Original Research

Original Research articles report complete, methodologically rigorous investigations that make a substantive contribution to augmented intelligence in medicine, artificial intelligence in healthcare, digital health, medical informatics, clinical decision support, medical imaging, prediction modelling, or related fields within the journal’s scope.

Suitable submissions include clinical prediction-model studies, diagnostic or prognostic accuracy studies, evaluations of AI-assisted clinical decision support, randomized or non-randomized clinical studies, observational studies, medical-imaging studies, natural-language-processing studies, human–AI collaboration research, health-information-system evaluations, fairness and bias studies, and real-world validation of healthcare technologies.

Pure algorithm-development studies without a clearly articulated clinical application, appropriate validation, or meaningful healthcare context are not suitable.

Original Research manuscripts must normally follow the IMRaD structure: Introduction, Methods, Results, Discussion, and Conclusion. A structured abstract of no more than 250 words is required.

Submissions must include all applicable statements concerning ethics approval, informed consent, clinical-trial registration, funding, conflicts of interest, data availability, CRediT authorship contributions, and use of generative artificial intelligence.

Manuscripts involving artificial intelligence, machine learning, prediction models, or generative AI must comply with the IJAMI AI-Methods Reporting Policy and must include the applicable reporting checklist.

Original Research manuscripts undergo double-anonymous peer review. Authors must upload a separate title page and an anonymized manuscript that does not contain author names, affiliations, acknowledgments, institutional identifiers, document properties, or other identifying information.

Each manuscript will normally be assessed by at least two independent reviewers. Manuscripts involving advanced statistics, prediction models, comparative-effectiveness analysis, or AI methodology may also be evaluated by a Statistical Editor or AI/Technical Editor.

Review

Review articles critically synthesize and evaluate existing evidence relevant to augmented intelligence, artificial intelligence in healthcare, digital health, medical informatics, clinical technology, and related fields.

IJAMI considers systematic reviews, meta-analyses, scoping reviews, umbrella reviews, evidence maps, rapid reviews, methodological reviews, state-of-the-art reviews, and selected narrative reviews.

Systematic and scoping reviews must use transparent and reproducible methods. Authors must describe the review question, eligibility criteria, information sources, search strategy, study-selection process, data-extraction procedures, risk-of-bias assessment, synthesis methods, and limitations.

Systematic reviews and meta-analyses must follow PRISMA 2020 or another applicable reporting guideline. Scoping reviews should follow PRISMA-ScR. Protocol registration in PROSPERO or another recognised registry is strongly encouraged where applicable.

Narrative reviews must provide a balanced, critical, and evidence-based synthesis. Authors must explain how the literature was identified and selected.

Reviews of AI or machine-learning studies should assess dataset representativeness, external validation, risk of bias, subgroup performance, fairness, reproducibility, clinical utility, human oversight, and implementation feasibility where relevant.

Review manuscripts undergo single-anonymous peer review. Reviewer identities are concealed from the authors, while author identities are available to reviewers. Each manuscript will normally be evaluated by at least two reviewers with relevant subject or evidence-synthesis expertise.

Implementation Report

Implementation Reports describe the real-world introduction, adaptation, evaluation, scaling, or discontinuation of augmented-intelligence, artificial-intelligence, digital-health, or health-information technologies in clinical and healthcare environments.

Implementation Reports are a signature IJAMI article format. The journal particularly welcomes reports from Southeast Asia, low- and middle-income countries, resource-constrained healthcare settings, rural and remote services, and tropical-medicine contexts.

Suitable submissions include implementation of clinical decision-support systems, AI-assisted diagnostic services, electronic health records, telemedicine, mobile health, remote monitoring, connected-health programmes, clinician or patient adoption, organisational readiness, workflow integration, change management, training, implementation costs, human factors, usability, safety monitoring, implementation failure, unintended consequences, and transition from pilot projects to routine clinical use.

The journal welcomes both successful and unsuccessful implementations, provided that the report presents transparent, evidence-based, and transferable lessons. Implementation Reports must not be promotional descriptions of commercial products or institutional programmes.

The manuscript should describe the setting, implementation need, intervention or technology, implementation strategy, stakeholder involvement, workflow integration, evaluation methods, outcomes, barriers and facilitators, safety and ethical considerations, lessons learned, sustainability, and scalability.

A structured abstract of no more than 300 words is required, using the headings: Setting, Intervention, Outcome, and Lessons.

Authors should use an appropriate implementation-science framework where applicable. Reports involving artificial intelligence or machine learning must comply with the IJAMI AI-Methods Reporting Policy.

Implementation Reports undergo single-anonymous peer review. Reviewer identities are concealed from authors, while author identities and institutional settings are available to reviewers because implementation context is essential to evaluation.

Each submission will normally be reviewed by at least two reviewers whose combined expertise covers the relevant clinical domain and implementation science, health services, digital health, or medical informatics. An AI/Technical Editor may be assigned where applicable.

Perspective

Perspective articles present a focused, scholarly, and evidence-informed viewpoint on an emerging issue, unresolved problem, conceptual development, ethical question, policy challenge, or future direction within the journal’s scope.

Suitable topics include human oversight of clinical AI, responsible AI governance, algorithmic fairness, health equity, data sovereignty, regulation of medical AI, AI implementation in low-resource settings, clinical accountability, patient perspectives, workforce preparation, methodological controversies, research priorities, tropical-medicine technology, and regional priorities for Southeast Asia.

Perspective articles should present a clear central argument supported by relevant evidence. Authors should acknowledge significant alternative viewpoints and clearly distinguish established evidence from interpretation, opinion, or proposal.

Perspective articles are not intended to report original research findings, provide systematic evidence synthesis, promote commercial products, publish institutional publicity, or present unsupported personal opinion.

Perspective submissions are reviewed internally by the Editor-in-Chief or a designated Section Editor. The editor evaluates relevance, originality, timeliness, quality of argument, appropriate use of evidence, consideration of competing viewpoints, ethical appropriateness, and conflicts of interest.

External expert advice may be obtained when the topic is highly technical, controversial, or outside the immediate expertise of the Editorial Board. Such consultation does not classify the article as externally peer reviewed.

Case Report

Case Reports describe clinically important, unusual, educational, or previously underreported cases involving augmented intelligence, artificial intelligence, digital health, connected medical technology, or clinical decision-support systems.

Suitable submissions may include an unexpected benefit or failure of an AI system, a clinically significant false-positive or false-negative result, human–AI disagreement affecting clinical management, algorithmic bias affecting a patient or population, a technology-related adverse event, an unusual application of clinical decision support, or a case demonstrating the importance of clinician oversight.

A Case Report must provide a clear educational contribution and must not merely describe use of a technology.

Case Reports should follow the CARE reporting guideline. Authors must upload a completed CARE checklist as a supplementary file.

The manuscript should normally include an Introduction, Patient Information, Clinical Findings, Timeline, Diagnostic Assessment, Technology or AI Involvement, Clinical Intervention, Outcome and Follow-up, Discussion, Patient Perspective where appropriate, Conclusion, and Informed-Consent Statement.

The report must clearly explain the intended function of the technology, how its output was interpreted, the role of the responsible clinician, whether the recommendation was followed or overridden, how the technology influenced care, and the generalisable clinical or patient-safety lesson.

Written informed consent for publication must be obtained from the patient or the patient’s legally authorised representative. Consent must cover publication of identifiable clinical information or images under the journal’s open-access licence. Authors must remove unnecessary identifying information.

Case Reports undergo single-anonymous peer review. Reviewer identities are concealed from authors, while author identities are available to reviewers.

Each manuscript will normally be reviewed by at least one relevant clinical specialist and, where applicable, one reviewer with expertise in artificial intelligence, digital health, informatics, ethics, or patient safety.