Abstract Artificial intelligence has become a promising tool for supporting breast image interpretation, particularly mammography, because of its ability to analyse subtle patterns that may be difficult to distinguish visually. However, this capability presents a scientific paradox: the detected patterns may reflect disease-related changes, or they may represent technical characteristics or incidental associations that do not indicate cancer. This article uses the term “hidden-signature paradox” as an explanatory framework for this duality, rather than as an established diagnostic term. It discusses evidence supporting the value of artificial intelligence in detection, the risks of shortcut learning and automation bias, and the importance of clinical validation to ensure that computational accuracy translates into meaningful benefits for patients. Keywords: Breast imaging, mammography, artificial intelligence, deep learning, shortcut learning, automation bias, early detection. Introduction Studies indicate that artificial intelligence can support breast cancer detection, but understanding what a model learns remains essential. In a study developing a deep learning model, the system identified cancers in prior mammograms that had been interpreted clinically as negative. This finding supports the possibility that computationally extractable information may exist before signs become apparent during routine interpretation, without establishing that all systems perform better under every circumstance. This raises the central question of the article: does the model detect a subtle pathological sign, or does it rely on another signal associated with disease only within the training data? Research in medical artificial intelligence has shown that data acquisition characteristics can become predictive shortcuts that weaken performance when a model is transferred to a different setting. 1. What Is Meant by a Hidden Signature? A hidden signature can be understood as information within an image that a model uses in its computations, even though it may not appear to a human reader as a distinct, recognizable sign. This information may be medically useful. A study involving mammography and digital breast tomosynthesis demonstrated that a model could detect some cancers in prior images that had been interpreted clinically as negative. However, being hidden does not, by itself, give a signal pathological significance. A model may learn differences in contrast, image processing, or acquisition conditions and use them as substitutes for features relevant to the medical task. A high score may therefore arise from genuine pathological evidence or a misleading association, and these possibilities must be distinguished experimentally. 2. When Does Artificial Intelligence Add Value to Detection? The strongest evidence comes from studies that evaluate systems within actual screening workflows. In the randomized MASAI trial, artificial intelligence was used to triage examinations to single or double reading and to highlight suspicious findings. An analysis published in 2025 showed increased cancer detection and reduced image-reading workload compared with standard double reading. These findings apply to a particular system and integration strategy and should not be generalized to all artificial intelligence tools. A large implementation study within a German screening programme also found that AI-supported double reading was associated with a higher cancer detection rate without adversely affecting the recall rate for additional examinations. Its observational design provides important real-world evidence, while requiring recognition of its differences from randomized trials. 3. When a Signature Becomes a Misleading Shortcut Shortcut learning occurs when a model relies on an easily learned association within the data rather than on the features that should determine the presence of disease. If the image source or processing method is associated with different proportions of positive cases, the model may learn to identify the source and then use it to predict the diagnosis. A study across multiple medical data modalities documented how data acquisition bias can inflate performance estimates and weaken external generalization. In a preliminary mammography study, a model trained exclusively on images of view markers, without breast tissue, achieved discrimination above chance. This finding illustrates the possibility of extracting misleading information from elements outside the tissue. However, it was reported in a preprint and should be interpreted within that limitation. 4. Why Might Success Not Extend Beyond the Test Data? A model may appear accurate when its test data resemble its training data, yet its performance may decline when equipment, imaging protocols, or data collection pathways change. Such a decline becomes understandable if some predictions depend on local technical characteristics that are absent in the new hospital. Internal testing alone therefore does not necessarily establish a model’s suitability for broader use. The methodological implication is that a system’s performance should be evaluated in its intended deployment environment, with an analysis of the causes of error, rather than relying solely on an aggregate performance score that may conceal important differences between data sources. 5. When Machine Error Influences Clinical Judgment The risks extend beyond the model’s prediction: its recommendation may influence the physician’s interpretation of the image. The tendency to accept an automated system’s suggestion is known as automation bias. In an experiment published in Radiology in 2023, 27 radiologists interpreted mammograms with BI-RADS classification suggestions from a system presented to them as artificial intelligence. Some suggestions contained deliberately introduced errors. Readers across different experience levels were influenced by these suggestions, demonstrating that including a physician in the workflow does not automatically prevent error. The interaction between the physician and the system must also be evaluated. 6. How Can We Determine Whether a Signature Is Truly Useful? The evidence discussed above leads to several practical requirements for evaluating these systems: External validation: Testing performance using data from independent institutions and imaging environments. Shortcut assessment: Examining whether the model relies on breast tissue and relevant information, and investigating the effects of markers, image processing, and data sources. Prospective clinical evaluation: Measuring the system’s effects on detection, recall, and workload in actual practice. Human–AI team evaluation: Investigating when recommendations improve decisions and when they lead physicians into error. These requirements are derived from studies of generalization, clinical implementation, and automation bias. They help connect model performance with the safety of its use. Conclusion The hidden-signature paradox lies in the possibility that the same information-processing capabilities that allow artificial intelligence to detect subtle patterns may also enable it to learn misleading associations. High accuracy alone is therefore insufficient; the persistence of benefit across different environments and its effect on clinical decision-making must also be established. The evidence indicates considerable potential for supporting breast imaging, provided that the model and the way it is used are evaluated as integrated components of the healthcare system. Almustaqbal University – The First University in Iraq