Autonomous pathology research using agentic AI shows potential in oncology

The agentic artificial intelligence tool SPARK is able to reproduce pathology-based reasoning and produce biological hypotheses and relevant diagnostic, prognostic and predictive cellular parameters. The output of SPARK has the potential to advance the understanding of tumor biology and enable the development of diagnostic, prognostic and predictive tools for pathology and oncology. This is a preview of subscription content, access via your institution Access options Access Nature and 54 other Nature Portfolio journals Get Nature+, our best-value online-access subscription $32.99 / 30 days cancel any time Subscribe to this journal Receive 12 print issues and online access $259.00 per year only $21.58 per issue Buy this article - Purchase on SpringerLink - Instant access to the full article PDF. USD 39.95 Prices may be subject to local taxes which are calculated during checkout References Tolkach, Y. et al. High-accuracy prostate cancer pathology using deep learning. Nat. Mach. Intell. 2, 411–418 (2020). This paper showcases a diagnostic algorithm for tumor pathology. Vorontsov, E. et al. A foundation model for clinical-grade computational pathology and rare cancers detection. Nat. Med. 30, 2924–2935 (2024). This paper showcases foundational model-based approach to pathology algorithms. Kludt, C. et al. Next-generation lung cancer pathology: development and validation of diagnostic and prognostic algorithms. Cell Rep. Med. 5, 101697 (2024). This paper shows how handcrafted features at the tissue level can be used for advanced applications (prognosis). Mitchell Barroso, V. et al. Artificial intelligence-based single-cell analysis as a next-generation histologic grading approach in colorectal cancer: prognostic role and tumor biology assessment. Mod. Pathol. 38, 100771 (2025). This paper shows how handcrafted features at the single-cell level can be used for advanced applications (prognosis). Additional information Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This is a summary of: Trost, F. et al. An agentic framework for autonomous scientific discovery in cancer pathology. Nat. Med. https://doi.org/10.1038/s41591-026-04357-y (2026). Rights and permissions About this article Cite this article Autonomous pathology research using agentic AI shows potential in oncology. Nat Med (2026). https://doi.org/10.1038/s41591-026-04403-9 Published: Version of record: DOI: https://doi.org/10.1038/s41591-026-04403-9