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The state of the art in artificial intelligence and digital pathology in prostate cancer

Nature Reviews Urology volume 23, pages 13–28 (2026) Cite this article

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Abstract

Prostate cancer is among the most common cancers worldwide, with ~1.5 million new diagnoses globally every year. The sheer mass of data becoming available on prostate cancer, as well as other types of cancer, is increasing exponentially. The growth of digital pathology has particularly sparked interest in developing artificial intelligence (AI) approaches to data synthesis to predict cancer grade and outcomes in men with prostate cancer. Progress has been made in this field, particularly in applications for diagnosis, prognosis and inferring molecular alterations, but several challenges remain. Variability in tissue processing and scanning contribute to dataset heterogeneity. The absence of well-annotated, multi-institutional databases hinders AI model development and generalization of model performances across clinical settings. Regulatory frameworks for AI-driven diagnostics remain nascent. Moreover, bias in training datasets skewing against under-represented demographic groups poses a fundamental challenge to developing equitable models. By mapping contemporary evidence around each of these hurdles and identifying tangible interventions, we can advance AI-augmented digital pathology towards reliable and generalizable tools to improve prostate cancer care.

Key points

  • Research output and computational developments in prostate cancer digital pathology are substantially increasing, reflecting growing academic and clinical interest in this field.

  • Artificial intelligence (AI)-powered prostate cancer detection systems show high clinical performance, substantially enhancing the diagnostic accuracy of pathologists while substantially reducing analysis time.

  • Deep learning algorithms successfully automate Gleason grading with pathologist-level agreement, showing value particularly in standardizing assessment of intermediate grades.

  • Digital pathology-enabled systems are prognostic and predictive of response to treatment, leading to improved counselling and care of patients with prostate cancer.

  • Advanced AI approaches effectively predict molecular alterations directly from standard haematoxylin and eosin (H&E)-stained slides, offering an efficient alternative to conventional molecular testing methods.

  • Regulatory frameworks remain in early development, with limited approved platforms in clinical use, highlighting the need for standardized validation protocols.

  • Data bias presents a fundamental challenge, and research examining AI performance across diverse populations is insufficient, emphasizing the crucial need for representative training datasets.

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Fig. 1: Digital pathology touch-points across the prostate cancer continuum.
Fig. 2: Steps in the workflow for digital pathology.
Fig. 3: Steps in the workflow for good machine learning practices.

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Acknowledgements

The authors thank M. Landry for his help with the figures and C. Wogan for her help with editing the manuscript.

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Author notes

  1. These authors contributed equally: Heyuan Michael Ni, Ramez Kouzy, Ali Sabbagh.

Authors and Affiliations

  1. Massachusetts Institute of Technology, Cambridge, MA, USA

    Heyuan Michael Ni

  2. MD Anderson Cancer Center, Houston, TX, USA

    Ramez Kouzy, Michael K. Rooney, Simon P. Castillo, Sherif M. Gadoue, Zakaria El Kouzi, Karen Hoffman, Yinyin Yuan & Osama Mohamad

  3. Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Ali Sabbagh

  4. University of California San Francisco, San Francisco, CA, USA

    Jean Feng

  5. Emory University, Atlanta, GA, USA

    Anant Madabhushi

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Contributions

O.M., H.M.N., A.S., R.K., J.F., S.P.C., Z.E.K., K.E.H., Y.Y. and A.M. researched data for the article. All authors contributed substantially to discussion of the content. All authors wrote the article. All authors reviewed and/or edited the manuscript before submission.

Corresponding author

Correspondence to Osama Mohamad.

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Competing interests

A.M. discloses several commercial and institutional relationships in the field of medical imaging and computational pathology. He maintains a leadership position at Inspirata and holds equity interests in both Inspirata and Elucid Bioimaging. His advisory activities include consulting roles with Merck, Aiforia, Roche, Caris Life Sciences and Cernostics, and he has received honoraria from AstraZeneca and Inspirata. His research programme benefits from institutional funding support from multiple industry partners, including Inspirata, Philips Healthcare, Bristol Myers Squibb, AstraZeneca and Boehringer Ingelheim. Additionally, A.M. reports intellectual property relationships, with patents licensed through his institution to both Inspirata Inc. and Elucid Bioimaging. All other authors declare no competing interests.

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Glossary

Algorithms

Sets of generic mathematical instructions to train a model on data to perform a task.

Area under the curve

A performance metric representing the probability that a model will rank a randomly chosen positive instance higher than a randomly chosen negative instance, measured using the receiver operating characteristic curve.

Artificial intelligence

The science of simulating human intelligence by computers or machines, encompassing broad capabilities including reasoning, learning and problem-solving.

Convolutional neural networks

Specific types of neural network designed to process grid-like data (such as images) through layers of convolutional filters.

Data augmentation

The process of generating new data by manipulating existing instances (zooming, cropping or rotating) to increase the diversity and amount of data available for training.

Deep learning

A specialized subset of machine learning that uses multiple layers of neural networks to analyse complex patterns and perform sophisticated tasks such as image classification and segmentation.

Digital pathology

The field of pathology that uses digital imaging technology to convert glass slides of tissue specimen samples into high-resolution digital images.

F1 score

A metric that combines precision and recall into a single value, calculated as 2 × (precision × recall)/(precision + recall), providing a balanced assessment of model performance.

Feature extraction

The process of deriving informative values (features) from images that can be used for machine learning tasks.

Feature selection

A methodological process of identifying and selecting the most relevant variables (features) from a dataset to optimize model performance and reduce computational complexity.

Generative adversarial networks

A type of deep learning model that can generate new data or transform existing data by having two neural networks compete against each other.

Ground truth

The verified, accurate reference data used to train and validate AI models, typically consisting of expert-annotated datasets in medical imaging applications.

Inter-observer variability

The degree of disagreement among different observers when evaluating the same data, particularly relevant in medical imaging annotation and diagnosis.

Kappa scores

A statistical measure that assesses the level of agreement between observers while accounting for the possibility of agreement occurring by chance, ranging from −1 to 1.

Machine learning

A subset of AI that automatically learns and improves from experience without explicit programming, using statistical methods to identify patterns in data.

Negative predictive value

The probability that a negative test result represents a true-negative (TN), calculated as TN/(TN + false-negative), indicating the reliability of negative predictions.

Neural networks

A method of machine learning which processes data simulating the human brain.

Positive predictive value

The probability that a positive test result represents a true-positive (TP), calculated as TP/(TP + false-positive), indicating the precision of positive predictions.

Receiver operating characteristic

A graph showing model performance at all classification thresholds by plotting the true-positive rate against the false-positive rate.

Sensitivity

The proportion of true-positive (TP) cases correctly identified by a model, calculated as TP/(TP + false-negative).

Specificity

The proportion of true-negative (TN) cases correctly identified by a model, calculated as TN/(TN + false-positive).

Stain normalization

A preprocessing technique that standardizes the colour and intensity variations in histological images.

Supervised learning

A type of machine learning where training is performed on annotated datasets, in which input features have corresponding output annotations.

Transfer learning

A machine learning method where a model developed for one task is reused as the starting point for a model on a second task.

Unsupervised learning

A type of machine learning where training is performed on unannotated datasets, where the model must find associations between the data by itself.

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Ni, H.M., Kouzy, R., Sabbagh, A. et al. The state of the art in artificial intelligence and digital pathology in prostate cancer. Nat Rev Urol 23, 13–28 (2026). https://doi.org/10.1038/s41585-025-01070-2

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