WHY ENGLISH?
A medical-imaging AI score becomes interpretable when readers can trace the people, images, reference standard, partitions, thresholds, failures and testing relationship.
This guide follows CLAIM 2024 while keeping complete reporting separate from risk-of-bias appraisal, clinical benefit and deployment approval.
Begin with Identify the Medical Imaging AI Study.ROUTE 2 · SECTIONS 6–11Trace data and reference
Begin with Replace Ground Truth With an Honest Reference.ROUTE 3 · SECTIONS 12–17Read testing and results
Begin with Avoid the Ambiguous Word Validation.ROUTE 4 · SECTIONS 18–23Explain limits and audit
Begin with Report Subgroups Without Overclaiming Fairness.
Open the full article contents
- Identify the Medical Imaging AI Study
- State the Intended Use and Users
- Describe Participants and Recruitment
- Describe Imaging Acquisition
- Define the Reference Standard
- Replace Ground Truth With an Honest Reference
- Identify Model Version and Inputs
- Report Preprocessing and Augmentation
- Separate Patients Across Data Partitions
- Use Internal Testing Precisely
- Use External Testing Precisely
- Avoid the Ambiguous Word Validation
- Prespecify Thresholds and Operating Points
- Report Sample Size and Event Counts
- Show Participant, Examination and Image Flow
- Report Metrics With Uncertainty
- Analyse Errors and No-Results
- Report Subgroups Without Overclaiming Fairness
- Explain Every No and Not Applicable Response
- Discuss Generalisability and Clinical Translation
- Report Access, Ethics and Conflicts
- Did You Know? External Testing Is a Relationship
- Finish With a CLAIM 2024 Audit
CLAIM 2024 medical-imaging AI reporting matters because the word accuracy can hide several different studies. People searching for AI medical imaging, internal testing, external testing, reference standards or radiology AI reproducibility need the participants, imaging acquisition, patient-level partitions, model version, threshold, reference process, failed inputs and uncertainty.
The primary CLAIM 2024 update was published on 29 May 2024 and checked on 5 November 2026. The open full text explains major updates: Yes, No and Not Applicable responses; use of reference standard instead of ground truth; and clearer internal testing and external testing language because validation is ambiguous. The EQUATOR record confirms its role in medical-imaging AI reporting.
English matters because reference, test and exception are not minor word choices. They reveal what counted as correct, whether evaluation was independent and which checklist items do not fit the study. This article shows students, parents and future professionals how to read those claims carefully without treating any imaging result as personal medical advice.
Connect the report to eduKate’s STARD-AI diagnostic-accuracy guide, QUADAS-3 appraisal guide and CONSORT-AI trial guide.
Identify the Medical Imaging AI Study
Name modality, task, clinical role and study design in searchable language.
Read the imaging study question precisely
The CLAIM 2024 update was published on 29 May 2024 after a formal Delphi process involving a multidisciplinary panel. For identify the medical imaging ai study, state whether the system detects, classifies, segments, predicts, reconstructs, generates or supports workflow. Name the intended user and decision point. The same algorithm can have a different meaning when used for triage, diagnosis, measurement or research annotation.
Worked medical-imaging example
For identify the medical imaging ai study, a classifier is internally tested on held-out images from the same institution; calling this external validation would overstate transportability. Trace participants, examinations, images and outputs as separate counts. One person may contribute several images; one image may produce several regions; and the system may fail before an output exists. English keeps these denominators from collapsing into one impressive percentage.
Reference standard, testing and exceptions
Describe who established the reference, what information they could see and how disagreement was handled. Say internal testing when the held-out data come from the development source and external testing when they come from a genuinely different source. Under identify the medical imaging ai study, explain every No or Not Applicable response instead of treating the checklist as a decorative certificate. CLAIM is a reporting checklist, not a risk-of-bias score, regulatory decision or guarantee of safe clinical use.
What a careful reader does next
For identify the medical imaging ai study, compare the study population, imaging equipment, prevalence, workflow and threshold with the intended setting. Inspect intervals and representative errors, then ask what evidence would be required for real use. Students can borrow this sequence for any image-classification project; parents can use the lighter habit when an “AI scan” headline appears—what was the reference, who was tested, where, and what happened to failed cases?
State the Intended Use and Users
Explain who acts on the output and where it enters the pathway.
Read the imaging study question precisely
The update encourages authors to mark each item Yes, No or Not Applicable and give locations or explanations so omissions remain visible. For state the intended use and users, state whether the system detects, classifies, segments, predicts, reconstructs, generates or supports workflow. Name the intended user and decision point. The same algorithm can have a different meaning when used for triage, diagnosis, measurement or research annotation.
Worked medical-imaging example
For state the intended use and users, two radiologists disagree on the reference standard and later reach consensus; the report explains the process rather than calling the label unquestionable truth. Trace participants, examinations, images and outputs as separate counts. One person may contribute several images; one image may produce several regions; and the system may fail before an output exists. English keeps these denominators from collapsing into one impressive percentage.
Reference standard, testing and exceptions
Describe who established the reference, what information they could see and how disagreement was handled. Say internal testing when the held-out data come from the development source and external testing when they come from a genuinely different source. Under state the intended use and users, explain every No or Not Applicable response instead of treating the checklist as a decorative certificate. A reference standard may be imperfect; transparent reporting allows readers to judge its suitability and limitations.
What a careful reader does next
For state the intended use and users, compare the study population, imaging equipment, prevalence, workflow and threshold with the intended setting. Inspect intervals and representative errors, then ask what evidence would be required for real use. Students can borrow this sequence for any image-classification project; parents can use the lighter habit when an “AI scan” headline appears—what was the reference, who was tested, where, and what happened to failed cases?
Describe Participants and Recruitment
Show eligibility, setting, dates, prevalence and selection route.
Read the imaging study question precisely
CLAIM 2024 replaces the phrase ground truth with reference standard because the comparison method can itself have uncertainty or error. For describe participants and recruitment, state whether the system detects, classifies, segments, predicts, reconstructs, generates or supports workflow. Name the intended user and decision point. The same algorithm can have a different meaning when used for triage, diagnosis, measurement or research annotation.
Worked medical-imaging example
For describe participants and recruitment, images from one patient appear in both development and test partitions; patient-level separation is needed to prevent information leakage. Trace participants, examinations, images and outputs as separate counts. One person may contribute several images; one image may produce several regions; and the system may fail before an output exists. English keeps these denominators from collapsing into one impressive percentage.
Reference standard, testing and exceptions
Describe who established the reference, what information they could see and how disagreement was handled. Say internal testing when the held-out data come from the development source and external testing when they come from a genuinely different source. Under describe participants and recruitment, explain every No or Not Applicable response instead of treating the checklist as a decorative certificate. External testing at one institution does not prove performance across all hospitals, devices, populations or time periods.
What a careful reader does next
For describe participants and recruitment, compare the study population, imaging equipment, prevalence, workflow and threshold with the intended setting. Inspect intervals and representative errors, then ask what evidence would be required for real use. Students can borrow this sequence for any image-classification project; parents can use the lighter habit when an “AI scan” headline appears—what was the reference, who was tested, where, and what happened to failed cases?
Describe Imaging Acquisition
Report equipment, protocols, operators and site variation.
Read the imaging study question precisely
The update discourages the ambiguous word validation for data splits and recommends internal testing and external testing for clearer evaluation descriptions. For describe imaging acquisition, state whether the system detects, classifies, segments, predicts, reconstructs, generates or supports workflow. Name the intended user and decision point. The same algorithm can have a different meaning when used for triage, diagnosis, measurement or research annotation.
Worked medical-imaging example
For describe imaging acquisition, the threshold is selected after test results are seen, so sensitivity and specificity are partly tuned to the evaluation set rather than independently estimated. Trace participants, examinations, images and outputs as separate counts. One person may contribute several images; one image may produce several regions; and the system may fail before an output exists. English keeps these denominators from collapsing into one impressive percentage.
Reference standard, testing and exceptions
Describe who established the reference, what information they could see and how disagreement was handled. Say internal testing when the held-out data come from the development source and external testing when they come from a genuinely different source. Under describe imaging acquisition, explain every No or Not Applicable response instead of treating the checklist as a decorative certificate. Internal and external testing describe data relationships, not automatic quality labels; both still require appropriate design and analysis.
What a careful reader does next
For describe imaging acquisition, compare the study population, imaging equipment, prevalence, workflow and threshold with the intended setting. Inspect intervals and representative errors, then ask what evidence would be required for real use. Students can borrow this sequence for any image-classification project; parents can use the lighter habit when an “AI scan” headline appears—what was the reference, who was tested, where, and what happened to failed cases?
Define the Reference Standard
Explain how the comparison label was established and its limitations.
Read the imaging study question precisely
Medical imaging AI reporting should identify the clinical role, modality, acquisition setting, participant selection, data partitions and reference standard. For define the reference standard, state whether the system detects, classifies, segments, predicts, reconstructs, generates or supports workflow. Name the intended user and decision point. The same algorithm can have a different meaning when used for triage, diagnosis, measurement or research annotation.
Worked medical-imaging example
For define the reference standard, a system works on one scanner family but rejects images from another; equipment and failed-input counts matter to generalisability. Trace participants, examinations, images and outputs as separate counts. One person may contribute several images; one image may produce several regions; and the system may fail before an output exists. English keeps these denominators from collapsing into one impressive percentage.
Reference standard, testing and exceptions
Describe who established the reference, what information they could see and how disagreement was handled. Say internal testing when the held-out data come from the development source and external testing when they come from a genuinely different source. Under define the reference standard, explain every No or Not Applicable response instead of treating the checklist as a decorative certificate. Diagnostic or predictive performance does not by itself show that using the system improves decisions or patient outcomes.
What a careful reader does next
For define the reference standard, compare the study population, imaging equipment, prevalence, workflow and threshold with the intended setting. Inspect intervals and representative errors, then ask what evidence would be required for real use. Students can borrow this sequence for any image-classification project; parents can use the lighter habit when an “AI scan” headline appears—what was the reference, who was tested, where, and what happened to failed cases?
Replace Ground Truth With an Honest Reference
Use language that acknowledges disagreement and measurement error.
Read the imaging study question precisely
Model identity, inputs, preprocessing, architecture, training procedure, thresholds and evaluation methods should be clear enough to understand the tested system. For replace ground truth with an honest reference, state whether the system detects, classifies, segments, predicts, reconstructs, generates or supports workflow. Name the intended user and decision point. The same algorithm can have a different meaning when used for triage, diagnosis, measurement or research annotation.
Worked medical-imaging example
For replace ground truth with an honest reference, an item truly does not apply to a study, and the author explains why; Not Applicable is more transparent than leaving a blank cell. Trace participants, examinations, images and outputs as separate counts. One person may contribute several images; one image may produce several regions; and the system may fail before an output exists. English keeps these denominators from collapsing into one impressive percentage.
Reference standard, testing and exceptions
Describe who established the reference, what information they could see and how disagreement was handled. Say internal testing when the held-out data come from the development source and external testing when they come from a genuinely different source. Under replace ground truth with an honest reference, explain every No or Not Applicable response instead of treating the checklist as a decorative certificate. Images and metadata may be sensitive; transparency must remain consistent with consent, privacy, security and lawful access.
What a careful reader does next
For replace ground truth with an honest reference, compare the study population, imaging equipment, prevalence, workflow and threshold with the intended setting. Inspect intervals and representative errors, then ask what evidence would be required for real use. Students can borrow this sequence for any image-classification project; parents can use the lighter habit when an “AI scan” headline appears—what was the reference, who was tested, where, and what happened to failed cases?
Identify Model Version and Inputs
Name the evaluated system, input form and output.
Read the imaging study question precisely
Results need participant and image flow, uncertainty, errors, subgroup evidence and comparison conditions rather than one performance score. For identify model version and inputs, state whether the system detects, classifies, segments, predicts, reconstructs, generates or supports workflow. Name the intended user and decision point. The same algorithm can have a different meaning when used for triage, diagnosis, measurement or research annotation.
Worked medical-imaging example
For identify model version and inputs, average performance is high, but false-negative cases cluster in a rare presentation; case review and subgroup uncertainty change interpretation. Trace participants, examinations, images and outputs as separate counts. One person may contribute several images; one image may produce several regions; and the system may fail before an output exists. English keeps these denominators from collapsing into one impressive percentage.
Reference standard, testing and exceptions
Describe who established the reference, what information they could see and how disagreement was handled. Say internal testing when the held-out data come from the development source and external testing when they come from a genuinely different source. Under identify model version and inputs, explain every No or Not Applicable response instead of treating the checklist as a decorative certificate. Subgroup findings should not stigmatise people and need adequate samples, uncertainty and a plausible pathway for the observed difference.
What a careful reader does next
For identify model version and inputs, compare the study population, imaging equipment, prevalence, workflow and threshold with the intended setting. Inspect intervals and representative errors, then ask what evidence would be required for real use. Students can borrow this sequence for any image-classification project; parents can use the lighter habit when an “AI scan” headline appears—what was the reference, who was tested, where, and what happened to failed cases?
Report Preprocessing and Augmentation
Describe transformations that could alter what the model learns or sees.
Read the imaging study question precisely
Discussion should preserve limitations, intended use, external testing conditions, data or code access and the distance between a research result and clinical translation. For report preprocessing and augmentation, state whether the system detects, classifies, segments, predicts, reconstructs, generates or supports workflow. Name the intended user and decision point. The same algorithm can have a different meaning when used for triage, diagnosis, measurement or research annotation.
Worked medical-imaging example
For report preprocessing and augmentation, a student imaging project reports source images, reference labels, patient separation, preprocessing, test type, threshold and errors before claiming accuracy. Trace participants, examinations, images and outputs as separate counts. One person may contribute several images; one image may produce several regions; and the system may fail before an output exists. English keeps these denominators from collapsing into one impressive percentage.
Reference standard, testing and exceptions
Describe who established the reference, what information they could see and how disagreement was handled. Say internal testing when the held-out data come from the development source and external testing when they come from a genuinely different source. Under report preprocessing and augmentation, explain every No or Not Applicable response instead of treating the checklist as a decorative certificate. Use STARD-AI, TRIPOD+AI, CONSORT-AI or other current guidance when the study design brings additional reporting needs.
What a careful reader does next
For report preprocessing and augmentation, compare the study population, imaging equipment, prevalence, workflow and threshold with the intended setting. Inspect intervals and representative errors, then ask what evidence would be required for real use. Students can borrow this sequence for any image-classification project; parents can use the lighter habit when an “AI scan” headline appears—what was the reference, who was tested, where, and what happened to failed cases?
Separate Patients Across Data Partitions
Prevent repeated people or linked examinations from leaking into testing.
Read the imaging study question precisely
The CLAIM 2024 update was published on 29 May 2024 after a formal Delphi process involving a multidisciplinary panel. For separate patients across data partitions, state whether the system detects, classifies, segments, predicts, reconstructs, generates or supports workflow. Name the intended user and decision point. The same algorithm can have a different meaning when used for triage, diagnosis, measurement or research annotation.
Worked medical-imaging example
For separate patients across data partitions, a classifier is internally tested on held-out images from the same institution; calling this external validation would overstate transportability. Trace participants, examinations, images and outputs as separate counts. One person may contribute several images; one image may produce several regions; and the system may fail before an output exists. English keeps these denominators from collapsing into one impressive percentage.
Reference standard, testing and exceptions
Describe who established the reference, what information they could see and how disagreement was handled. Say internal testing when the held-out data come from the development source and external testing when they come from a genuinely different source. Under separate patients across data partitions, explain every No or Not Applicable response instead of treating the checklist as a decorative certificate. CLAIM is a reporting checklist, not a risk-of-bias score, regulatory decision or guarantee of safe clinical use.
What a careful reader does next
For separate patients across data partitions, compare the study population, imaging equipment, prevalence, workflow and threshold with the intended setting. Inspect intervals and representative errors, then ask what evidence would be required for real use. Students can borrow this sequence for any image-classification project; parents can use the lighter habit when an “AI scan” headline appears—what was the reference, who was tested, where, and what happened to failed cases?
Use Internal Testing Precisely
Reserve the phrase for held-out data from the development source.
Read the imaging study question precisely
The update encourages authors to mark each item Yes, No or Not Applicable and give locations or explanations so omissions remain visible. For use internal testing precisely, state whether the system detects, classifies, segments, predicts, reconstructs, generates or supports workflow. Name the intended user and decision point. The same algorithm can have a different meaning when used for triage, diagnosis, measurement or research annotation.
Worked medical-imaging example
For use internal testing precisely, two radiologists disagree on the reference standard and later reach consensus; the report explains the process rather than calling the label unquestionable truth. Trace participants, examinations, images and outputs as separate counts. One person may contribute several images; one image may produce several regions; and the system may fail before an output exists. English keeps these denominators from collapsing into one impressive percentage.
Reference standard, testing and exceptions
Describe who established the reference, what information they could see and how disagreement was handled. Say internal testing when the held-out data come from the development source and external testing when they come from a genuinely different source. Under use internal testing precisely, explain every No or Not Applicable response instead of treating the checklist as a decorative certificate. A reference standard may be imperfect; transparent reporting allows readers to judge its suitability and limitations.
What a careful reader does next
For use internal testing precisely, compare the study population, imaging equipment, prevalence, workflow and threshold with the intended setting. Inspect intervals and representative errors, then ask what evidence would be required for real use. Students can borrow this sequence for any image-classification project; parents can use the lighter habit when an “AI scan” headline appears—what was the reference, who was tested, where, and what happened to failed cases?
Use External Testing Precisely
Describe genuinely different institutions, populations, devices or periods.
Read the imaging study question precisely
CLAIM 2024 replaces the phrase ground truth with reference standard because the comparison method can itself have uncertainty or error. For use external testing precisely, state whether the system detects, classifies, segments, predicts, reconstructs, generates or supports workflow. Name the intended user and decision point. The same algorithm can have a different meaning when used for triage, diagnosis, measurement or research annotation.
Worked medical-imaging example
For use external testing precisely, images from one patient appear in both development and test partitions; patient-level separation is needed to prevent information leakage. Trace participants, examinations, images and outputs as separate counts. One person may contribute several images; one image may produce several regions; and the system may fail before an output exists. English keeps these denominators from collapsing into one impressive percentage.
Reference standard, testing and exceptions
Describe who established the reference, what information they could see and how disagreement was handled. Say internal testing when the held-out data come from the development source and external testing when they come from a genuinely different source. Under use external testing precisely, explain every No or Not Applicable response instead of treating the checklist as a decorative certificate. External testing at one institution does not prove performance across all hospitals, devices, populations or time periods.
What a careful reader does next
For use external testing precisely, compare the study population, imaging equipment, prevalence, workflow and threshold with the intended setting. Inspect intervals and representative errors, then ask what evidence would be required for real use. Students can borrow this sequence for any image-classification project; parents can use the lighter habit when an “AI scan” headline appears—what was the reference, who was tested, where, and what happened to failed cases?
Avoid the Ambiguous Word Validation
Say what was tested, where and on which independent data.
Read the imaging study question precisely
The update discourages the ambiguous word validation for data splits and recommends internal testing and external testing for clearer evaluation descriptions. For avoid the ambiguous word validation, state whether the system detects, classifies, segments, predicts, reconstructs, generates or supports workflow. Name the intended user and decision point. The same algorithm can have a different meaning when used for triage, diagnosis, measurement or research annotation.
Worked medical-imaging example
For avoid the ambiguous word validation, the threshold is selected after test results are seen, so sensitivity and specificity are partly tuned to the evaluation set rather than independently estimated. Trace participants, examinations, images and outputs as separate counts. One person may contribute several images; one image may produce several regions; and the system may fail before an output exists. English keeps these denominators from collapsing into one impressive percentage.
Reference standard, testing and exceptions
Describe who established the reference, what information they could see and how disagreement was handled. Say internal testing when the held-out data come from the development source and external testing when they come from a genuinely different source. Under avoid the ambiguous word validation, explain every No or Not Applicable response instead of treating the checklist as a decorative certificate. Internal and external testing describe data relationships, not automatic quality labels; both still require appropriate design and analysis.
What a careful reader does next
For avoid the ambiguous word validation, compare the study population, imaging equipment, prevalence, workflow and threshold with the intended setting. Inspect intervals and representative errors, then ask what evidence would be required for real use. Students can borrow this sequence for any image-classification project; parents can use the lighter habit when an “AI scan” headline appears—what was the reference, who was tested, where, and what happened to failed cases?
Prespecify Thresholds and Operating Points
Report when cut-offs were selected and which role they support.
Read the imaging study question precisely
Medical imaging AI reporting should identify the clinical role, modality, acquisition setting, participant selection, data partitions and reference standard. For prespecify thresholds and operating points, state whether the system detects, classifies, segments, predicts, reconstructs, generates or supports workflow. Name the intended user and decision point. The same algorithm can have a different meaning when used for triage, diagnosis, measurement or research annotation.
Worked medical-imaging example
For prespecify thresholds and operating points, a system works on one scanner family but rejects images from another; equipment and failed-input counts matter to generalisability. Trace participants, examinations, images and outputs as separate counts. One person may contribute several images; one image may produce several regions; and the system may fail before an output exists. English keeps these denominators from collapsing into one impressive percentage.
Reference standard, testing and exceptions
Describe who established the reference, what information they could see and how disagreement was handled. Say internal testing when the held-out data come from the development source and external testing when they come from a genuinely different source. Under prespecify thresholds and operating points, explain every No or Not Applicable response instead of treating the checklist as a decorative certificate. Diagnostic or predictive performance does not by itself show that using the system improves decisions or patient outcomes.
What a careful reader does next
For prespecify thresholds and operating points, compare the study population, imaging equipment, prevalence, workflow and threshold with the intended setting. Inspect intervals and representative errors, then ask what evidence would be required for real use. Students can borrow this sequence for any image-classification project; parents can use the lighter habit when an “AI scan” headline appears—what was the reference, who was tested, where, and what happened to failed cases?
Report Sample Size and Event Counts
Make precision and rare outcomes visible.
Read the imaging study question precisely
Model identity, inputs, preprocessing, architecture, training procedure, thresholds and evaluation methods should be clear enough to understand the tested system. For report sample size and event counts, state whether the system detects, classifies, segments, predicts, reconstructs, generates or supports workflow. Name the intended user and decision point. The same algorithm can have a different meaning when used for triage, diagnosis, measurement or research annotation.
Worked medical-imaging example
For report sample size and event counts, an item truly does not apply to a study, and the author explains why; Not Applicable is more transparent than leaving a blank cell. Trace participants, examinations, images and outputs as separate counts. One person may contribute several images; one image may produce several regions; and the system may fail before an output exists. English keeps these denominators from collapsing into one impressive percentage.
Reference standard, testing and exceptions
Describe who established the reference, what information they could see and how disagreement was handled. Say internal testing when the held-out data come from the development source and external testing when they come from a genuinely different source. Under report sample size and event counts, explain every No or Not Applicable response instead of treating the checklist as a decorative certificate. Images and metadata may be sensitive; transparency must remain consistent with consent, privacy, security and lawful access.
What a careful reader does next
For report sample size and event counts, compare the study population, imaging equipment, prevalence, workflow and threshold with the intended setting. Inspect intervals and representative errors, then ask what evidence would be required for real use. Students can borrow this sequence for any image-classification project; parents can use the lighter habit when an “AI scan” headline appears—what was the reference, who was tested, where, and what happened to failed cases?
Show Participant, Examination and Image Flow
Keep exclusions, failures and multiple images per person traceable.
Read the imaging study question precisely
Results need participant and image flow, uncertainty, errors, subgroup evidence and comparison conditions rather than one performance score. For show participant, examination and image flow, state whether the system detects, classifies, segments, predicts, reconstructs, generates or supports workflow. Name the intended user and decision point. The same algorithm can have a different meaning when used for triage, diagnosis, measurement or research annotation.
Worked medical-imaging example
For show participant, examination and image flow, average performance is high, but false-negative cases cluster in a rare presentation; case review and subgroup uncertainty change interpretation. Trace participants, examinations, images and outputs as separate counts. One person may contribute several images; one image may produce several regions; and the system may fail before an output exists. English keeps these denominators from collapsing into one impressive percentage.
Reference standard, testing and exceptions
Describe who established the reference, what information they could see and how disagreement was handled. Say internal testing when the held-out data come from the development source and external testing when they come from a genuinely different source. Under show participant, examination and image flow, explain every No or Not Applicable response instead of treating the checklist as a decorative certificate. Subgroup findings should not stigmatise people and need adequate samples, uncertainty and a plausible pathway for the observed difference.
What a careful reader does next
For show participant, examination and image flow, compare the study population, imaging equipment, prevalence, workflow and threshold with the intended setting. Inspect intervals and representative errors, then ask what evidence would be required for real use. Students can borrow this sequence for any image-classification project; parents can use the lighter habit when an “AI scan” headline appears—what was the reference, who was tested, where, and what happened to failed cases?
Report Metrics With Uncertainty
Define direction, threshold, interval and denominator.
Read the imaging study question precisely
Discussion should preserve limitations, intended use, external testing conditions, data or code access and the distance between a research result and clinical translation. For report metrics with uncertainty, state whether the system detects, classifies, segments, predicts, reconstructs, generates or supports workflow. Name the intended user and decision point. The same algorithm can have a different meaning when used for triage, diagnosis, measurement or research annotation.
Worked medical-imaging example
For report metrics with uncertainty, a student imaging project reports source images, reference labels, patient separation, preprocessing, test type, threshold and errors before claiming accuracy. Trace participants, examinations, images and outputs as separate counts. One person may contribute several images; one image may produce several regions; and the system may fail before an output exists. English keeps these denominators from collapsing into one impressive percentage.
Reference standard, testing and exceptions
Describe who established the reference, what information they could see and how disagreement was handled. Say internal testing when the held-out data come from the development source and external testing when they come from a genuinely different source. Under report metrics with uncertainty, explain every No or Not Applicable response instead of treating the checklist as a decorative certificate. Use STARD-AI, TRIPOD+AI, CONSORT-AI or other current guidance when the study design brings additional reporting needs.
What a careful reader does next
For report metrics with uncertainty, compare the study population, imaging equipment, prevalence, workflow and threshold with the intended setting. Inspect intervals and representative errors, then ask what evidence would be required for real use. Students can borrow this sequence for any image-classification project; parents can use the lighter habit when an “AI scan” headline appears—what was the reference, who was tested, where, and what happened to failed cases?
Analyse Errors and No-Results
Examine false positives, false negatives, failed images and recurrent patterns.
Read the imaging study question precisely
The CLAIM 2024 update was published on 29 May 2024 after a formal Delphi process involving a multidisciplinary panel. For analyse errors and no-results, state whether the system detects, classifies, segments, predicts, reconstructs, generates or supports workflow. Name the intended user and decision point. The same algorithm can have a different meaning when used for triage, diagnosis, measurement or research annotation.
Worked medical-imaging example
For analyse errors and no-results, a classifier is internally tested on held-out images from the same institution; calling this external validation would overstate transportability. Trace participants, examinations, images and outputs as separate counts. One person may contribute several images; one image may produce several regions; and the system may fail before an output exists. English keeps these denominators from collapsing into one impressive percentage.
Reference standard, testing and exceptions
Describe who established the reference, what information they could see and how disagreement was handled. Say internal testing when the held-out data come from the development source and external testing when they come from a genuinely different source. Under analyse errors and no-results, explain every No or Not Applicable response instead of treating the checklist as a decorative certificate. CLAIM is a reporting checklist, not a risk-of-bias score, regulatory decision or guarantee of safe clinical use.
What a careful reader does next
For analyse errors and no-results, compare the study population, imaging equipment, prevalence, workflow and threshold with the intended setting. Inspect intervals and representative errors, then ask what evidence would be required for real use. Students can borrow this sequence for any image-classification project; parents can use the lighter habit when an “AI scan” headline appears—what was the reference, who was tested, where, and what happened to failed cases?
Report Subgroups Without Overclaiming Fairness
Use relevant groups, adequate counts and contextual interpretation.
Read the imaging study question precisely
The update encourages authors to mark each item Yes, No or Not Applicable and give locations or explanations so omissions remain visible. For report subgroups without overclaiming fairness, state whether the system detects, classifies, segments, predicts, reconstructs, generates or supports workflow. Name the intended user and decision point. The same algorithm can have a different meaning when used for triage, diagnosis, measurement or research annotation.
Worked medical-imaging example
For report subgroups without overclaiming fairness, two radiologists disagree on the reference standard and later reach consensus; the report explains the process rather than calling the label unquestionable truth. Trace participants, examinations, images and outputs as separate counts. One person may contribute several images; one image may produce several regions; and the system may fail before an output exists. English keeps these denominators from collapsing into one impressive percentage.
Reference standard, testing and exceptions
Describe who established the reference, what information they could see and how disagreement was handled. Say internal testing when the held-out data come from the development source and external testing when they come from a genuinely different source. Under report subgroups without overclaiming fairness, explain every No or Not Applicable response instead of treating the checklist as a decorative certificate. A reference standard may be imperfect; transparent reporting allows readers to judge its suitability and limitations.
What a careful reader does next
For report subgroups without overclaiming fairness, compare the study population, imaging equipment, prevalence, workflow and threshold with the intended setting. Inspect intervals and representative errors, then ask what evidence would be required for real use. Students can borrow this sequence for any image-classification project; parents can use the lighter habit when an “AI scan” headline appears—what was the reference, who was tested, where, and what happened to failed cases?
Explain Every No and Not Applicable Response
Turn checklist exceptions into visible reasoning.
Read the imaging study question precisely
CLAIM 2024 replaces the phrase ground truth with reference standard because the comparison method can itself have uncertainty or error. For explain every no and not applicable response, state whether the system detects, classifies, segments, predicts, reconstructs, generates or supports workflow. Name the intended user and decision point. The same algorithm can have a different meaning when used for triage, diagnosis, measurement or research annotation.
Worked medical-imaging example
For explain every no and not applicable response, images from one patient appear in both development and test partitions; patient-level separation is needed to prevent information leakage. Trace participants, examinations, images and outputs as separate counts. One person may contribute several images; one image may produce several regions; and the system may fail before an output exists. English keeps these denominators from collapsing into one impressive percentage.
Reference standard, testing and exceptions
Describe who established the reference, what information they could see and how disagreement was handled. Say internal testing when the held-out data come from the development source and external testing when they come from a genuinely different source. Under explain every no and not applicable response, explain every No or Not Applicable response instead of treating the checklist as a decorative certificate. External testing at one institution does not prove performance across all hospitals, devices, populations or time periods.
What a careful reader does next
For explain every no and not applicable response, compare the study population, imaging equipment, prevalence, workflow and threshold with the intended setting. Inspect intervals and representative errors, then ask what evidence would be required for real use. Students can borrow this sequence for any image-classification project; parents can use the lighter habit when an “AI scan” headline appears—what was the reference, who was tested, where, and what happened to failed cases?
Discuss Generalisability and Clinical Translation
Separate research performance from workflow benefit and deployment readiness.
Read the imaging study question precisely
The update discourages the ambiguous word validation for data splits and recommends internal testing and external testing for clearer evaluation descriptions. For discuss generalisability and clinical translation, state whether the system detects, classifies, segments, predicts, reconstructs, generates or supports workflow. Name the intended user and decision point. The same algorithm can have a different meaning when used for triage, diagnosis, measurement or research annotation.
Worked medical-imaging example
For discuss generalisability and clinical translation, the threshold is selected after test results are seen, so sensitivity and specificity are partly tuned to the evaluation set rather than independently estimated. Trace participants, examinations, images and outputs as separate counts. One person may contribute several images; one image may produce several regions; and the system may fail before an output exists. English keeps these denominators from collapsing into one impressive percentage.
Reference standard, testing and exceptions
Describe who established the reference, what information they could see and how disagreement was handled. Say internal testing when the held-out data come from the development source and external testing when they come from a genuinely different source. Under discuss generalisability and clinical translation, explain every No or Not Applicable response instead of treating the checklist as a decorative certificate. Internal and external testing describe data relationships, not automatic quality labels; both still require appropriate design and analysis.
What a careful reader does next
For discuss generalisability and clinical translation, compare the study population, imaging equipment, prevalence, workflow and threshold with the intended setting. Inspect intervals and representative errors, then ask what evidence would be required for real use. Students can borrow this sequence for any image-classification project; parents can use the lighter habit when an “AI scan” headline appears—what was the reference, who was tested, where, and what happened to failed cases?
Report Access, Ethics and Conflicts
Describe data, code, model, consent, governance and interests within lawful limits.
Read the imaging study question precisely
Medical imaging AI reporting should identify the clinical role, modality, acquisition setting, participant selection, data partitions and reference standard. For report access, ethics and conflicts, state whether the system detects, classifies, segments, predicts, reconstructs, generates or supports workflow. Name the intended user and decision point. The same algorithm can have a different meaning when used for triage, diagnosis, measurement or research annotation.
Worked medical-imaging example
For report access, ethics and conflicts, a system works on one scanner family but rejects images from another; equipment and failed-input counts matter to generalisability. Trace participants, examinations, images and outputs as separate counts. One person may contribute several images; one image may produce several regions; and the system may fail before an output exists. English keeps these denominators from collapsing into one impressive percentage.
Reference standard, testing and exceptions
Describe who established the reference, what information they could see and how disagreement was handled. Say internal testing when the held-out data come from the development source and external testing when they come from a genuinely different source. Under report access, ethics and conflicts, explain every No or Not Applicable response instead of treating the checklist as a decorative certificate. Diagnostic or predictive performance does not by itself show that using the system improves decisions or patient outcomes.
What a careful reader does next
For report access, ethics and conflicts, compare the study population, imaging equipment, prevalence, workflow and threshold with the intended setting. Inspect intervals and representative errors, then ask what evidence would be required for real use. Students can borrow this sequence for any image-classification project; parents can use the lighter habit when an “AI scan” headline appears—what was the reference, who was tested, where, and what happened to failed cases?
Did You Know? External Testing Is a Relationship
It describes how test data differ from development data, not a magic quality stamp.
Read the imaging study question precisely
Model identity, inputs, preprocessing, architecture, training procedure, thresholds and evaluation methods should be clear enough to understand the tested system. For did you know? external testing is a relationship, state whether the system detects, classifies, segments, predicts, reconstructs, generates or supports workflow. Name the intended user and decision point. The same algorithm can have a different meaning when used for triage, diagnosis, measurement or research annotation.
Worked medical-imaging example
For did you know? external testing is a relationship, an item truly does not apply to a study, and the author explains why; Not Applicable is more transparent than leaving a blank cell. Trace participants, examinations, images and outputs as separate counts. One person may contribute several images; one image may produce several regions; and the system may fail before an output exists. English keeps these denominators from collapsing into one impressive percentage.
Reference standard, testing and exceptions
Describe who established the reference, what information they could see and how disagreement was handled. Say internal testing when the held-out data come from the development source and external testing when they come from a genuinely different source. Under did you know? external testing is a relationship, explain every No or Not Applicable response instead of treating the checklist as a decorative certificate. Images and metadata may be sensitive; transparency must remain consistent with consent, privacy, security and lawful access.
What a careful reader does next
For did you know? external testing is a relationship, compare the study population, imaging equipment, prevalence, workflow and threshold with the intended setting. Inspect intervals and representative errors, then ask what evidence would be required for real use. Students can borrow this sequence for any image-classification project; parents can use the lighter habit when an “AI scan” headline appears—what was the reference, who was tested, where, and what happened to failed cases?
Finish With a CLAIM 2024 Audit
Check reference, partitions, testing language, flow, metrics, errors and exceptions.
Read the imaging study question precisely
Results need participant and image flow, uncertainty, errors, subgroup evidence and comparison conditions rather than one performance score. For finish with a claim 2024 audit, state whether the system detects, classifies, segments, predicts, reconstructs, generates or supports workflow. Name the intended user and decision point. The same algorithm can have a different meaning when used for triage, diagnosis, measurement or research annotation.
Worked medical-imaging example
For finish with a claim 2024 audit, average performance is high, but false-negative cases cluster in a rare presentation; case review and subgroup uncertainty change interpretation. Trace participants, examinations, images and outputs as separate counts. One person may contribute several images; one image may produce several regions; and the system may fail before an output exists. English keeps these denominators from collapsing into one impressive percentage.
Reference standard, testing and exceptions
Describe who established the reference, what information they could see and how disagreement was handled. Say internal testing when the held-out data come from the development source and external testing when they come from a genuinely different source. Under finish with a claim 2024 audit, explain every No or Not Applicable response instead of treating the checklist as a decorative certificate. Subgroup findings should not stigmatise people and need adequate samples, uncertainty and a plausible pathway for the observed difference.
What a careful reader does next
For finish with a claim 2024 audit, compare the study population, imaging equipment, prevalence, workflow and threshold with the intended setting. Inspect intervals and representative errors, then ask what evidence would be required for real use. Students can borrow this sequence for any image-classification project; parents can use the lighter habit when an “AI scan” headline appears—what was the reference, who was tested, where, and what happened to failed cases?
Frequently Asked Questions
What is CLAIM 2024?
It is the 2024 update of the Checklist for Artificial Intelligence in Medical Imaging, intended to improve complete and consistent study reporting.
Why replace ground truth with reference standard?
A comparison method can contain disagreement, uncertainty or error. Reference standard is more accurate and invites its limitations to be reported.
What is internal testing?
Testing on held-out data from the same source or institution as development, when kept independent of model choices.
What is external testing?
Testing on data from a genuinely different source, such as another institution, population, device environment or period.
Why explain Not Applicable?
The explanation shows that the item was considered and why it does not fit, instead of leaving an ambiguous blank.
Does CLAIM prove clinical usefulness?
No. It improves reporting; workflow benefit, patient outcomes, safety and deployment require appropriate additional evidence.
Useful Next Reading
- Read the open CLAIM 2024 update.
- Check the current EQUATOR CLAIM record.
- Connect imaging reports to STARD-AI diagnostic accuracy.
- Appraise diagnostic-accuracy bias with QUADAS-3.
Closing thought: CLAIM 2024 shows why English matters in imaging AI: precise terms keep the reference, partitions, testing conditions, exceptions and limits visible beside every performance number.