Overview

JRAI publishes research that advances robotic surgery, medical AI, and intelligent healthcare systems. Manuscripts should demonstrate technical rigor, clinical relevance, and clear reporting of methods and results.

Authors should ensure the manuscript aligns with journal scope and includes evidence that supports translation to clinical or operational settings.

Article Types

We accept a range of manuscript formats to support diverse research outputs.

• Original research articles and validation studies
• Technical notes and system descriptions
• Systematic or scoping reviews
• Benchmark comparisons and dataset papers
• Clinical validation, pilot, and feasibility studies

Manuscript Structure

Use a structured format with clear headings. Present objectives, methods, results, and conclusions in a way that enables replication.

• Title page with author details and affiliations
• Structured abstract and 4 to 6 keywords
• Introduction with rationale and objectives
• Methods with datasets, hardware, software, and evaluation metrics
• Results with tables, figures, and statistical analysis
• Discussion with limitations, clinical implications, and future work

Formatting and Style

Manuscripts should be written in clear, technical English. Define all abbreviations and use consistent terminology across the manuscript.

Include units, device specifications, and software versions to support reproducibility.

Figures, Tables, and Media

Provide high resolution figures and clearly labeled tables. Visuals should demonstrate system architecture, workflow, and performance outcomes.

Include figure captions that explain clinical relevance and experimental conditions.

Data, Code, and Reproducibility

Describe datasets, preprocessing steps, model training procedures, and evaluation protocols. Provide repository links when possible.

If data cannot be shared, include a statement explaining restrictions and access conditions.

Ethics and Compliance

Human and animal studies must include ethics approval and informed consent details where applicable. Disclose funding sources, conflicts of interest, and device affiliations.

AI Specific Reporting

Include model architecture, training data composition, validation strategy, and bias assessment. Report generalizability and limitations for clinical deployment.

Submission Process

Use one of the two submission methods below. Provide a cover letter summarizing novelty, validation, and clinical relevance.

Peer Review and Decisions

JRAI uses independent peer review focused on technical integrity, clinical relevance, and reproducibility. Editors may request revisions to strengthen methodology or reporting clarity.

After Acceptance

Upon acceptance, authors receive production instructions and APC details. Proofs must be reviewed promptly to avoid delays.

Clinical and Engineering Impact

JRAI focuses on research that advances patient outcomes and technical performance. We emphasize reproducible methods, transparent reporting, and practical implementation insights for robotics and AI systems in surgery and healthcare.

Author Support

The editorial office provides guidance on scope, formatting, and compliance so research teams can move efficiently from submission to publication. Clear communication helps interdisciplinary teams align technical and clinical expectations.

Reporting Standards

Apply appropriate reporting frameworks such as CONSORT, STROBE, PRISMA, or TRIPOD when relevant. Clearly describe validation datasets and evaluation metrics.

System Safety and Validation

For robotic systems, describe safety mechanisms, failure modes, and verification protocols. Clinical claims must be supported by data and appropriate study design.

Revision Guidance

When revising, include a response to reviewers and highlight changes. Clear responses support efficient reevaluation.

Language and Clarity

Ensure the manuscript is accessible to both engineering and clinical readers. Define specialized terms and avoid ambiguous claims.

Abstract and Keywords

Provide a structured abstract that summarizes objectives, methods, results, and conclusions. Select 4 to 6 keywords that reflect the robotics, AI, and clinical context to support discovery.

Author Contributions and ORCID

Include a contribution statement describing each author role and provide ORCID identifiers when available. Clear attribution supports transparency and accountability.

References and Citation Style

Use consistent citation formatting and verify all references. Include DOIs where available to support long term discoverability and cross linking.

Supplementary Materials

Supplementary files may include additional figures, videos of robotic systems, or extended validation tables. Label files clearly and reference them in the manuscript.

Clinical Trial Registration

Prospective clinical trials should be registered in an accepted registry. Provide registration numbers and protocol links when applicable.

Statistical Reporting

Report statistical methods, confidence intervals, and significance thresholds. For AI models, include sensitivity, specificity, and calibration metrics where relevant.

Hardware and Software Details

List device specifications, sensors, software versions, and computing environments. These details support reproducibility and system evaluation.

Data Privacy and Security

Describe privacy safeguards, de identification methods, and data security controls for patient data or sensitive clinical records.

Cover Letter

Provide a brief cover letter summarizing novelty, validation approach, and clinical relevance. This helps editors assign appropriate reviewers quickly.

Proofs and Corrections

After acceptance, review proofs promptly and confirm accuracy of figures, captions, and author affiliations. Report corrections quickly to avoid delays.

File Formats and Submission Package

Submit editable source files in Word format along with separate high resolution figures. Provide tables in editable format and avoid embedding low resolution images.

Units and Nomenclature

Use standard units and provide clear definitions for all technical terms. Consistent nomenclature helps multidisciplinary readers follow complex methods.

Algorithm Training Details

Describe training, validation, and test splits, hyperparameters, and model selection criteria. Provide information about compute resources and runtime where relevant.

Clinical Workflow Context

Explain how the system integrates into clinical workflows, including user roles, decision support touchpoints, and safety checks.

Funding and Acknowledgements

List funding sources, institutional support, and acknowledgements. Transparent funding disclosure supports credibility and compliance.

Preprints and Prior Publication

If the manuscript has appeared as a preprint, disclose the repository and DOI. Prior presentation should be acknowledged to maintain transparency.

Conflict of Interest

Declare financial or non financial relationships that could influence interpretation. Disclosures protect trust in AI enabled clinical research.

Image Integrity

Do not manipulate figures in a way that alters meaning. Any enhancements must be applied uniformly and disclosed in the methods section.

Manuscript Length and Organization

Keep sections concise and focused. Use clear headings, numbered figures, and consistent terminology to help reviewers evaluate complex systems efficiently. Avoid repetitive text and focus on reproducible steps. Clear organization helps clinical readers understand technical choices and engineering readers see the clinical context. Use numbered subsections for complex pipelines and include diagrams when helpful where appropriate.

Videos and Demonstrations

Robotic system demonstrations or procedural videos can be submitted as supplementary materials. Provide short captions and ensure patient privacy protections are maintained.

Suggested Reviewers

Authors may suggest qualified reviewers with relevant expertise. The editorial office retains full discretion when selecting reviewers.

Ethical AI and Bias Mitigation

Describe fairness assessments, bias mitigation strategies, and limitations for AI systems used in clinical settings. Transparency supports responsible adoption.

Device Safety and Risk Controls

Include information on safety thresholds, fail safe mechanisms, and risk controls when describing robotic systems. This detail is critical for clinical evaluation.

Algorithm Explainability

Describe explainability methods, interpretability visualizations, or feature analysis used to support clinical trust. Explain how model outputs can be audited by clinicians.

Clinical Outcome Measures

Define primary and secondary outcomes clearly, including accuracy, precision, safety events, or workflow efficiency. Link outcomes to clinical relevance and patient impact.

Dataset Balance and Bias

Report dataset composition across demographics, device types, or clinical sites. Address imbalance and discuss how it may affect model generalizability.

Hardware Calibration and Testing

Provide calibration procedures, testing environments, and performance validation methods for robotic hardware. Clear testing descriptions support reproducibility and safety assurance.

Regulatory Documentation

If applicable, describe regulatory context, approvals, or standards compliance. This information supports translation to clinical settings and industry adoption.

Reproducibility Checklist

Before submission, confirm that datasets, code references, hardware details, and evaluation metrics are clearly described. A reproducibility checklist helps reviewers verify results efficiently.

Appendices and Extended Methods

Use appendices or supplementary sections for extended methods, additional experiments, or sensitivity analyses. These materials help readers validate findings without overloading the main text.

Quality Assurance

Review the manuscript for consistency across figures, tables, and text. Ensure that reported metrics match values in tables and that abbreviations are defined at first use.

Submission File Naming

Label files clearly with the manuscript title or corresponding author name. Clear file naming reduces delays during editorial checks and production.

Human Factors and Usability

If your study includes user testing, describe participant selection, task design, and usability metrics. These details help reviewers evaluate clinical workflow impact and safety.

Data Licensing and Access

Clarify data licensing terms and reuse permissions when sharing datasets or code. Transparent licensing supports ethical reuse and collaborative development.

Deployment and Monitoring

If your work includes deployment in clinical settings, describe monitoring plans, performance drift checks, and user training procedures. Deployment details help reviewers assess long term reliability and patient safety. Include rollout plans, user feedback loops, and post deployment audit processes where applicable.