Open Access
ISSN:2837-6331 (print)
ISSN:2837-634X (online)
Publication Frequency:Continuously
Innovation and rapid technological development in intelligent medicine and healthcare impacts profoundly on many aspects of people’s life. It is believed that developing advanced intelligent algorithms and systems has the potential to save medical resources, reduce administrative costs and burdens, improve integration between medical worker and care providers, reduce medical errors, and improve medical and healthcare quality and patient outcomes. Along with the world’s population growing and aging, challenges in medicine and healthcare on a global scale are very apparent. The vision of Journal of Intelligent Medicine and Healthcare is to attack these apparent challenges through the design of algorithms, mathematical methods, systems, devices, and policies for medicine and healthcare in an intelligent way.
Open Access
ARTICLE
Journal of Intelligent Medicine and Healthcare, Vol.4, pp. 155-177, 2026, DOI:10.32604/jimh.2026.082983 - 14 September 2026
Abstract Background: Exponential growth of diverse clinical data presents challenges for real-time predictive analytics in healthcare. Federated learning offers a paradigm for multi-institutional model training without centralized data sharing, but large-scale deployment across diverse healthcare settings with real-world electronic health record (EHR) integration challenges remains limited. Methods: We implemented Phase 1 of a federated learning network deploying federated histogram-based XGBoost across 47 U.S. healthcare institutions from January to June 2023 as a quality improvement initiative. The system processes clinical data locally, transmitting only gradient and Hessian histograms with differential privacy (ε = 1.0, δ = 10−5). Primary… More >
Open Access
ARTICLE
Journal of Intelligent Medicine and Healthcare, Vol.4, pp. 125-153, 2026, DOI:10.32604/jimh.2026.075373 - 14 September 2026
Abstract Accurate classification of pancreatic endocrinogenesis-related abnormalities in capsule endoscopy images remains challenging because of class imbalance, high intra-class variability, and limited annotated data. This study proposes an optimal data distribution framework based on Local Interpretable Model-Agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP) perturbations to enhance automated classification performance. The workflow combines perturbation-guided data augmentation, feature-importance analysis, and deep-learning classifiers, including convolutional neural networks (CNNs), U-Net, and You Only Look Once (YOLO). The approach is evaluated on the Kvasir-CapsuleSeg dataset using accuracy, macro F1-score, balanced area under the receiver operating characteristic curve (AUC), and confusion-matrix More >
Open Access
ARTICLE
Journal of Intelligent Medicine and Healthcare, Vol.4, pp. 109-124, 2026, DOI:10.32604/jimh.2026.084876 - 21 July 2026
Abstract High-stakes clinical decision support (CDS) demands a property that aggregate accuracy cannot capture: a trace that a clinician who was not in the room can inspect layer by layer when the system is wrong. We argue that the way to obtain this property is to refuse to entangle the large language model (LLM) with the rest of the pipeline. We propose
Open Access
ARTICLE
Journal of Intelligent Medicine and Healthcare, Vol.4, pp. 99-108, 2026, DOI:10.32604/jimh.2026.083110 - 18 June 2026
Abstract Objective: To develop a low-cost automated cataract severity classification system operating on standard consumer-grade colour photographs of the eye, without specialised ophthalmic hardware. Methods: A hybrid framework was designed that fuses deep features from a Convolutional Neural Network (CNN) with five handcrafted Grey-Level Co-occurrence Matrix (GLCM) and intensity descriptors—mean intensity, uniformity, standard deviation, contrast, and energy—extracted from a Hough-circle-localised pupil Region of Interest (ROI). A multi-class Support Vector Machine (SVM) with Radial Basis Function (RBF) kernel classifies each image into one of four severity grades: normal, immature, mature, or hypermature cataract. Results: The proposed fused system More >
Open Access
ARTICLE
Journal of Intelligent Medicine and Healthcare, Vol.4, pp. 87-97, 2026, DOI:10.32604/jimh.2026.080288 - 24 April 2026
Abstract Background: Retinal fundus imaging is central to early diagnosis of sight-threatening conditions, including diabetic retinopathy, glaucoma, and retinal vein occlusion. Clinical utility is compromised by non-uniform illumination, motion blur, and low contrast—artefacts that reduce diagnostic accuracy. Effective image enhancement is a prerequisite for reliable computer-aided ophthalmic diagnosis. Methods: This paper proposes a two-stage enhancement pipeline combining luminosity correction via HSV colour space decomposition with Contrast Limited Adaptive Histogram Equalization (CLAHE) on the Value (V) channel. Validation is conducted on three publicly available benchmarks: DRIVE (40 images), STARE (20 images), and CHASEDB1 (28 images). Quantitative metrics… More >
Open Access
ARTICLE
Journal of Intelligent Medicine and Healthcare, Vol.4, pp. 37-86, 2026, DOI:10.32604/jimh.2026.075201 - 23 January 2026
Abstract Wide QRS Complex Tachycardia (WCT) is a life-threatening cardiac arrhythmia requiring rapid and accurate diagnosis. Traditional manual ECG interpretation is time-consuming and subject to inter-observer variability, while existing AI models often lack the clinical interpretability necessary for trusted deployment in emergency settings. We developed CardioForest, an optimized Random Forest ensemble model, for automated WCT detection from 12-lead ECG signals. The model was trained, tested, and validated using 10-fold cross-validation on 800,000 ten-second-long 12-lead Electrocardiogram (ECG) recordings from the MIMIC-IV dataset (15.46% WCT prevalence), with comparative evaluation against XGBoost, LightGBM, and Gradient Boosting models. Performance was… More >
Open Access
ARTICLE
Journal of Intelligent Medicine and Healthcare, Vol.4, pp. 1-35, 2026, DOI:10.32604/jimh.2026.074347 - 23 January 2026
Abstract Smoking continues to be a major preventable cause of death worldwide, affecting millions through damage to the heart, metabolism, liver, and kidneys. However, current medical screening methods often miss the early warning signs of smoking-related health problems, leading to late-stage diagnoses when treatment options become limited. This study presents a systematic comparative evaluation of machine learning approaches for smoking-related health risk assessment, emphasizing clinical interpretability and practical deployment over algorithmic innovation. We analyzed health screening data from 55,691 individuals, examining various health indicators including body measurements, blood tests, and demographic information. We tested three advanced… More >
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