Aims & Scope
The Journal of Artificial Intelligence in Healthcare & Life Sciences (JAIHL) is an international, peer-reviewed, open-access journal dedicated to advancing transformative research at the intersection of computational intelligence, biomedical science, and clinical practice. The journal provides an authoritative scholarly platform for the rapid dissemination of high-quality, original research addressing complex, real-world challenges in medicine, global health, and molecular biology through innovative applications of artificial intelligence.
JAIHL publishes original research articles, comprehensive systematic reviews, meta-analyses, methodological and technical papers, short communications, and critical editorial perspectives. Interdisciplinary research with strong methodological rigor, reproducible datasets, and clear translational relevance is highly encouraged.
The core scope of the journal includes, but is not limited to, the following specialized tracks:
- Medical AI & Diagnostics: Development, validation, and clinical evaluation of deep learning models for medical imaging, radiology, computer vision, early disease diagnosis, prognostic prediction, and digital pathology.
- Drug Discovery & Genomics: Applications of machine learning, reinforcement learning, and transformers in structural biology, predictive molecular design, target identification, genomics, proteomics, systems biology, and personalized medicine pipelines.
- Clinical Decision Support Systems: AI-driven frameworks for treatment recommendations, clinical workflow optimization, risk stratification, remote patient monitoring, outcome prediction, and precision oncology tracking.
- Healthcare Data Science & NLP: Large language models (LLMs) and natural language processing architectures optimized for electronic health records (EHRs), medical text mining, clinical knowledge graph construction, and large-scale multimodal healthcare data analytics.
- Public & Global Health AI: Artificial intelligence applications in automated epidemiology, disease surveillance networks, population health modeling, health systems logistics, health policy planning, and the deployment of digital solutions to reduce global health disparities.
- AI Ethics, Governance & Translational Research: Critical investigations addressing algorithmic fairness, explainable AI (XAI), dataset bias, transparency, accountability, data privacy security, and regulatory approval pathways for artificial intelligence implementation in clinical environments.