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Because identifying a disease in an image and locating the lesion are different tasks. A classifier can learn that an X-ray is associated with pneumonia, yet base that decision on a portable-device marker, a chest tube, or another correlated cue instead of the infected lung. A heatmap added afterward may then emphasize that cue rather than the pathology.
So a correct label does not prove a correct highlight. Localization must be trained, annotated, and tested as its own capability; a visually convincing saliency map is not a lesion boundary or a record of human-like reasoning.
Classification, localization and segmentation answer different questions
An image-level classifier answers what condition is present? It may return a disease label or probability for an entire radiograph, scan, or photograph. The training target is usually one or more image-level labels, not coordinates. If most images with a label also contain a particular scanner artifact, device, positioning pattern, or treatment marker, the network can use that association without learning the lesion itself.
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| Output | What the model is trained to do | What it can establish | What it cannot establish by itself |
|---|---|---|---|
| Image classification | Assign a label or score to the whole image | Whether the image resembles examples of the labeled condition | Which structure caused the score or whether a lesion was found |
| Post-hoc saliency map | Visualize image regions associated with a classifier output | Which pixels influenced a particular output under that method | That the highlighted area is the lesion, is sufficient for the decision, or is stable under testing |
| Detection/localization network | Predict lesion coordinates, points, or boxes | Where a finding is predicted, subject to annotation-based validation | Exact lesion extent unless the target and metric support it |
| Segmentation network | Predict a mask for each lesion or structure | Pixel- or voxel-level extent, measured against masks | Clinical importance or diagnosis without appropriate clinical validation |
The practical consequence is simple: classification accuracy and localization accuracy must be reported separately.
How a model gets the right diagnosis for the wrong reason
Correlated visual shortcuts
Deep-learning systems optimize predictive performance on their training distribution. They are not automatically required to use the feature a radiologist considers causal. A 2024 review describes pneumonia predictions associated with portable intensive-care markers and pneumothorax predictions associated with chest tubes. Those clues can correlate with the diagnosis because of how patients are treated or imaged, while failing to show the pathology itself. The review calls these shortcut signals a source of bias and recommends auditing data collection, labels, evaluation, subgroups, and deployment conditions (Radiology: Artificial Intelligence, 2024).
Image-level labels do not specify a lesion
Suppose every training image is labeled “pneumonia” but only some include a marked infected region. The loss function can reward any feature that separates pneumonia from non-pneumonia images. It has no reason to learn the exact lung opacity if another cue is easier and more consistent. Even when a disease usually has a visible lesion, weak labels leave the model free to distribute attention across unrelated regions.
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Heatmaps are explanations generated after the prediction
Methods such as Grad-CAM produce a visualization tied to a selected model output. They are useful for probing a network, but they are not direct photographs of its reasoning and do not automatically trace a lesion boundary. In the multicenter radiograph study, Grad-CAM examples sometimes failed to cover the intended lesion or highlighted false-positive areas despite strong image-level results (“Rethinking Annotation Granularity for Overcoming Shortcuts in Deep Learning–based Radiograph Diagnosis: A Multicenter Study,” 2022).
What published evaluations found
CheXNet: high classification, weak lesion finding
The multicenter study compared a chest-radiograph classifier, CheXNet, with models trained using finer-grained lesion annotations. The authors reported radiologist-level internal classification results for CheXNet but low performance in finding lesions and examples of unintended or false-positive highlighted areas. Their conclusion was that CheXNet relied heavily on patterns other than true pathologic signs. This is a study result on its datasets, not a claim that every classifier behaves identically.
The same work examined CheXDet, trained with more detailed annotations. In the reported experiments, finer-grained supervision generally improved focus on pathological patterns and some external comparisons. It did not eliminate failures: external performance was lower than internal performance in the paper’s examples, and detailed annotation increased labeling work. Annotation granularity can help, but it is not a universal cure (study report).
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Saliency methods can fail formal trustworthiness tests
Arun and colleagues evaluated eight saliency techniques on two public radiology datasets. They assessed properties including localization accuracy, sensitivity to model changes, repeatability, and reproducibility. Their conclusion states: “All eight saliency map techniques failed at least one of the criteria and were inferior in performance compared with localization networks.” That finding applies to the methods, datasets, and criteria in the 2021 evaluation; it is not evidence that every possible saliency approach fails in every modality.
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For pneumothorax, the study reported area under the precision-recall curve (AUPRC) values from 0.024 to 0.224 for the saliency methods, compared with 0.404 for a U-Net segmentation model. These are dataset-specific evaluation results, not expected clinical performance or a universal benchmark (“Assessing the Trustworthiness of Saliency Maps for Localizing Abnormalities in Medical Imaging,” 2021).
Robustness can break even when a map looks plausible
A 2024 retrospective evaluation, Revisiting the Trustworthiness of Saliency Methods in Radiology AI, analyzed 191,229 chest radiographs and 7,022 brain MR images. It reported weak sensitivity of saliency maps to subtle input perturbations and described a commercial prototype whose map could be irrelevant to changes in the model output. A separate article on automated musculoskeletal diagnoses likewise concluded that gradient-based saliency maps were not trustworthy visual explanations. Together, these studies show why an attractive example image is not enough; explanation behavior must be stress-tested (radiology robustness study; musculoskeletal saliency study).
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How to test whether a heatmap points to the lesion
Ask for evidence against expert annotations and model-behavior tests, not a gallery of plausible overlays. A useful evaluation plan includes the following checks.
- Localization against reference regions. Have radiologists mark the lesion with boxes, points, or masks, then calculate an agreed metric such as overlap or area under a precision-recall curve. Report confidence intervals and the cases in which no lesion was detected.
- Comparison with a model trained to locate. Evaluate the saliency map beside a detection or segmentation network trained on location labels. In the 2021 study, localization-network baselines outperformed the tested saliency methods.
- Model-parameter sensitivity. Randomize or reinitialize model weights in controlled tests. A useful explanation should change when the learned representation changes; a nearly unchanged map may be driven by image structure or the visualization algorithm rather than the trained decision.
- Output sensitivity. Alter inputs in ways that measurably change the model score and check whether the highlighted region changes accordingly. The converse test is also important: if the map changes but the output does not, the visualization may be unstable.
- Repeatability. Run the same case across seeds, batches, preprocessing choices, and repeated inference. Record how often the highlighted region remains consistent.
- Reproducibility across conditions. Test different hospitals, scanners, views, acquisition protocols, and patient subgroups. A map that works only on one site may be showing site-specific cues.
- Perturbation robustness. Apply small, clinically plausible changes such as mild noise, contrast shifts, or cropping. Large, unexplained map changes indicate that the visualization is fragile.
- Expert review of failure cases. Inspect false positives, false negatives, device-heavy images, portable studies, and cases with multiple findings. Aggregate accuracy can hide systematic shortcuts.
These checks do not turn a saliency map into proof of causation. They establish whether the visualization is useful and stable enough for the intended workflow.
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| Approach | Primary training target | Location evidence | Main trade-off |
|---|---|---|---|
| Image classifier | Image-level disease label or score | None unless separately validated | Usually less annotation effort, but shortcuts can go undetected |
| Classifier plus saliency | Classification; map generated afterward | Must be tested against annotations and robustness criteria | Easy to add to an existing model, but the map is not guaranteed to be faithful |
| Localization or detection network | Points, boxes, or regions | Directly measured against expert location labels | Needs location annotations and may not describe full lesion extent |
| Segmentation network such as U-Net | Pixel- or voxel-level mask | Direct mask comparison | Most detailed output, with the highest annotation and quality-control burden |
| Fine-grained classifier such as the study’s CheXDet setup | Diagnosis with lesion-level supervision | Improved localization in reported experiments | Still has residual and external-site failures; detailed labels cost time |
If a clinical decision depends on the exact location, train and validate a localization or segmentation output directly. Use a saliency map as an investigative aid, not as a substitute for that output.
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How developers can reduce wrong-area explanations
Audit the data-generating process
Document where images came from, how patients were selected, which devices and views were used, and whether treatment markers are unevenly distributed between labels. Check whether one hospital, scanner, ward, or acquisition protocol nearly identifies the diagnosis. Remove or balance avoidable shortcuts where possible, and test on a genuinely independent site.
Add location supervision where the task requires it
Use expert boxes, points, or masks for a representative set of cases. Define annotation rules for multifocal disease, uncertain borders, overlapping findings, and “no visible lesion.” Measure inter-reader agreement so that model disagreement is not confused with an impossible target. The multicenter study found benefits from finer-grained annotations, but its remaining failures show why annotation quality and external testing still matter.
Separate internal and external performance
Report classification, localization, and segmentation metrics separately for the development site and for outside institutions. Include subgroup analyses and confidence intervals. A high internal score can coexist with a drop in external performance when acquisition practices or patient populations change.
Make explanations part of validation, not decoration
Before deployment, predefine what a clinically acceptable map must show, which perturbation tests it must pass, and how reviewers will handle disagreement. Store the model version, preprocessing settings, output score, and explanation method for each case so that a changed map can be traced to a changed model or pipeline.
A practical checklist for reviewing a medical-AI heatmap
- Is the underlying model trained only for classification, or was it trained with lesion locations?
- Are highlighted regions compared with radiologist annotations on a held-out test set?
- Are localization metrics reported separately from label accuracy?
- Does the map respond to controlled changes in model weights and output?
- Is it repeatable across runs and preprocessing settings?
- Has it been tested on external hospitals, scanners, views, and patient subgroups?
- Do failure cases include devices, portable studies, multiple findings, and subtle disease?
- Is there a direct detection or segmentation output when a location is clinically required?
What this evidence does—and does not—tell us
There is no established population-level percentage for how often medical AI gets the disease label right while highlighting the wrong area. The published numbers above come from particular radiology datasets and evaluation designs. They should not be generalized automatically to ultrasound, pathology, dermatology, every imaging vendor, or every deployed system.
The defensible conclusion is narrower and more useful: a correct image-level prediction is not evidence of correct lesion localization. Correlated cues can produce right answers for the wrong reasons, and saliency maps can be unstable or misleading. When location matters, require location-aware supervision, annotation-based metrics, robustness testing, and external validation before treating a highlighted region as clinically meaningful.
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