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.
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).
Nature Medicine published a clinical update in Oncology on 05 May 2026.
The item focuses on Autonomous pathology research using agentic AI shows potential in oncology.
Review the original article for the full source wording and details.