Artificial Intelligence in Medicine Submission Guide

For each model input, ask whether it would actually exist at the moment the prediction is supposed to be made.

In our editorial analysis, the recurring failure pattern is retrospective foresight: the model uses variables that are present in the final database but were not available at the proposed decision time. The correction is to reconstruct the workflow clock, exclude future information, and state which users receive which output at which moment.

A useful paper can stop at technical validation if it labels that boundary honestly. Trouble begins when an abstract calls a model clinically useful but the study never tests a clinician, action threshold, workflow, or patient consequence.

Suppose a model predicts deterioration within 24 hours from electronic health records. The first evaluation randomly splits encounters and includes laboratory values recorded after escalation. It reports excellent discrimination.

The stronger design defines the prediction timestamp, excludes post-trigger variables, separates patients, uses a temporal test cohort, reports calibration and subgroup behavior, compares with the current clinical score, and analyzes alert burden at plausible thresholds. The conclusion becomes decision-specific: the model improves risk stratification in the tested retrospective setting, while prospective workflow benefit remains unproven.

1. define the intended use, user, population, setting, decision, and excluded uses;
2. document cohort construction, missingness, label generation, and data timing;
3. select reporting guidance appropriate to the design through the EQUATOR Network;
4. state ethics review, consent or waiver, privacy protection, data governance, and access conditions accurately;
5. report model version, preprocessing, tuning, evaluation, calibration, and failure analysis;
6. disclose funding, conflicts, contributor roles, and AI assistance under current policy;
7. reconcile the manuscript, supplement, code/data statement, and submission fields.

Reporting guidance is a design audit, not decoration. Use the guideline that matches the actual study rather than claiming compliance with every AI checklist.

Verify the current article type, file requirements, declarations, data statement, and submission route in the live journal guide. Then inspect the generated PDF and supplementary files for missing tables, broken references, unreadable figures, and discrepancies between the abstract and the evaluation.

Do not infer requirements from another Elsevier title. Shared publisher policies support the package, while the Artificial Intelligence in Medicine guide controls journal-specific instructions.

AIIM utility claimed without a decision threshold. A model can rank patients well while producing poorly calibrated probabilities or an unusable alert burden. State who acts, what threshold triggers action, which error matters most, and what current alternative the system would augment or replace.

AIIM validation performed after repeated test-set feedback. Once the test set guides model selection, feature engineering, threshold choice, or manuscript framing, it is no longer an untouched estimate. Preserve a final evaluation set or use nested procedures that match the development process. Report how often the evaluation data influenced decisions.

AIIM fairness reduced to subgroup tables. Subgroup performance is a starting point, not a conclusion. Explain why groups were selected, whether sample size supports estimation, which data or workflow mechanisms might create differences, and what action follows a detected disparity. Avoid biological interpretations of socially constructed categories without evidence.

Intended-use statement

Write a short statement containing every item below, then make the abstract and conclusion conform to it.

Deployment-readiness boundary

A retrospective model paper does not need to prove deployment if it does not claim deployment. It should, however, identify what remains between validation and use: prospective data flow, integration, silent testing, human factors, monitoring, update governance, privacy, security, and responsibility for errors. That map makes a technical paper more useful without pretending the product is clinically ready.

This source-backed synthesis cannot predict acceptance or editorial priority. The live Artificial Intelligence in Medicine guide controls submission details; the decision artifacts here help keep medical claims proportional to the study.