing, to extract six uncoded urinary tract infection clinical features (fever, nitrites, leukocytes, lumbar pain, abdominal pain, and haematuria) from 15,498 Catalan/Spanish bilingual MEAP primary care narratives in the SIDIAP database in Catalonia, achieving 93.8% accuracy, 96.6% specificity, and 83.6% sensitivity in a double-blind clinician gold standard validation of 60 real patient records, and 87.2% accuracy, 99.5% specificity, and 99.2% positive predictive value in adversarial synthetic stress-testing of 720 notes, with no patient data leaving institutional servers.

Using GISTIC copy number from the TCGA PanCancer Atlas to classify tumors as PTEN intact, hemizygous, or homozygous deletion, this study scored a five-gene glutaminolysis signature (GLS, SLC1A5, GOT1, GLUD1, GPT2) against loss severity across fourteen tumor types and found that the signature decreased with PTEN loss in all fourteen (significantly in twelve) but was not MYC-mediated; instead the decline tracked chromosomal position, since GLUD1 and GOT1 flank PTEN on 10q and thirty-seven neighboring genes carrying no glutaminolysis annotation tracked PTEN copy number just as closely (mean rho 0.843 versus 0.842), with co-deletion fidelity falling monotonically with distance from PTEN (rho = -0.