About the Journal
The American Journal of Machine Learning in Healthcare (AJMLH) publishes peer-reviewed research that advances safe, effective, and equitable AI for clinical care and health systems. We welcome translational studies, prospective deployments, methodological innovations, and governance frameworks that improve patient outcomes, safety, and efficiency across diverse populations and settings.
Aims & Scope
- Supervised/unsupervised/deep learning for diagnosis, prognosis, and triage
- Multimodal fusion: EHR, medical imaging, signals (ECG/EEG), text, genomics
- Foundation models & LLMs for clinical tasks; retrieval-augmented systems
- Clinical decision support, workflow integration, human-AI teaming
- Bias, fairness, generalizability, and external validation
- Privacy-preserving ML: federated, differential privacy, secure enclaves
- Causal inference, counterfactuals, interpretability & uncertainty estimation
- Real-world evaluation: prospective/silent trials, RCTs, post-deployment monitoring
- Model governance, MLOps in healthcare, safety cases, audit & lifecycle management
Article Types
- Original Research (clinical, methodological, systems)
- Systematic Reviews & Meta-analyses
- Practice & Deployment Reports (implementation studies)
- Datasets/Benchmarks & Model/Code Papers
- Registered Reports (Stage 1 & 2) and Replications
- Tutorials/Guidelines & Policy/Regulatory Analyses
Peer Review & Reporting
Submissions undergo double-blind peer review by at least two experts. Authors should include appropriate reporting checklists (e.g., TRIPOD-AI, CONSORT-AI/SPIRIT-AI, PROBAST-AI) and provide details on data governance, inclusion/exclusion, missingness, external validation, and model maintenance plans.
Data, Privacy & Ethics
De-identification is required for any patient-level data. Where legal/contractual limits apply, provide synthetic samples or detailed descriptors enabling independent assessment. Ethics approvals and consent statements must be included where applicable. Share code and model cards when possible.
Open Access & Licensing
AJMLH is open access under a CC BY license. Authors retain copyright. See the APC policy for fees or waivers.
Indexing & Archiving
DOIs are registered via Crossref. Target indexing: Google Scholar, DOAJ, and Scopus (subject to eligibility). Long-term archiving via repository submissions and backups.
Contact
Editorial queries: editor@ajmlh.org | Submissions: submissions@ajmlh.org