“Clients are asking therapists to assess their AI chats.”

“We exploit existing ICD-9 codes for prediction on admissions, which achieved an 89% F1.”

“A key takeaway from the discussion would highlight the importance of balancing AI's capabilities with human expertise and ethical considerations within the healthcare context. (This is a predicted quote based on the title)”

“Yet none of the releases are cleared as medical devices, approved for clinical use, or available for direct patient diagnosis—despite marketing language emphasising healthcare transformation.”

“These findings strongly support a human-in-the-loop (HITL) workflow in which the on-premise LLM serves as a collaborative tool, not a full replacement.”

“The challenge is no longer whether AI can help, but how tightly it needs to be built into research and clinical work to improve decisions around trials and treatment.”

“MedGemma 1.5, small multimodal model for real clinical data MedGemma […]”

“NVIDIA founder and CEO Jensen Huang told attendees… ‘a blueprint for what is possible in the future of drug discovery’”

“OpenAI and Anthropic have each launched healthcare-focused products over the last week.”

“It not only spots rare abnormalities but also recognizes its own uncertainty, making it a powerful support tool for clinicians.”

“OmadaSpark, an AI agent trained with robust clinical input that delivers real-time motivational interviewing and nutrition education.”

“A team of Stanford Medicine researchers have introduced SleepFM Clinical, a multimodal sleep foundation model that learns from clinical polysomnography and predicts long term disease risk from a single night of sleep.”

“OpenAI for Healthcare enables secure, enterprise-grade AI that supports HIPAA compliance—reducing administrative burden and supporting clinical workflows.”

“"ChatGPT Health is a dedicated experience that securely connects your health data and apps, with privacy protections and a physician-informed design."”

“…the first drug generated entirely by generative artificial intelligence to reach mid-stage human clinical trials, and the first to target a novel AI-discovered biological pathway”

“Model-derived metrics such as arterial stiffness, pulse wave velocity, resistance, and compliance were found to align with clinical indicators of disease severity and progression.”

“MAMAMemeia improves upon the current state-of-the-art by 7.55% in macro-F1 and is established as the new benchmark compared to over 30 methods.”

“The proposed system consistently outperformed flat multi-class classifiers and pre-trained self-supervised models.”

“CPR achieves an F1 score of 0.632 under SAP attacks, surpassing Median Smoothing (0.541 F1) by 9.1%.”

“GPT-4 excels in clarity (4.88/5) but exhibits lower scores for natural writing flow (2.81/5), indicating that current systems achieve clinical accuracy but remain stylistically distinguishable from radiologist-authored text.”

“The paper proposes 'weighted geometric mean' odds ratios and relative risks, and 'weighted mean' risk differences as transparent summary measures for ordinal outcomes.”

“The study demonstrates the feasibility of anatomically realistic $μ$FE simulations at this scale, with models containing over $8\times10^{8}$ DOFs.”

“The Dermatology Assessment Schema (DAS) is a novel expert-developed framework that systematically captures clinically meaningful dermatological features in a structured and standardized form.”

“Virtual-Eyes improves RAD-DINO slice-level AUC from 0.576 to 0.610 and patient-level AUC from 0.646 to 0.683 (mean pooling) and from 0.619 to 0.735 (max pooling), with improved calibration (Brier score 0.188 to 0.112).”

“Novice endoscopists exposed to EndoRare-generated cases achieved a 0.400 increase in recall and a 0.267 increase in precision.”

“PGMP framework outperforms state-of-the-art methods on unseen anatomy, setting new benchmarks in efficiency and diagnostic reliability.”

“BATISNet outperforms existing methods in tooth integrity segmentation, providing more reliable and detailed data support for practical clinical applications.”

“MedKGI improves dialogue efficiency by 30% on average while maintaining state-of-the-art accuracy.”

“GCA-ResUNet achieves Dice scores of 86.11% and 92.64% on Synapse and ACDC benchmarks, respectively, outperforming a range of representative CNN and Transformer-based methods.”

“The method achieved superior performance compared to state-of-the-art methods on BraTS2020, with average Dice scores of 87.55, 79.36, and 62.67 for WT, TC, and ET, respectively, across fifteen missing modality combinations.”

“sCTs achieved 99% structural similarity and a Frechet inception distance of 1.01 relative to real CTs. Skull segmentation attained an average Dice coefficient of 85% across seven cranial bones, and sutures achieved 80% Dice.”

“The Bayesian joint model consistently outperforms conventional two-stage approaches in terms of parameter estimation accuracy and predictive performance.”

“The model reaching 96.23% accuracy, 95.58% F1-score and 94.83% specificity.”

“PathFound integrates pathological visual foundation models, vision-language models, and reasoning models trained with reinforcement learning to perform proactive information acquisition and diagnosis refinement.”

“ClinDEF effectively exposes critical clinical reasoning gaps in state-of-the-art LLMs, offering a more nuanced and clinically meaningful evaluation paradigm.”

“ECG-RAMBA achieves a macro ROC-AUC ≈ 0.85 on the Chapman--Shaoxing dataset and attains PR-AUC = 0.708 for atrial fibrillation detection on the external CPSC-2021 dataset in zero-shot transfer.”

“MedGemma-4b-it model, fine-tuned using Low-Rank Adaptation (LoRA), demonstrated superior diagnostic capability by achieving a mean test accuracy of 80.37% compared to 69.58% for the untuned GPT-4.”

“The MoraNet preserved better structural details with lower RMSE and higher SSIM values at acceleration factor of 4, and meanwhile took ten-fold faster inference time.”

“The GNN-TF model outperforms state-of-the-art methods, delivering superior predictive accuracy for predicting future tobacco usage.”

“PathoSyn provides a mathematically principled pipeline for generating high-fidelity patient-specific synthetic datasets, facilitating the development of robust diagnostic algorithms in low-data regimes.”

“GRPO recovers in-distribution performance but degrades cross-dataset transferability.”

“Lung masking should be treated as a controllable spatial prior selected to match the backbone and clinical objective, rather than applied uniformly.”

“The system delivers interpretable, real-time predictions via Explainable AI (XAI) visualizations, supporting transparent clinical decision-making.”

“LENS outperforms strong baselines on standard NLP metrics and task-specific measures of symptom-severity accuracy.”

“The proposed framework achieves an overall accuracy of 89.72% and a macro-average F1-score of 85.46%. Notably, it attains an F1- score of 61.7% for the challenging N1 stage, demonstrating a substantial improvement over previous methods on the SleepEDF datasets.”

“Prompt engineering is not a one-size-fit-all solution.”

“SwinTF3D achieves competitive Dice and IoU scores across multiple organs, despite its compact architecture.”

“Lamps' superior robustness, transferability, and clinical potential when compared to 10 baseline models.”

“GPT-5-mini reaching a best average F1 of 72.4 across sentence-level and freetext segmentation.”

“USF-MAE outperformed the DenseNet-169 baseline on all evaluation metrics.”