Medicine & Health

467 items

Stroke Sep 17, 2026
This review synthesizes the current literature on explainable and fair artificial intelligence in acute stroke management, noting that AI already performs strongly on tasks such as large vessel occlusion detection, Alberta Stroke Program Early Computed Tomography Score scoring, and functional outcome prediction, while post hoc explainability methods remain approximate and rarely formally tested, fairness evaluation remains uncommon due to limited demographic metadata, regulatory constraints, and the absence of stroke-specific fairness criteria, explainability and fairness remain largely disconnected, and generalizability is affected by dataset partitioning and reporting practices, leading to a call for unified evaluation frameworks that jointly assess explainability, fairness, and generali
This review synthesizes the current literature on explainable and fair artificial intelligence in acute stroke management, noting that AI already performs strongly on tasks such as large vessel occlusion detection, Alberta Stroke Program Early Computed Tomography Score scoring, and functional outcome prediction, while post hoc explainability methods remain approximate and rarely formally tested, fairness evaluation remains uncommon due to limited demographic metadata, regulatory constraints, and the absence of stroke-specific fairness criteria, explainability and fairness remain largely disconnected, and generalizability is affected by dataset partitioning and reporting practices, leading to a call for unified evaluation frameworks that jointly assess explainability, fairness, and generali
Responsible AI for Acute Stroke Management: A Review of Explainability and Fairness
This review synthesizes the current literature on explainable and fair artificial intelligence in acute stroke management, noting that AI already performs strongly on tasks such as large vessel occlusion detection, Alberta Stroke Program Early Computed Tomography Score scoring, and functional outcome prediction, while post hoc explainability methods remain approximate and rarely formally tested, fairness evaluation remains uncommon due to limited demographic metadata, regulatory constraints, and the absence of stroke-specific fairness criteria, explainability and fairness remain largely disconnected, and generalizability is affected by dataset partitioning and reporting practices, leading to a call for unified evaluation frameworks that jointly assess explainability, fairness, and generali
This review synthesizes the current literature on explainable and fair artificial intelligence in acute stroke management, noting that AI already performs strongly on tasks such as large vessel occlusion detection, Alberta Stroke Program Early Computed Tomography Score scoring, and functional outcome prediction, while post hoc explainability methods remain approximate and rarely formally tested, fairness evaluation remains uncommon due to limited demographic metadata, regulatory constraints, and the absence of stroke-specific fairness criteria, explainability and fairness remain largely disconnected, and generalizability is affected by dataset partitioning and reporting practices, leading to a call for unified evaluation frameworks that jointly assess explainability, fairness, and generali
Journal of Agricultural and Food Chemistry Sep 17, 2026
This review surveys the diverse sources and novel preparation strategies of food-derived antihypertensive peptides, examines their multitarget mechanisms and structure-activity relationships, and summarizes how machine learning supports precise identification and activity prediction, arguing that future work should integrate advanced biotechnologies and intelligent platforms to accelerate the transition from laboratory to clinical application.
This review surveys the diverse sources and novel preparation strategies of food-derived antihypertensive peptides, examines their multitarget mechanisms and structure-activity relationships, and summarizes how machine learning supports precise identification and activity prediction, arguing that future work should integrate advanced biotechnologies and intelligent platforms to accelerate the transition from laboratory to clinical application.
Food-Derived Antihypertensive Peptides: A Review of Preparation Strategies, Multitarget Mechanisms, and Machine Learning Advances
This review surveys the diverse sources and novel preparation strategies of food-derived antihypertensive peptides, examines their multitarget mechanisms and structure-activity relationships, and summarizes how machine learning supports precise identification and activity prediction, arguing that future work should integrate advanced biotechnologies and intelligent platforms to accelerate the transition from laboratory to clinical application.
This review surveys the diverse sources and novel preparation strategies of food-derived antihypertensive peptides, examines their multitarget mechanisms and structure-activity relationships, and summarizes how machine learning supports precise identification and activity prediction, arguing that future work should integrate advanced biotechnologies and intelligent platforms to accelerate the transition from laboratory to clinical application.
arXiv Sep 17, 2026
This work presents EarStreAM, a closed-loop earable system built on OpenEarable 2.0 and a companion smartphone app that continuously monitors in-ear PPG to derive heart rate and heart rate variability as stress proxies, triggers an LLM-generated personalized guided meditation that adapts in real time to the user's physiological state, and terminates it once stress returns to baseline, demonstrated in two modes: a biosignal-adaptive mode with optional stress induction and a meditation-only mode.
This work presents EarStreAM, a closed-loop earable system built on OpenEarable 2.0 and a companion smartphone app that continuously monitors in-ear PPG to derive heart rate and heart rate variability as stress proxies, triggers an LLM-generated personalized guided meditation that adapts in real time to the user's physiological state, and terminates it once stress returns to baseline, demonstrated in two modes: a biosignal-adaptive mode with optional stress induction and a meditation-only mode.
EarStreAM: A Closed-Loop Earable System for Personalized Stress-Adaptive Meditation
This work presents EarStreAM, a closed-loop earable system built on OpenEarable 2.0 and a companion smartphone app that continuously monitors in-ear PPG to derive heart rate and heart rate variability as stress proxies, triggers an LLM-generated personalized guided meditation that adapts in real time to the user's physiological state, and terminates it once stress returns to baseline, demonstrated in two modes: a biosignal-adaptive mode with optional stress induction and a meditation-only mode.
This work presents EarStreAM, a closed-loop earable system built on OpenEarable 2.0 and a companion smartphone app that continuously monitors in-ear PPG to derive heart rate and heart rate variability as stress proxies, triggers an LLM-generated personalized guided meditation that adapts in real time to the user's physiological state, and terminates it once stress returns to baseline, demonstrated in two modes: a biosignal-adaptive mode with optional stress induction and a meditation-only mode.
arXiv Sep 17, 2026
The study presents Andromeda 2, an agentic system that reasons over structured in-house experimental evidence and invokes computational and experimental tools, achieving a 50% high-AUC hit rate for paclitaxel self-emulsifying drug delivery system (SEDDS) development at a matched budget of 96 formulations (versus 17% for Andromeda 1 and 2% for wet-lab DoE) and identifying 12 formulations meeting all four target product profile (TPP) objectives (versus 6 and 0), while an ablation showed that access to structured in-house evidence increased mean AUC by 34%.
The study presents Andromeda 2, an agentic system that reasons over structured in-house experimental evidence and invokes computational and experimental tools, achieving a 50% high-AUC hit rate for paclitaxel self-emulsifying drug delivery system (SEDDS) development at a matched budget of 96 formulations (versus 17% for Andromeda 1 and 2% for wet-lab DoE) and identifying 12 formulations meeting all four target product profile (TPP) objectives (versus 6 and 0), while an ablation showed that access to structured in-house evidence increased mean AUC by 34%.
Evidence-Grounded Agentic Formulation Development in an Autonomous Laboratory
The study presents Andromeda 2, an agentic system that reasons over structured in-house experimental evidence and invokes computational and experimental tools, achieving a 50% high-AUC hit rate for paclitaxel self-emulsifying drug delivery system (SEDDS) development at a matched budget of 96 formulations (versus 17% for Andromeda 1 and 2% for wet-lab DoE) and identifying 12 formulations meeting all four target product profile (TPP) objectives (versus 6 and 0), while an ablation showed that access to structured in-house evidence increased mean AUC by 34%.
The study presents Andromeda 2, an agentic system that reasons over structured in-house experimental evidence and invokes computational and experimental tools, achieving a 50% high-AUC hit rate for paclitaxel self-emulsifying drug delivery system (SEDDS) development at a matched budget of 96 formulations (versus 17% for Andromeda 1 and 2% for wet-lab DoE) and identifying 12 formulations meeting all four target product profile (TPP) objectives (versus 6 and 0), while an ablation showed that access to structured in-house evidence increased mean AUC by 34%.
arXiv Sep 17, 2026
This cross-sectional study surveyed 761 healthcare professionals across multiple disciplines and practice settings in Nigeria between December 2025 and March 2026 using a structured, validated questionnaire, finding high overall awareness of AI in healthcare (92.6%) alongside limited knowledge and preparedness (40.9% reporting low or very low knowledge; only 63.0% feeling adequately prepared), high willingness to adopt (92.5% interested in training; 78.7% supporting AI education in undergraduate curricula), key barriers of lack of training (84.7%), poor infrastructure (71.1%), high cost of AI tools (61.0%), fear of job displacement (60.6%), ethical concerns (52.9%) and data privacy concerns (52.
This cross-sectional study surveyed 761 healthcare professionals across multiple disciplines and practice settings in Nigeria between December 2025 and March 2026 using a structured, validated questionnaire, finding high overall awareness of AI in healthcare (92.6%) alongside limited knowledge and preparedness (40.9% reporting low or very low knowledge; only 63.0% feeling adequately prepared), high willingness to adopt (92.5% interested in training; 78.7% supporting AI education in undergraduate curricula), key barriers of lack of training (84.7%), poor infrastructure (71.1%), high cost of AI tools (61.0%), fear of job displacement (60.6%), ethical concerns (52.9%) and data privacy concerns (52.
Prepared Or Unprepared? Evaluating Healthcare Workforce Readiness for Clinical Adoption of Artificial Intelligence in Nigeria
This cross-sectional study surveyed 761 healthcare professionals across multiple disciplines and practice settings in Nigeria between December 2025 and March 2026 using a structured, validated questionnaire, finding high overall awareness of AI in healthcare (92.6%) alongside limited knowledge and preparedness (40.9% reporting low or very low knowledge; only 63.0% feeling adequately prepared), high willingness to adopt (92.5% interested in training; 78.7% supporting AI education in undergraduate curricula), key barriers of lack of training (84.7%), poor infrastructure (71.1%), high cost of AI tools (61.0%), fear of job displacement (60.6%), ethical concerns (52.9%) and data privacy concerns (52.
This cross-sectional study surveyed 761 healthcare professionals across multiple disciplines and practice settings in Nigeria between December 2025 and March 2026 using a structured, validated questionnaire, finding high overall awareness of AI in healthcare (92.6%) alongside limited knowledge and preparedness (40.9% reporting low or very low knowledge; only 63.0% feeling adequately prepared), high willingness to adopt (92.5% interested in training; 78.7% supporting AI education in undergraduate curricula), key barriers of lack of training (84.7%), poor infrastructure (71.1%), high cost of AI tools (61.0%), fear of job displacement (60.6%), ethical concerns (52.9%) and data privacy concerns (52.
arXiv Sep 17, 2026
This work represents dense health narratives as tuples linking atomic claims with thematic aspects, stance, and a six-dimensional pragmatic profile, builds a benchmark of 1,191 manually annotated claims from 60 videos across GLP-1 weight-loss medications, testosterone replacement therapy, collagen supplementation, and intermittent fasting, and evaluates large language models on structured discourse prediction under different discourse context settings.
This work represents dense health narratives as tuples linking atomic claims with thematic aspects, stance, and a six-dimensional pragmatic profile, builds a benchmark of 1,191 manually annotated claims from 60 videos across GLP-1 weight-loss medications, testosterone replacement therapy, collagen supplementation, and intermittent fasting, and evaluates large language models on structured discourse prediction under different discourse context settings.
Structured Claim-Level Discourse Representations for Dense Health Narratives
This work represents dense health narratives as tuples linking atomic claims with thematic aspects, stance, and a six-dimensional pragmatic profile, builds a benchmark of 1,191 manually annotated claims from 60 videos across GLP-1 weight-loss medications, testosterone replacement therapy, collagen supplementation, and intermittent fasting, and evaluates large language models on structured discourse prediction under different discourse context settings.
This work represents dense health narratives as tuples linking atomic claims with thematic aspects, stance, and a six-dimensional pragmatic profile, builds a benchmark of 1,191 manually annotated claims from 60 videos across GLP-1 weight-loss medications, testosterone replacement therapy, collagen supplementation, and intermittent fasting, and evaluates large language models on structured discourse prediction under different discourse context settings.
arXiv Sep 16, 2026
EviGen proposes a three-layer framework that first retrieves and ranks evidence from a patient's longitudinal EHR by predictive contribution rather than textual relevance using learnable queries trained on outcome labels, then has an LLM generate a citation-grounded clinical rationale within that evidence scaffold, and finally applies a process-supervised verifier to check each reasoning step and flag unreliable claims; across MIMIC-IV one-year mortality, autism, and ADHD tasks it improves prediction performance and rationale faithfulness over full-context and RAG baselines and is preferred by most reviewers in a clinical pilot.
EviGen proposes a three-layer framework that first retrieves and ranks evidence from a patient's longitudinal EHR by predictive contribution rather than textual relevance using learnable queries trained on outcome labels, then has an LLM generate a citation-grounded clinical rationale within that evidence scaffold, and finally applies a process-supervised verifier to check each reasoning step and flag unreliable claims; across MIMIC-IV one-year mortality, autism, and ADHD tasks it improves prediction performance and rationale faithfulness over full-context and RAG baselines and is preferred by most reviewers in a clinical pilot.
EviGen: Predictive Evidence Scaffolding for Verifiable Clinical Rationale Generation
EviGen proposes a three-layer framework that first retrieves and ranks evidence from a patient's longitudinal EHR by predictive contribution rather than textual relevance using learnable queries trained on outcome labels, then has an LLM generate a citation-grounded clinical rationale within that evidence scaffold, and finally applies a process-supervised verifier to check each reasoning step and flag unreliable claims; across MIMIC-IV one-year mortality, autism, and ADHD tasks it improves prediction performance and rationale faithfulness over full-context and RAG baselines and is preferred by most reviewers in a clinical pilot.
EviGen proposes a three-layer framework that first retrieves and ranks evidence from a patient's longitudinal EHR by predictive contribution rather than textual relevance using learnable queries trained on outcome labels, then has an LLM generate a citation-grounded clinical rationale within that evidence scaffold, and finally applies a process-supervised verifier to check each reasoning step and flag unreliable claims; across MIMIC-IV one-year mortality, autism, and ADHD tasks it improves prediction performance and rationale faithfulness over full-context and RAG baselines and is preferred by most reviewers in a clinical pilot.
medRxiv Sep 16, 2026
Using manually annotated cardiac catheterization reports from three hospitals within Yale New Haven Health (1,412 notes, 596 PCI reports) as a reference standard, this study evaluated three open-weight large language models (Llama 3.3 70B, Meditron-7B, BioMistral-7B) for identifying PCI reports and extracting six complex PCI features, finding that Llama 3.3 70B outperformed the two smaller domain-specific models on most tasks, achieving 100.0% sensitivity, 93.8% specificity, 96.4% accuracy and 95.9% F1 for PCI identification, and among 590 evaluable PCI reports 97.7% sensitivity, 80.1% specificity, 57.6% positive predictive value, 99.2% negative predictive value and 83.
Using manually annotated cardiac catheterization reports from three hospitals within Yale New Haven Health (1,412 notes, 596 PCI reports) as a reference standard, this study evaluated three open-weight large language models (Llama 3.3 70B, Meditron-7B, BioMistral-7B) for identifying PCI reports and extracting six complex PCI features, finding that Llama 3.3 70B outperformed the two smaller domain-specific models on most tasks, achieving 100.0% sensitivity, 93.8% specificity, 96.4% accuracy and 95.9% F1 for PCI identification, and among 590 evaluable PCI reports 97.7% sensitivity, 80.1% specificity, 57.6% positive predictive value, 99.2% negative predictive value and 83.
Automated Identification of Complex Percutaneous Coronary Intervention from Cardiac Catheterization Reports Using Large Language Models
Using manually annotated cardiac catheterization reports from three hospitals within Yale New Haven Health (1,412 notes, 596 PCI reports) as a reference standard, this study evaluated three open-weight large language models (Llama 3.3 70B, Meditron-7B, BioMistral-7B) for identifying PCI reports and extracting six complex PCI features, finding that Llama 3.3 70B outperformed the two smaller domain-specific models on most tasks, achieving 100.0% sensitivity, 93.8% specificity, 96.4% accuracy and 95.9% F1 for PCI identification, and among 590 evaluable PCI reports 97.7% sensitivity, 80.1% specificity, 57.6% positive predictive value, 99.2% negative predictive value and 83.
Using manually annotated cardiac catheterization reports from three hospitals within Yale New Haven Health (1,412 notes, 596 PCI reports) as a reference standard, this study evaluated three open-weight large language models (Llama 3.3 70B, Meditron-7B, BioMistral-7B) for identifying PCI reports and extracting six complex PCI features, finding that Llama 3.3 70B outperformed the two smaller domain-specific models on most tasks, achieving 100.0% sensitivity, 93.8% specificity, 96.4% accuracy and 95.9% F1 for PCI identification, and among 590 evaluable PCI reports 97.7% sensitivity, 80.1% specificity, 57.6% positive predictive value, 99.2% negative predictive value and 83.
Current oncology reports Sep 16, 2026
This review systematically examines current follow-up protocols and recent developments for nasopharyngeal carcinoma (NPC): on one hand it compares and analyzes major follow-up guidelines in terms of follow-up frequency, imaging modalities (MRI and PET), plasma EBV-DNA monitoring, and functional assessments; on the other hand it elaborates on the application prospects and research progress of genomics, radiomics, and artificial intelligence in NPC surveillance, noting that risk-stratified, individualized follow-up strategies such as those based on conditional survival models can enhance the cost-effectiveness of surveillance, while radiomics and artificial intelligence show promise for improving recurrence risk assessment, prognostic stratification, and individualized surveillance.
This review systematically examines current follow-up protocols and recent developments for nasopharyngeal carcinoma (NPC): on one hand it compares and analyzes major follow-up guidelines in terms of follow-up frequency, imaging modalities (MRI and PET), plasma EBV-DNA monitoring, and functional assessments; on the other hand it elaborates on the application prospects and research progress of genomics, radiomics, and artificial intelligence in NPC surveillance, noting that risk-stratified, individualized follow-up strategies such as those based on conditional survival models can enhance the cost-effectiveness of surveillance, while radiomics and artificial intelligence show promise for improving recurrence risk assessment, prognostic stratification, and individualized surveillance.
Recent Advances in Surveillance Strategies for Nasopharyngeal Carcinoma: From Guideline Follow-up to Individualized Precision Surveillance
This review systematically examines current follow-up protocols and recent developments for nasopharyngeal carcinoma (NPC): on one hand it compares and analyzes major follow-up guidelines in terms of follow-up frequency, imaging modalities (MRI and PET), plasma EBV-DNA monitoring, and functional assessments; on the other hand it elaborates on the application prospects and research progress of genomics, radiomics, and artificial intelligence in NPC surveillance, noting that risk-stratified, individualized follow-up strategies such as those based on conditional survival models can enhance the cost-effectiveness of surveillance, while radiomics and artificial intelligence show promise for improving recurrence risk assessment, prognostic stratification, and individualized surveillance.
This review systematically examines current follow-up protocols and recent developments for nasopharyngeal carcinoma (NPC): on one hand it compares and analyzes major follow-up guidelines in terms of follow-up frequency, imaging modalities (MRI and PET), plasma EBV-DNA monitoring, and functional assessments; on the other hand it elaborates on the application prospects and research progress of genomics, radiomics, and artificial intelligence in NPC surveillance, noting that risk-stratified, individualized follow-up strategies such as those based on conditional survival models can enhance the cost-effectiveness of surveillance, while radiomics and artificial intelligence show promise for improving recurrence risk assessment, prognostic stratification, and individualized surveillance.
medRxiv Sep 16, 2026
This study ablated question components across 792 Orthopaedic In-Training Examination (OITE) questions from 2020 through 2024 (434 with clinical images, 358 without), evaluating three open-source Ministral-3 models (3B, 8B, 14B) and five proprietary models (Claude Haiku-4.5, Sonnet-4.6, Opus-4.8, GPT-5.6 Luna, GPT-5.6 Terra), and found that pooled accuracy on image-containing questions was 59.13% (58.21-60.08) with complete information and 59.13% (58.18-60.11) without images, dropping to 49.05% (48.07-50.00) without the clinical vignette; non-image questions fell from 72.94% (72.10-73.85) with complete clinical context to 53.53% (52.41-54.68) without the vignette and 37.36% (36.28-38.48) with answer options alone; with options only, every model exceeded the 25% random baseline (highest 45.
This study ablated question components across 792 Orthopaedic In-Training Examination (OITE) questions from 2020 through 2024 (434 with clinical images, 358 without), evaluating three open-source Ministral-3 models (3B, 8B, 14B) and five proprietary models (Claude Haiku-4.5, Sonnet-4.6, Opus-4.8, GPT-5.6 Luna, GPT-5.6 Terra), and found that pooled accuracy on image-containing questions was 59.13% (58.21-60.08) with complete information and 59.13% (58.18-60.11) without images, dropping to 49.05% (48.07-50.00) without the clinical vignette; non-image questions fell from 72.94% (72.10-73.85) with complete clinical context to 53.53% (52.41-54.68) without the vignette and 37.36% (36.28-38.48) with answer options alone; with options only, every model exceeded the 25% random baseline (highest 45.
Do Large Language Models Use the Clinical Vignette? A Question Ablation Study on the Orthopaedic In-Training Examination
This study ablated question components across 792 Orthopaedic In-Training Examination (OITE) questions from 2020 through 2024 (434 with clinical images, 358 without), evaluating three open-source Ministral-3 models (3B, 8B, 14B) and five proprietary models (Claude Haiku-4.5, Sonnet-4.6, Opus-4.8, GPT-5.6 Luna, GPT-5.6 Terra), and found that pooled accuracy on image-containing questions was 59.13% (58.21-60.08) with complete information and 59.13% (58.18-60.11) without images, dropping to 49.05% (48.07-50.00) without the clinical vignette; non-image questions fell from 72.94% (72.10-73.85) with complete clinical context to 53.53% (52.41-54.68) without the vignette and 37.36% (36.28-38.48) with answer options alone; with options only, every model exceeded the 25% random baseline (highest 45.
This study ablated question components across 792 Orthopaedic In-Training Examination (OITE) questions from 2020 through 2024 (434 with clinical images, 358 without), evaluating three open-source Ministral-3 models (3B, 8B, 14B) and five proprietary models (Claude Haiku-4.5, Sonnet-4.6, Opus-4.8, GPT-5.6 Luna, GPT-5.6 Terra), and found that pooled accuracy on image-containing questions was 59.13% (58.21-60.08) with complete information and 59.13% (58.18-60.11) without images, dropping to 49.05% (48.07-50.00) without the clinical vignette; non-image questions fell from 72.94% (72.10-73.85) with complete clinical context to 53.53% (52.41-54.68) without the vignette and 37.36% (36.28-38.48) with answer options alone; with options only, every model exceeded the 25% random baseline (highest 45.

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