Aims and Scope

Trends in Health Informatics particularly welcomes high-quality original research, reviews, and perspectives that combine methodological rigor with real-world impact. The journal emphasizes interdisciplinary studies that lie at the interface of health informatics and biomedical engineering, with a strong focus on artificial intelligence, data-centric approaches, and intelligent systems in healthcare.

Contributions are encouraged in, but not limited to, the following areas:

  1. Artificial Intelligence and Clinical Decision Support Development and application of machine learning, deep learning, explainable AI, and generative models for diagnosis, prognosis, risk prediction, and personalized clinical decision-making.

  2. Advanced Health Data Analytics and Big Data Data mining, predictive modeling, multimodal data integration, and large-scale analytics applied to clinical, genomic, wearable, sensor, and real-world healthcare data.

  3. Clinical Informatics and Health Information Systems Design, implementation, evaluation, and optimization of electronic health records (EHR/EMR), health information exchange (HIE), interoperability standards, and intelligent clinical workflows.

  4. Medical Image and Signal Informatics Advanced computational analysis and AI-driven processing of medical images (MRI, CT, ultrasound, X-ray, etc.) and biosignals (ECG, EEG, EMG, etc.) for improved diagnostics and monitoring.

  5. Digital Health, Telemedicine, and Connected Care Telehealth platforms, remote patient monitoring, mobile health (mHealth), wearable technologies, and Internet of Medical Things (IoMT) for accessible and continuous healthcare.

  6. Health Data Security, Privacy, and Ethics Secure data sharing, privacy-preserving techniques (including federated learning, blockchain applications), ethical considerations, and regulatory aspects in digital health systems.

  7. Bioinformatics and Precision Medicine Computational genomics, multi-omics data analysis, AI-driven drug discovery, treatment optimization, and personalized/precision medicine approaches.

  8. Computational and Data-driven Biomechanics Integration of biomechanical modeling with data-driven methods, including finite element analysis, digital twins, physics-informed neural networks, and simulation of biological systems.

  9. Biomaterials and Tissue Informatics Data-driven design and optimization of biomaterials, AI-assisted development of implants and scaffolds, computational modeling in tissue engineering and regenerative medicine.

  10. Intelligent Medical Devices and Smart Healthcare Systems Development of smart biosensors, AI-enabled medical devices, rehabilitation technologies, and intelligent healthcare infrastructures for real-time monitoring and diagnostics.

  11. Behavioral, Social, and Population Health Informatics Analysis of health-related data from social media, patient-reported outcomes, and public health systems using natural language processing, network science, and data-driven epidemiological methods.

  12. Interdisciplinary and Translational Health Informatics Research that bridges health informatics with biomedical engineering, data science, clinical practice, and public health, with a clear emphasis on translational impact and real-world implementation.