Aim & Scope

Aim

The Journal of Intelligent Computing and Health Informatics (JICHI) advances research at the intersection of computational intelligence and healthcare systems with a strong emphasis on methodological rigor and scientific innovation. Rooted in artificial intelligence, machine learning, and data science, JICHI offers a premier platform for publishing original work that not only addresses current challenges in medical technologies and public health systems but also contributes to foundational advancements in intelligent computing theory. JICHI distinguishes itself from other journals by prioritizing integrative contributions that simultaneously advance theoretical frameworks and produce practical implications for digital health. The journal values work that enhances algorithmic design, deepens epistemological understanding, and demonstrates measurable impact across healthcare applications. It promotes interdisciplinary collaboration among experts in computer science, informatics, clinical medicine, and public health, thereby fostering knowledge that is both scientifically robust and socially impactful.

Scope

We invite researchers to actively contribute to two core domains: Intelligent Computing (Multidisciplinary) and Health Informatics. These domains emphasize scientific rigor, algorithmic innovation, and methodological advancement with strong relevance to both computational theory and real-world healthcare systems.

1. Intelligent Computing (Multidisciplinary)

Scope related to this domain includes:

  • AI, Machine Learning, and Deep Learning: We welcome advanced supervised and unsupervised methods used for classification, prediction, anomaly detection, and medical inference, including convolutional, recurrent, and transformer-based architectures. Contributions should emphasize theoretical foundations such as model generalizability, interpretability, robustness, and computational efficiency, while clearly demonstrating their significance to health-related challenges and scientific advancement in computational intelligence.
  • Explainable Artificial Intelligence (XAI): We seek work that develops interpretable and transparent models supporting ethical healthcare decision-making. Preferred submissions will include methodological innovations such as causal inference modeling, saliency mapping, surrogate models, or counterfactual reasoning, especially where these strengthen theoretical constructs in explainability.
  • Quantum and Edge Computing: Contributions in this area should emphasize the development and application of novel algorithms for real-time analytics, decentralized inference, and privacy-preserving computation. We prioritize work that advances theoretical paradigms in quantum-enhanced learning and edge-based intelligence.
  • Natural Language Processing (NLP): We encourage submissions focused on processing unstructured clinical narratives through semantic analysis, EHR mining, and contextual embeddings. Contributions should show how innovations in NLP inform both computational linguistics and domain-specific applications in clinical informatics.
  • Bio-Inspired and Swarm Intelligence: We welcome work modeling natural systems such as genetic evolution or swarm behavior to develop adaptive, scalable, and distributed algorithms. Theoretical contributions that push the boundaries of emergent behavior modeling and algorithmic hybridization with mainstream AI are especially valued.

2. Health Informatics

Scope related to this domain includes:

  • Clinical Decision Support Systems (CDSS): We seek intelligent systems that improve diagnostic accuracy, treatment planning, and clinical workflow. Submissions should present robust models and frameworks that contribute to the theoretical evolution of adaptive learning in clinical decision-making.
  • Medical Image Analysis: Contributions should demonstrate algorithmic advancements in image segmentation, feature extraction, and classification. We value theoretical contributions that define new directions in computational imaging science and its application to medical diagnostics.
  • Health Data Mining and Epidemiological Analytics: We invite scalable and interpretable models that reveal population-level health patterns and inform policy decisions. Emphasis is placed on methodological innovation and the generalizability of findings.
  • IoT and Remote Health Monitoring: Submissions should highlight methodological approaches to data acquisition, transmission, and real-time analytics using wearable and telehealth platforms, contributing to theoretical advances in distributed health computing.
  • Electronic Health Records (EHR) and HIS: We prioritize work on interoperability frameworks, data integration, and intelligent system design that improve scalability, reliability, and semantic precision in health information systems.
  • Standards and Interoperability: We welcome research addressing health IT frameworks that enable secure and interoperable data exchange. Work that proposes novel architectures for aligning ontologies, harmonizing semantics, or ensuring compliance with global health standards is strongly encouraged.
  • Healthcare Technology Evaluation: Contributions should provide rigorous computational frameworks for evaluating technology adoption, clinical outcomes, and cost-effectiveness. We favor theoretical models that contribute to digital health economics and strategic decision-making.

JICHI prioritizes studies that bridge computational theory with healthcare implementation, particularly those that contribute simultaneously to theoretical innovation and practical application across health informatics and intelligent computing domains and welcomes interdisciplinary contributions from computer science, bioinformatics, public health, and clinical domains. All submissions undergo rigorous peer review to ensure relevance, innovation, and scientific quality.

Recent Discussion Related Journal Scope

To find out the progress of the discussion about selected science topics, you can click on the following tag:

Intelligent Computing: Artificial Intelligence; Machine Learning; Deep Learning; Explainable AI (XAI); Natural Language Processing (NLP); Computational Linguistics; Swarm Intelligence; Bio-inspired Algorithms; Evolutionary Computing; Multi-agent Systems; Federated Learning; Reinforcement Learning; Self-supervised Learning; Causal Inference; Knowledge Graphs; Edge Computing; Quantum Machine Learning; Optimization Algorithms; Model Interpretability; Transfer Learning; Human-Centered AI.

Health Informatics: Clinical Decision Support Systems (CDSS); Medical Image Analysis; Electronic Health Records (EHR); Health Information Systems (HIS); Data Mining in Healthcare; Epidemiological Modeling; Patient Monitoring Systems; Telemedicine; Remote Sensing in Healthcare; Wearable Health Devices; IoT for Health; Digital Biomarkers; Health Data Interoperability; Semantic Web in Medicine; Predictive Modeling in Public Health; Healthcare Technology Assessment; Mobile Health (mHealth); Privacy-preserving Computation in Health; Health NLP; Biomedical Signal Processing; Public Health Surveillance; Health Economic Modeling.

You may refer to these keywords when preparing your manuscript or conducting literature reviews to align your work with the current discourse and evolving focus of the journal.