Aim

The aim of IJAI-MH is to provide a global platform for the publication and dissemination of innovative research on the application of artificial intelligence and advanced computational technologies in medicine and healthcare.

The journal aims to:

Advance AI-driven approaches for disease diagnosis and prediction

Promote intelligent technologies for clinical decision-making

Support research in medical imaging and computer-aided diagnosis

Encourage development of predictive and personalized healthcare

Explore applications of generative AI and large language models

Promote AI-based drug discovery and development

Advance digital health, telemedicine, and remote monitoring

Encourage AI applications in public health and epidemiology

Examine robotics and intelligent medical devices

Promote reproducible and clinically validated AI research

Address AI ethics, governance, transparency, privacy, bias, and patient safety

Scope

Artificial Intelligence & Machine Learning

Machine Learning

Deep Learning

Reinforcement Learning

Neural Networks

Natural Language Processing

Computer Vision

Explainable AI

Federated Learning

Transfer Learning

Multimodal AI

Clinical & Medical AI

AI-assisted diagnosis

Clinical decision support systems

Disease prediction

Risk stratification

Clinical outcome prediction

Personalized medicine

Precision medicine

Treatment optimization

Patient monitoring

Clinical workflow automation

Medical Imaging

AI in radiology

MRI analysis

CT image analysis

X-ray interpretation

Ultrasound

Mammography

Pathology image analysis

Dermatological imaging

Ophthalmic imaging

Image segmentation

Image classification

Computer-aided diagnosis

Generative AI & LLMs

Generative AI in healthcare

Large language models

Medical chatbots

Clinical NLP

AI-assisted medical documentation

Medical question answering

Clinical summarization

Multimodal medical AI

AI-generated medical content

WHO's recent guidance specifically recognizes large multimodal models as an emerging area with potential applications across healthcare, research, public health, and drug development.

Biomedical & Pharmaceutical AI

Drug discovery

Drug repurposing

Molecular modeling

Protein structure prediction

Biomarker discovery

Genomics

Bioinformatics

Computational biology

Pharmacovigilance

Clinical trial optimization

Digital Health

Telemedicine

Mobile health

Wearable devices

Remote patient monitoring

Digital therapeutics

Smart hospitals

Healthcare IoT

Electronic health records

Health information systems

Healthcare analytics

Robotics & Intelligent Systems

Surgical robotics

Rehabilitation robotics

Assistive technologies

Autonomous medical systems

Intelligent prosthetics

Hospital robotics

Human–AI interaction

Public Health

Disease surveillance

Outbreak prediction

Epidemiological modeling

Population health analytics

Health resource optimization

Global health

Preventive medicine

Responsible & Ethical AI

AI ethics

Algorithmic bias

Fairness

Explainability

Transparency

Patient privacy

Data governance

AI regulation

Clinical safety

Responsible AI

Human-AI collaboration

These areas are particularly important because AI in health raises questions around privacy, equity, accountability, bias, and appropriate governance.