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PubMed2026

The application of artificial intelligence in healthcare practice: A mapping review of systematic reviews.

Artificial intelligence (AI) is rapidly transforming healthcare practice, with growing evidence supporting its use in diagnosis, prognosis, treatment planning, and operational decision-making. The proliferation of systematic reviews in recent years underscores the need for an updated synthesis of the literature to inform research, policy, and practice. We searched PubMed, Web of Science, Scopus, IEEE Xplore, and CINAHL for systematic reviews and meta-analyses published between 2019 and February 2026. Eligible reviews focused on AI applications in healthcare practice, were peer-reviewed, and written in English. A total of 368 reviews met the inclusion criteria. Publication volume increased steadily, peaking in 2025. AI research was concentrated in high-density domains, such as radiology, oncology, and critical care. Across reviews, diagnostic imaging, electronic health record (EHR) data, and biomarkers/laboratory results accounted for 68% of training data sources, though newer data types, such as wearable device and sensor data, emerged from 2022 onward. Diagnosis, prognosis, and treatment comprised over 80% of AI applications, with novel uses emerging in recent years, such as AI-assisted clinical documentation (e.g., ambient documentation tools) and patient education. Ethical concerns were reported in 78.5% of reviews, with privacy, model accuracy, data and algorithmic bias, and explainability as recurrent themes. The proportion of reviews reporting ethical concerns increased from 2021 to 2025. AI applications in healthcare are expanding in scope, diversifying in data sources, and evolving toward novel clinical and operational uses. The human-centered AI or augmented intelligence paradigm, integrating computational precision with clinical expertise, holds significant promise but will require parallel advances in governance, regulatory frameworks, and ethical oversight to ensure safe adoption.

Artificial Intelligence↗

PubMed2026

Granger connectivity and graph-theoretical analysis of scalp EEG across the preictal to ictal transition for presurgical evaluation.

OBJECTIVE: To assess the feasibility of estimating lateralization and localization of the epileptogenic zone (EZ) in temporal and extratemporal lobe epilepsy by combining Electric Source Imaging (ESI) with functional connectivity analysis of high-density EEG from the preictal to the ictal phase. METHODS: Adults with drug-resistant focal epilepsy and at least one recorded seizure during 40- or 64 channels EEG monitoring were retrospectively included. Granger causality and hubness centrality were computed over the 10-s preictal interval and the first 5 s of the ictal period, with ictal onset defined as the first EEG change identified by experienced epileptologists. The reference standard for EZ localization was based on resective surgical outcome or stereo-EEG findings. RESULTS: Thirteen patients (7 females; median age 35 years) were included. Connectivity analyses showed higher concordance with clinical findings during the preictal phase than during the ictal phase for both lateralization (91% vs 46%) and localization (73% vs 27%). Performance was highest in temporal (7/7 lateralization; 6/7 localization) and frontal lobe epilepsy (2/2 for both), and lower in parieto-occipital epilepsy (1/2 and 0/2, respectively). In two cases with poor surgical outcome or no surgical indication, connectivity findings were discordant with clinical estimates. CONCLUSIONS: Connectivity analysis across the preictal to ictal transition provides relevant lateralizing and localizing information, particularly in temporal and frontal lobe epilepsy, and may reveal clinically meaningful discordance. SIGNIFICANCE: Integrating high-density EEG, ESI, and functional connectivity during the phase preceding the first EEG change may support non-invasive presurgical evaluation.

Humans↗

PubMed2026

Beyond antigen matching: compatibility intelligence theory for transfusion as an emergent biological system.

BACKGROUND: Despite major advances in serologic testing, extended phenotyping, and blood group genomics, clinically similar transfusion exposures may result in markedly different immune and clinical outcomes. Existing compatibility strategies do not fully explain this biological variability. OBJECTIVES: To examine transfusion compatibility as an emergent donor-recipient biological state and propose a systems-level conceptual framework that integrates established biological determinants into a testable model for future precision transfusion medicine. METHODS: This narrative review critically synthesizes current evidence from blood group genomics, recipient immunobiology, inflammation, disease-specific biology, transfusion medicine, and computational prediction. The proposed framework distinguishes Compatibility Intelligence Theory (CIT) as a biological interpretation from Precision Transfusion Intelligence (PTI) as its potential clinician-supervised translational application. RESULTS: The review argues that transfusion compatibility is shaped by interactions among donor genetics, recipient immune biology, inflammatory physiology, disease context, transfusion history, and longitudinal adaptation rather than by antigen matching alone. CIT provides an organizational framework for integrating these determinants, whereas PTI describes a possible clinician-supervised translation. To address current feasibility, the revised framework separates variables into routinely measurable, contextually available but incompletely standardized, and research-stage domains, and proposes a staged strategy for deriving rather than assuming their quantitative weights. Any clinical implementation would require comparative validation against current serologic, phenotypic, and genotype-based practice. CONCLUSIONS: Compatibility Intelligence Theory offers a testable systems-level framework for understanding transfusion compatibility without replacing established transfusion practices. The framework is not presented as a ready-to-use score: currently measurable variables can be organized for structured risk review, whereas inflammatory, immunogenetic, and multi-omic inputs require prospective standardization and validation. If future studies demonstrate incremental predictive and patient-centered benefit, CIT-informed PTI could support an adaptive, evidence-based extension of current precision transfusion practice.

Humans↗

PubMed2026

Effectiveness of an AI-based home exercise app for rehabilitation of rotator cuff-related shoulder pain: A randomized controlled trial.

BACKGROUND: Rotator cuff-related shoulder pain contributes to disability and healthcare use. Although therapeutic exercise is first-line treatment, limited supervision and adherence may reduce its effectiveness; digital rehabilitation with real-time feedback may address these limitations. OBJECTIVES: To evaluate the effectiveness of adding a digital rehabilitation program to standard physiotherapy on pain, function, fear-avoidance beliefs, and healthcare utilization. DESIGN: Single-center, assessor-blinded, randomized controlled trial with two parallel groups. METHOD: Forty-six adults (mean age 59 years) with rotator cuff-related shoulder pain were randomized to 12 weeks of conventional physiotherapy or physiotherapy plus an AI-based digital rehabilitation program using computer vision for real-time feedback and performance monitoring. Outcomes were assessed at baseline and at 2, 4, and 12 weeks. Pain intensity (NPRS) was primary outcome; secondary outcomes included upper limb function (QuickDASH), fear-avoidance beliefs (FABQ), and post-intervention healthcare utilization. Analyses followed an intention-to-treat approach. RESULTS: Pain reduction exceeded the MCID (1.3) at 4 and 12 weeks. Between-group differences favoured the intervention at Weeks 2 and 4 (MD -0.7; 95% CI -1.13 to -0.14 and MD -1.01; 95% CI -1.8 to -0.2, respectively). Upper limb function improved more at Week 4 (MD -7.3; 95% CI -12.3 to -2.2). FABQ scores decreased more at Week 12 (MD -7.6; 95% CI -14 to -0.5). Fewer participants in the experimental group required post-intervention healthcare (3 vs 10; p = 0.02). CONCLUSION: Adding AI-based home exercise app to conventional treatment improve pain and may improve function and reduce healthcare utilization in rotator cuff-related shoulder pain.

Humans↗

PubMed2026

Machine learning-based prediction of unplanned readmission and construction of an online calculator for elderly patients with mild ischemic stroke.

OBJECTIVE: To screen for independent risk factors for unplanned readmission in elderly patients with mild ischemic stroke, and to construct and validate an online risk prediction calculator based on an interpretable machine learning model, thereby providing a promising practical tool for accurate clinical assessment of 30‑day all‑cause unplanned readmission risk in this population. METHODS: A prospective cohort study was conducted, including 1050 patients aged ≥ 60 years with mild ischemic stroke admitted between August 2023 and September 2024. Participants were randomly divided into a training set (840 cases) and a test set (210 cases) at a ratio of 8:2. Risk factors were screened by univariate analysis and multivariable Logistic regression. Four machine learning models, namely LightGBM, XGBoost, Random Forest, and K‑Nearest Neighbors (KNN), were developed and their performance was evaluated using AUC, accuracy, sensitivity, and specificity as metrics. The SHAP framework was used for interpretability analysis, and an online calculator was subsequently developed based on the optimal model. RESULTS: Univariate analysis showed significant differences (P < 0.05) in 13 factors including age, smoking, AIP, TyG index, HALP score, etc. Multivariable Logistic regression identified age (OR = 9.752), smoking (OR = 5.171), AIP (OR = 6.691), TyG index (OR = 4.393), HALP score (OR = 2.831), and ≥ 2 comorbidities (OR = 3.664) as independent risk factors. All four machine learning models demonstrated good predictive performance. Based on a comprehensive evaluation of multiple metrics and computational efficiency, the LightGBM model exhibited the best predictive performance (AUC = 0.884, accuracy = 0.829, sensitivity = 0.812, specificity = 0.875). SHAP analysis showed that age, AIP, TyG index, smoking, and HALP score were key predictors. An online calculator developed based on this model enables individualized risk predictions. CONCLUSION: Key risk factors associated with 30‑day unplanned readmission in elderly patients with mild ischemic stroke were identified. The LightGBM model demonstrated high predictive accuracy, and together with the interpretability analysis and online calculator, offers a practical tool to support clinical risk assessment. However, this tool requires future external validation.

Humans↗

PubMed2026

Long-term safety of oral orforglipron in Japanese participants with type 2 diabetes (ACHIEVE-J): a multicentre, randomised, open-label, parallel-group phase 3 trial.

BACKGROUND: Orforglipron, an oral GLP-1 receptor agonist, requires further evaluation in east Asian populations with type 2 diabetes, given this group's distinct pathophysiological characteristics. This study aimed to assess orforglipron as add-on treatment to diet and exercise alone or to oral antihyperglycaemic medications in Japanese participants with type 2 diabetes. METHODS: This multicentre, randomised, open-label phase 3 study was conducted in 40 medical research centres and hospitals in Japan. Adults with type 2 diabetes and elevated glucose levels managing their condition with diet and exercise alone or with one or two oral antihyperglycaemic medications were assigned (1:1:1) via computer-generated random sequence to receive once-daily oral orforglipron (3 mg, 12 mg, or 36 mg). Randomisation was stratified by background therapy, baseline HbA1c (≤8·5% or >8·5%), and metformin use (yes vs no; applied only to α-glucosidase inhibitors, thiazolidinedione, and glinides). Investigators, participants, and site staff were not masked to treatment. The primary endpoint was safety for 52 weeks, assessed in all randomly assigned participants who received at least one dose of orforglipron. This study is registered with ClinicalTrials.gov, NCT06010004 (ACHIEVE-J). FINDINGS: Between Sept 28, 2023, and June 5, 2025, 450 participants were screened and 401 were randomly assigned to three groups (3 mg, n=132; 12 mg, n=135; and 36 mg, n=134). 352 (88%) completed study treatment. 339 participants (85%, 95% CI 80·7-87·8) had at least one treatment-emergent adverse event (TEAE), more frequently in the 36-mg group (118 [88%, 95% CI 81·5-92·5]) than in the 3-mg (107 [81%, 73·5-86·8]) and 12-mg (114 [84%, 77·4-89·6]) groups. Most TEAEs were of mild (267 [67%, 61·8-71·0]) or moderate (63 [16%, 12·5-19·6]) severity. Discontinuations due to an adverse event occurred in 19 of 134 participants (14%, 95% CI 9·3-21·1) in the 36-mg group compared with seven of 132 (5%, 2·6-10·5) in the 3-mg group and 11 of 135 (8%, 4·6-14·0) in the 12-mg group. Across treatment groups, gastrointestinal symptoms were the most common TEAEs leading to study treatment discontinuation (3 mg: 5 [3·8%, 1·6-8·6]; 12 mg: 8 [5·9%, 3·0-11·3]; and 36 mg: 11 [8·2%, 4·7-14·1]). Level 2 (blood glucose <54 mg/dL) hypoglycaemia events occurred in three of 135 participants in the 12-mg group (2%, 0·8-6·3) and in three of 134 in the 36-mg group (2%, 0·8-6·4) groups. No level 3 (severe) hypoglycaemia events occurred. Outcomes were generally similar across background therapies. INTERPRETATION: Treatment with orforglipron in combination with diet and exercise alone or one or two oral antihyperglycaemic medications for 52 weeks demonstrated an acceptable safety profile in Japanese adults with type 2 diabetes. FUNDING: Eli Lilly. TRANSLATION: For the Japanese translation of the abstract see Supplementary Materials section.

Aged↗

PubMed2026

The glucagon and GLP-1 receptor dual agonist DD01 for metabolic dysfunction-associated steatotic liver disease and steatohepatitis (DD01-DN-02): 12-week results from a randomised, double-blind, multicentre, placebo-controlled, phase 2 trial.

BACKGROUND: Metabolic dysfunction-associated steatohepatitis (MASH) is a major public health problem arising in the context of metabolic syndrome and obesity. DD01 is a liver-targeted GLP-1 receptor and glucagon dual agonist being investigated for the treatment of metabolic dysfunction-associated steatotic liver disease (MASLD) and MASH. The DD01-DN-02 trial aimed to evaluate the efficacy and safety of DD01 in adults with MASLD or MASH; this initial analysis reports prespecified 12-week outcomes to assess early hepatic effects. METHODS: DD01-DN-02 is an ongoing, randomised, double-blind, multicentre, placebo-controlled, phase 2 trial conducted at 12 outpatient clinical sites in the USA. Adults aged 18-70 years with obesity or who were overweight (BMI ≥25 kg/m2) were included in the study. Patients with MASLD or MASH underwent liver biopsy and MRI-proton density fat fraction (PDFF) and were eligible if liver fat content was 10% or higher with metabolic risk factors, or if the biopsy confirmed MASH with a non-alcoholic fatty liver disease activity score of at least 4. Participants were randomly assigned (1:1) to receive once-weekly subcutaneous DD01 40 mg or matched placebo over 48 weeks, dose-escalated over 2 weeks, using a centrally administered interactive response technology system. The randomisation sequence was computer-generated by an independent statistician. Participants, investigators, study staff, outcome assessors, and the sponsor were masked to treatment assignment. The primary endpoint was the proportion of participants having at least a 30% relative reduction in liver fat by MRI-PDFF at week 12, which was analysed in all randomly assigned participants receiving at least one dose of study drug or placebo. Safety analyses included all participants who received at least one dose of study drug. Missing primary endpoint data were handled using multiple imputation under a missing-at-random assumption. This trial is registered with ClinicalTrials.gov (NCT06410924) and is ongoing but closed to new participants. FINDINGS: Between June 13, 2024, and Jan 30, 2025, 67 eligible participants were enrolled, of whom 33 were randomly assigned to DD01 and 34 to placebo. The mean age of participants was 48·4 years (SD 10·6), 42 (63%) were female, 25 (37%) were male, 57 (85%) were White, and 52 (78%) participants had biopsy-confirmed MASH. At week 12, 25 (76%) of 33 participants receiving DD01 had a 30% or higher reduction in liver fat versus four (12%) of 34 participants receiving placebo (adjusted common odds ratio 28·8 [95% CI 7·2-115·2]; adjusted relative risk 6·3 [95% CI 2·5-15·9]; p<0·0001). Treatment-emergent adverse events occurred in 28 (85%) of 33 participants receiving DD01 and 23 (68%) of 34 participants receiving placebo. The most common adverse events were nausea (18 [55%] of 33 participants assigned DD01; six [18%] of 34 participants assigned placebo), diarrhoea (nine [27%] of 33; six [18%] of 34), and vomiting (ten [30%] of 33; four [12%] of 34). Treatment-emergent adverse events led to treatment discontinuation in four (12%) of 33 participants in the DD01 group and one (3%) of 34 participants in the placebo group. Two (6%) treatment-emergent serious adverse events occurred in the DD01 group (abdominal pain and acute cholecystitis) and zero in the placebo group. No deaths occurred. INTERPRETATION: In this prespecified 12-week primary analysis, DD01 produced rapid reductions in liver fat compared with placebo, supporting further evaluation in long-term studies. FUNDING: D&D Pharmatech, Neuraly.

Humans↗