Medicine & Health

468 items

JMIR Formative Research Sep 16, 2026
This case report describes a woman in her mid-20s with generalized anxiety disorder and major depressive disorder and a history of strong social, academic, and occupational functioning who developed a pattern of functional dependence on ChatGPT, outsourcing routine cognitive and interpersonal tasks such as composing emails, interpreting social interactions, predicting the future, and making decisions, and becoming increasingly uncomfortable completing such tasks independently; the authors frame this as cognitive offloading, reduced confidence in independent judgment, and reinforcement of externalized thinking using the I-PACE model, and suggest that the unlimited accessibility of AI tools may intensify reassurance seeking and worsen tolerance of uncertainty.
This case report describes a woman in her mid-20s with generalized anxiety disorder and major depressive disorder and a history of strong social, academic, and occupational functioning who developed a pattern of functional dependence on ChatGPT, outsourcing routine cognitive and interpersonal tasks such as composing emails, interpreting social interactions, predicting the future, and making decisions, and becoming increasingly uncomfortable completing such tasks independently; the authors frame this as cognitive offloading, reduced confidence in independent judgment, and reinforcement of externalized thinking using the I-PACE model, and suggest that the unlimited accessibility of AI tools may intensify reassurance seeking and worsen tolerance of uncertainty.
Problematic Reliance on Generative AI in an Anxious Young Adult: A Case Report
This case report describes a woman in her mid-20s with generalized anxiety disorder and major depressive disorder and a history of strong social, academic, and occupational functioning who developed a pattern of functional dependence on ChatGPT, outsourcing routine cognitive and interpersonal tasks such as composing emails, interpreting social interactions, predicting the future, and making decisions, and becoming increasingly uncomfortable completing such tasks independently; the authors frame this as cognitive offloading, reduced confidence in independent judgment, and reinforcement of externalized thinking using the I-PACE model, and suggest that the unlimited accessibility of AI tools may intensify reassurance seeking and worsen tolerance of uncertainty.
This case report describes a woman in her mid-20s with generalized anxiety disorder and major depressive disorder and a history of strong social, academic, and occupational functioning who developed a pattern of functional dependence on ChatGPT, outsourcing routine cognitive and interpersonal tasks such as composing emails, interpreting social interactions, predicting the future, and making decisions, and becoming increasingly uncomfortable completing such tasks independently; the authors frame this as cognitive offloading, reduced confidence in independent judgment, and reinforcement of externalized thinking using the I-PACE model, and suggest that the unlimited accessibility of AI tools may intensify reassurance seeking and worsen tolerance of uncertainty.
JMIR Medical Informatics Sep 16, 2026
This systematic review searched PubMed, MEDLINE, Embase, PsycINFO, and Web of Science from inception to November 11, 2025, and included 29 studies that developed, validated, or evaluated multivariable prediction models using routinely collected electronic health record or administrative data to predict acute mental status deterioration during adult hospital admissions, all operationalized as delirium; using CHARMS and TRIPOD/TRIPOD-AI for data extraction and PROBAST for risk of bias, it found that the evidence clustered into four overlapping prediction tasks (admission or early-stay risk stratification, perioperative or postoperative prediction, dynamic intensive care unit prediction, and external validation or workflow evaluation of existing tools), that most studies were retrospective co
This systematic review searched PubMed, MEDLINE, Embase, PsycINFO, and Web of Science from inception to November 11, 2025, and included 29 studies that developed, validated, or evaluated multivariable prediction models using routinely collected electronic health record or administrative data to predict acute mental status deterioration during adult hospital admissions, all operationalized as delirium; using CHARMS and TRIPOD/TRIPOD-AI for data extraction and PROBAST for risk of bias, it found that the evidence clustered into four overlapping prediction tasks (admission or early-stay risk stratification, perioperative or postoperative prediction, dynamic intensive care unit prediction, and external validation or workflow evaluation of existing tools), that most studies were retrospective co
Prediction Models for In-Hospital Delirium Using Routinely Collected Electronic Health Record Data: Systematic Review
This systematic review searched PubMed, MEDLINE, Embase, PsycINFO, and Web of Science from inception to November 11, 2025, and included 29 studies that developed, validated, or evaluated multivariable prediction models using routinely collected electronic health record or administrative data to predict acute mental status deterioration during adult hospital admissions, all operationalized as delirium; using CHARMS and TRIPOD/TRIPOD-AI for data extraction and PROBAST for risk of bias, it found that the evidence clustered into four overlapping prediction tasks (admission or early-stay risk stratification, perioperative or postoperative prediction, dynamic intensive care unit prediction, and external validation or workflow evaluation of existing tools), that most studies were retrospective co
This systematic review searched PubMed, MEDLINE, Embase, PsycINFO, and Web of Science from inception to November 11, 2025, and included 29 studies that developed, validated, or evaluated multivariable prediction models using routinely collected electronic health record or administrative data to predict acute mental status deterioration during adult hospital admissions, all operationalized as delirium; using CHARMS and TRIPOD/TRIPOD-AI for data extraction and PROBAST for risk of bias, it found that the evidence clustered into four overlapping prediction tasks (admission or early-stay risk stratification, perioperative or postoperative prediction, dynamic intensive care unit prediction, and external validation or workflow evaluation of existing tools), that most studies were retrospective co
Diagnosis (Berlin, Germany) Sep 16, 2026
In Fall 2024, 175 second-year medical students completed three MAESSCR multi-agent LLM clinical encounters as coursework, and six clinician-educators rated 120 randomly sampled transcripts with a dichotomous tool, finding 92% (110/120) adherence to scripted details, 2.5% (3/120) diagnosis-changing information, 12% (14/120) unrealistic patient portrayal, and 17% (20/120) technical issues, while the most prominent problem was agents interpreting findings before students could, occurring in 28% (34/120) of encounters for history/physical exam agents and 59% (71/120) for diagnostics/management agents, with most disruptions judged minor.
In Fall 2024, 175 second-year medical students completed three MAESSCR multi-agent LLM clinical encounters as coursework, and six clinician-educators rated 120 randomly sampled transcripts with a dichotomous tool, finding 92% (110/120) adherence to scripted details, 2.5% (3/120) diagnosis-changing information, 12% (14/120) unrealistic patient portrayal, and 17% (20/120) technical issues, while the most prominent problem was agents interpreting findings before students could, occurring in 28% (34/120) of encounters for history/physical exam agents and 59% (71/120) for diagnostics/management agents, with most disruptions judged minor.
Early evidence for a multi-agent AI simulator for clinical reasoning practice: performance, consistency, and challenges
In Fall 2024, 175 second-year medical students completed three MAESSCR multi-agent LLM clinical encounters as coursework, and six clinician-educators rated 120 randomly sampled transcripts with a dichotomous tool, finding 92% (110/120) adherence to scripted details, 2.5% (3/120) diagnosis-changing information, 12% (14/120) unrealistic patient portrayal, and 17% (20/120) technical issues, while the most prominent problem was agents interpreting findings before students could, occurring in 28% (34/120) of encounters for history/physical exam agents and 59% (71/120) for diagnostics/management agents, with most disruptions judged minor.
In Fall 2024, 175 second-year medical students completed three MAESSCR multi-agent LLM clinical encounters as coursework, and six clinician-educators rated 120 randomly sampled transcripts with a dichotomous tool, finding 92% (110/120) adherence to scripted details, 2.5% (3/120) diagnosis-changing information, 12% (14/120) unrealistic patient portrayal, and 17% (20/120) technical issues, while the most prominent problem was agents interpreting findings before students could, occurring in 28% (34/120) of encounters for history/physical exam agents and 59% (71/120) for diagnostics/management agents, with most disruptions judged minor.
Advanced science (Weinheim, Baden-Wurttemberg, Germany) Sep 16, 2026
This study applied AI-guided phenotypic drug repurposing to drug-resistant Streptococcus pneumoniae, using ensembles of transformer, graph, and tree models trained on 1849 actives and 34 503 inactives to prospectively examine 6747 drugs, selecting 11 candidate antibiotics of which nine strongly reduced in vitro growth of S. pneumoniae R6 (IC50 ≤ 0.4 µg/mL), with the most potent drugs thiostrepton and ceftiofur showing IC50 values of 0.0001 µg/mL (60.1 pM) and 0.0004 µg/mL (764 pM), respectively, and thiostrepton remaining highly potent against multidrug-resistant strains.
This study applied AI-guided phenotypic drug repurposing to drug-resistant Streptococcus pneumoniae, using ensembles of transformer, graph, and tree models trained on 1849 actives and 34 503 inactives to prospectively examine 6747 drugs, selecting 11 candidate antibiotics of which nine strongly reduced in vitro growth of S. pneumoniae R6 (IC50 ≤ 0.4 µg/mL), with the most potent drugs thiostrepton and ceftiofur showing IC50 values of 0.0001 µg/mL (60.1 pM) and 0.0004 µg/mL (764 pM), respectively, and thiostrepton remaining highly potent against multidrug-resistant strains.
AI-Guided Phenotypic Drug Repurposing Against Streptococcus pneumoniae
This study applied AI-guided phenotypic drug repurposing to drug-resistant Streptococcus pneumoniae, using ensembles of transformer, graph, and tree models trained on 1849 actives and 34 503 inactives to prospectively examine 6747 drugs, selecting 11 candidate antibiotics of which nine strongly reduced in vitro growth of S. pneumoniae R6 (IC50 ≤ 0.4 µg/mL), with the most potent drugs thiostrepton and ceftiofur showing IC50 values of 0.0001 µg/mL (60.1 pM) and 0.0004 µg/mL (764 pM), respectively, and thiostrepton remaining highly potent against multidrug-resistant strains.
This study applied AI-guided phenotypic drug repurposing to drug-resistant Streptococcus pneumoniae, using ensembles of transformer, graph, and tree models trained on 1849 actives and 34 503 inactives to prospectively examine 6747 drugs, selecting 11 candidate antibiotics of which nine strongly reduced in vitro growth of S. pneumoniae R6 (IC50 ≤ 0.4 µg/mL), with the most potent drugs thiostrepton and ceftiofur showing IC50 values of 0.0001 µg/mL (60.1 pM) and 0.0004 µg/mL (764 pM), respectively, and thiostrepton remaining highly potent against multidrug-resistant strains.
Transfusion clinique et biologique : journal de la Societe francaise de transfusion sanguine Sep 16, 2026
This review reflects on how infectious diseases shaped transfusion services from 2016 to 2026, covering innovations in donor selection, testing and pathogen reduction, notable pathogens including Zika virus, Plasmodium, Babesia and SARS-CoV-2, and favorable developments such as individualized risk assessment, relaxation of donor deferral policies, and AI and machine learning tools for surveillance and horizon scanning, while noting that many of these gains do not extend to low and low-middle income countries.
This review reflects on how infectious diseases shaped transfusion services from 2016 to 2026, covering innovations in donor selection, testing and pathogen reduction, notable pathogens including Zika virus, Plasmodium, Babesia and SARS-CoV-2, and favorable developments such as individualized risk assessment, relaxation of donor deferral policies, and AI and machine learning tools for surveillance and horizon scanning, while noting that many of these gains do not extend to low and low-middle income countries.
Infectious risks of transfusion: a 10-year look back and perspectives
This review reflects on how infectious diseases shaped transfusion services from 2016 to 2026, covering innovations in donor selection, testing and pathogen reduction, notable pathogens including Zika virus, Plasmodium, Babesia and SARS-CoV-2, and favorable developments such as individualized risk assessment, relaxation of donor deferral policies, and AI and machine learning tools for surveillance and horizon scanning, while noting that many of these gains do not extend to low and low-middle income countries.
This review reflects on how infectious diseases shaped transfusion services from 2016 to 2026, covering innovations in donor selection, testing and pathogen reduction, notable pathogens including Zika virus, Plasmodium, Babesia and SARS-CoV-2, and favorable developments such as individualized risk assessment, relaxation of donor deferral policies, and AI and machine learning tools for surveillance and horizon scanning, while noting that many of these gains do not extend to low and low-middle income countries.
medRxiv Sep 16, 2026
This systematic review and meta-analysis of 445 studies found that weaker preoperative social connection was associated with early postoperative mortality (OR 1.50, 95% CI 1.12-2.01) and non-home discharge (OR 1.95, 95% CI 1.35-2.81), while confidence intervals for postoperative survival, unplanned readmission, and complications all included 1, with overall certainty ranging from low to very low.
This systematic review and meta-analysis of 445 studies found that weaker preoperative social connection was associated with early postoperative mortality (OR 1.50, 95% CI 1.12-2.01) and non-home discharge (OR 1.95, 95% CI 1.35-2.81), while confidence intervals for postoperative survival, unplanned readmission, and complications all included 1, with overall certainty ranging from low to very low.
Preoperative Social Connection and Postoperative Outcomes in Adults Undergoing Surgery: A Systematic Review and Meta-analysis
This systematic review and meta-analysis of 445 studies found that weaker preoperative social connection was associated with early postoperative mortality (OR 1.50, 95% CI 1.12-2.01) and non-home discharge (OR 1.95, 95% CI 1.35-2.81), while confidence intervals for postoperative survival, unplanned readmission, and complications all included 1, with overall certainty ranging from low to very low.
This systematic review and meta-analysis of 445 studies found that weaker preoperative social connection was associated with early postoperative mortality (OR 1.50, 95% CI 1.12-2.01) and non-home discharge (OR 1.95, 95% CI 1.35-2.81), while confidence intervals for postoperative survival, unplanned readmission, and complications all included 1, with overall certainty ranging from low to very low.
BMC Geriatrics Sep 16, 2026
Using 20 standardised geriatric pharmacotherapy vignettes (fictional older adults aged 72–88 across four clinical domains, each containing three potentially inappropriate medications and one START-type omission anchored to the AGS Beers Criteria and STOPP/START version 3), this study had GPT-5.2, Claude Sonnet 4.5, and Gemini 3 Pro respond under an identical master prompt and default end-user settings, with two geriatricians blinded to model identity independently rating anonymised outputs on a 100-point rubric (output quality 0–80, critical safety-risk prioritisation 0–20), finding that Stage 1 item-level answer-key concordance was uniformly high with limited between-model discrimination while expert-rated total scores differed significantly across models (p < 0.001; Kendall's W = 0.
Using 20 standardised geriatric pharmacotherapy vignettes (fictional older adults aged 72–88 across four clinical domains, each containing three potentially inappropriate medications and one START-type omission anchored to the AGS Beers Criteria and STOPP/START version 3), this study had GPT-5.2, Claude Sonnet 4.5, and Gemini 3 Pro respond under an identical master prompt and default end-user settings, with two geriatricians blinded to model identity independently rating anonymised outputs on a 100-point rubric (output quality 0–80, critical safety-risk prioritisation 0–20), finding that Stage 1 item-level answer-key concordance was uniformly high with limited between-model discrimination while expert-rated total scores differed significantly across models (p < 0.001; Kendall's W = 0.
Quality and Safety of Large Language Model–Generated Medication Review Outputs in Geriatric Pharmacotherapy: A Two-Stage Comparative Vignette-Based Benchmark Evaluation
Using 20 standardised geriatric pharmacotherapy vignettes (fictional older adults aged 72–88 across four clinical domains, each containing three potentially inappropriate medications and one START-type omission anchored to the AGS Beers Criteria and STOPP/START version 3), this study had GPT-5.2, Claude Sonnet 4.5, and Gemini 3 Pro respond under an identical master prompt and default end-user settings, with two geriatricians blinded to model identity independently rating anonymised outputs on a 100-point rubric (output quality 0–80, critical safety-risk prioritisation 0–20), finding that Stage 1 item-level answer-key concordance was uniformly high with limited between-model discrimination while expert-rated total scores differed significantly across models (p < 0.001; Kendall's W = 0.
Using 20 standardised geriatric pharmacotherapy vignettes (fictional older adults aged 72–88 across four clinical domains, each containing three potentially inappropriate medications and one START-type omission anchored to the AGS Beers Criteria and STOPP/START version 3), this study had GPT-5.2, Claude Sonnet 4.5, and Gemini 3 Pro respond under an identical master prompt and default end-user settings, with two geriatricians blinded to model identity independently rating anonymised outputs on a 100-point rubric (output quality 0–80, critical safety-risk prioritisation 0–20), finding that Stage 1 item-level answer-key concordance was uniformly high with limited between-model discrimination while expert-rated total scores differed significantly across models (p < 0.001; Kendall's W = 0.
medRxiv Sep 16, 2026
Using a zero-shot large language model pipeline (Gemini 3.5 Flash and Claude Haiku) to extract 46 symptoms from 2,728 discharge notes of 1,507 colorectal cancer patients in MIMIC-IV, this study built patient-level symptom co-occurrence networks with phi correlation (≥0.10) and Louvain community detection; both models converged on three clinically coherent symptom clusters — Systemic, CRC Disease-Specific, and Gastrointestinal — and Systemic cluster burden was associated with in-hospital mortality (OR=1.33) and 1-year mortality (OR=1.41) while CRC Disease-Specific cluster burden independently predicted 30-day readmission (OR=1.20), with both associations robust to adjustment for metastatic disease.
Using a zero-shot large language model pipeline (Gemini 3.5 Flash and Claude Haiku) to extract 46 symptoms from 2,728 discharge notes of 1,507 colorectal cancer patients in MIMIC-IV, this study built patient-level symptom co-occurrence networks with phi correlation (≥0.10) and Louvain community detection; both models converged on three clinically coherent symptom clusters — Systemic, CRC Disease-Specific, and Gastrointestinal — and Systemic cluster burden was associated with in-hospital mortality (OR=1.33) and 1-year mortality (OR=1.41) while CRC Disease-Specific cluster burden independently predicted 30-day readmission (OR=1.20), with both associations robust to adjustment for metastatic disease.
Large Language Model-derived Symptom Clusters and Patient Outcomes in Colorectal Cancer from MIMIC-IV Clinical Notes
Using a zero-shot large language model pipeline (Gemini 3.5 Flash and Claude Haiku) to extract 46 symptoms from 2,728 discharge notes of 1,507 colorectal cancer patients in MIMIC-IV, this study built patient-level symptom co-occurrence networks with phi correlation (≥0.10) and Louvain community detection; both models converged on three clinically coherent symptom clusters — Systemic, CRC Disease-Specific, and Gastrointestinal — and Systemic cluster burden was associated with in-hospital mortality (OR=1.33) and 1-year mortality (OR=1.41) while CRC Disease-Specific cluster burden independently predicted 30-day readmission (OR=1.20), with both associations robust to adjustment for metastatic disease.
Using a zero-shot large language model pipeline (Gemini 3.5 Flash and Claude Haiku) to extract 46 symptoms from 2,728 discharge notes of 1,507 colorectal cancer patients in MIMIC-IV, this study built patient-level symptom co-occurrence networks with phi correlation (≥0.10) and Louvain community detection; both models converged on three clinically coherent symptom clusters — Systemic, CRC Disease-Specific, and Gastrointestinal — and Systemic cluster burden was associated with in-hospital mortality (OR=1.33) and 1-year mortality (OR=1.41) while CRC Disease-Specific cluster burden independently predicted 30-day readmission (OR=1.20), with both associations robust to adjustment for metastatic disease.
medRxiv Sep 16, 2026
Using choroid segmentation as a prototype task on 6,076 OCT B-scans from 80 subjects, this study systematically compared scalar no-reference image quality metrics (BRISQUE, NIQE, PIQE, SNR, PSNR), general-purpose ImageNet-pretrained representations, and retinal foundation models as task-specific quality gates, finding that scalar metrics correlate weakly with segmentation Dice (|r| < 0.20), that general-purpose pretrained representations reach linear-probe ROC-AUC up to about 0.77, that the OCT-specific foundation model RETFound reaches about 0.81, and that only the retinal foundation model embeddings form quality-aligned unsupervised K-Means clusters exceeding a patient-level permutation null.
Using choroid segmentation as a prototype task on 6,076 OCT B-scans from 80 subjects, this study systematically compared scalar no-reference image quality metrics (BRISQUE, NIQE, PIQE, SNR, PSNR), general-purpose ImageNet-pretrained representations, and retinal foundation models as task-specific quality gates, finding that scalar metrics correlate weakly with segmentation Dice (|r| < 0.20), that general-purpose pretrained representations reach linear-probe ROC-AUC up to about 0.77, that the OCT-specific foundation model RETFound reaches about 0.81, and that only the retinal foundation model embeddings form quality-aligned unsupervised K-Means clusters exceeding a patient-level permutation null.
Task-Specific Quality Gating for Retinal OCT B-Scans: Learned Representations Over Scalar Metrics in Choroid Segmentation
Using choroid segmentation as a prototype task on 6,076 OCT B-scans from 80 subjects, this study systematically compared scalar no-reference image quality metrics (BRISQUE, NIQE, PIQE, SNR, PSNR), general-purpose ImageNet-pretrained representations, and retinal foundation models as task-specific quality gates, finding that scalar metrics correlate weakly with segmentation Dice (|r| < 0.20), that general-purpose pretrained representations reach linear-probe ROC-AUC up to about 0.77, that the OCT-specific foundation model RETFound reaches about 0.81, and that only the retinal foundation model embeddings form quality-aligned unsupervised K-Means clusters exceeding a patient-level permutation null.
Using choroid segmentation as a prototype task on 6,076 OCT B-scans from 80 subjects, this study systematically compared scalar no-reference image quality metrics (BRISQUE, NIQE, PIQE, SNR, PSNR), general-purpose ImageNet-pretrained representations, and retinal foundation models as task-specific quality gates, finding that scalar metrics correlate weakly with segmentation Dice (|r| < 0.20), that general-purpose pretrained representations reach linear-probe ROC-AUC up to about 0.77, that the OCT-specific foundation model RETFound reaches about 0.81, and that only the retinal foundation model embeddings form quality-aligned unsupervised K-Means clusters exceeding a patient-level permutation null.
Journal of Medical Internet Research Sep 16, 2026
Using electronic health record data from 9,496 patients with trauma treated in the Capital Region of Denmark between 2017 and 2024, this study developed a hybrid neural network combining tabular and sequential data to predict 30-day all-cause mortality at any time point from prehospital care to discharge, achieving AUROC 0.962 and AUPRC 0.655 on a holdout set of 1,829 patients and AUROC 0.905 at 1 hour from first patient contact in active-cohort evaluation, with better discrimination than the Revised Trauma Score (mean ΔAUROC +0.297) and the Trauma and Injury Severity Score (mean ΔAUROC +0.169).
Using electronic health record data from 9,496 patients with trauma treated in the Capital Region of Denmark between 2017 and 2024, this study developed a hybrid neural network combining tabular and sequential data to predict 30-day all-cause mortality at any time point from prehospital care to discharge, achieving AUROC 0.962 and AUPRC 0.655 on a holdout set of 1,829 patients and AUROC 0.905 at 1 hour from first patient contact in active-cohort evaluation, with better discrimination than the Revised Trauma Score (mean ΔAUROC +0.297) and the Trauma and Injury Severity Score (mean ΔAUROC +0.169).
Dynamic Prediction of 30-Day Mortality in Patients With Trauma Using a Hybrid Neural Network Model: Model Development and Evaluation Study
Using electronic health record data from 9,496 patients with trauma treated in the Capital Region of Denmark between 2017 and 2024, this study developed a hybrid neural network combining tabular and sequential data to predict 30-day all-cause mortality at any time point from prehospital care to discharge, achieving AUROC 0.962 and AUPRC 0.655 on a holdout set of 1,829 patients and AUROC 0.905 at 1 hour from first patient contact in active-cohort evaluation, with better discrimination than the Revised Trauma Score (mean ΔAUROC +0.297) and the Trauma and Injury Severity Score (mean ΔAUROC +0.169).
Using electronic health record data from 9,496 patients with trauma treated in the Capital Region of Denmark between 2017 and 2024, this study developed a hybrid neural network combining tabular and sequential data to predict 30-day all-cause mortality at any time point from prehospital care to discharge, achieving AUROC 0.962 and AUPRC 0.655 on a holdout set of 1,829 patients and AUROC 0.905 at 1 hour from first patient contact in active-cohort evaluation, with better discrimination than the Revised Trauma Score (mean ΔAUROC +0.297) and the Trauma and Injury Severity Score (mean ΔAUROC +0.169).

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