npj Digital Medicine / 2025

Convergence of evolving artificial intelligence and machine learning techniques in precision oncology

E. Fountzilas, T. Pearce, Mehmet A. Baysal, Abhijit Chakraborty, Apostolia M. Tsimberidou

Computer VisionFoundation ModelsLarge Language Models

The confluence of new technologies with artificial intelligence (AI) and machine learning (ML) analytical techniques is rapidly advancing the field of precision oncology, promising to improve diagnostic approaches and therapeutic strategies for patients with cancer. By analyzing multi-dimensional, multiomic, spatial pathology, and radiomic data, these technologies enable a deeper understanding of the intricate molecular pathways, aiding in the identification of critical nodes within the tumor’s biology to optimize treatment selection. The applications of AI/ML in precision oncology are extensive and include the generation of synthetic data, e.g., digital twins, in order to provide the necessary information to design or expedite the conduct of clinical trials. Currently, many operational and technical challenges exist related to data technology, engineering, and storage; algorithm development and structures; quality and quantity of the data and the analytical pipeline; data sharing and generalizability; and the incorporation of these technologies into the current clinical workflow and reimbursement models.

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Title: Convergence of evolving artificial intelligence and machine learning techniques in precision oncology
Authors: E. Fountzilas, T. Pearce, Mehmet A. Baysal, Abhijit Chakraborty, Apostolia M. Tsimberidou
Year: 2025
Venue: npj Digital Medicine
Categories: Computer Vision, Foundation Models, Large Language Models
Citations: 140
Paper URL: https://www.semanticscholar.org/paper/256ce5a88fe0323da1d812657aaa7fb1baf9921d
Open PDF: Not available

Abstract:
The confluence of new technologies with artificial intelligence (AI) and machine learning (ML) analytical techniques is rapidly advancing the field of precision oncology, promising to improve diagnostic approaches and therapeutic strategies for patients with cancer. By analyzing multi-dimensional, multiomic, spatial pathology, and radiomic data, these technologies enable a deeper understanding of the intricate molecular pathways, aiding in the identification of critical nodes within the tumor’s biology to optimize treatment selection. The applications of AI/ML in precision oncology are extensive and include the generation of synthetic data, e.g., digital twins, in order to provide the necessary information to design or expedite the conduct of clinical trials. Currently, many operational and technical challenges exist related to data technology, engineering, and storage; algorithm development and structures; quality and quantity of the data and the analytical pipeline; data sharing and generalizability; and the incorporation of these technologies into the current clinical workflow and reimbursement models.

Learning resources:
- Google Scholar: Google Scholar references (https://scholar.google.com/scholar?q=Convergence%20of%20evolving%20artificial%20intelligence%20and%20machine%20learning%20techniques%20in%20precision%20oncology)
- Papers with Code: Papers with Code search (https://paperswithcode.com/search?q=Convergence%20of%20evolving%20artificial%20intelligence%20and%20machine%20learning%20techniques%20in%20precision%20oncology)
- Semantic Scholar: Semantic Scholar paper page (https://www.semanticscholar.org/paper/256ce5a88fe0323da1d812657aaa7fb1baf9921d)
- YouTube: YouTube explanations (https://www.youtube.com/results?search_query=Convergence%20of%20evolving%20artificial%20intelligence%20and%20machine%20learning%20techniques%20in%20precision%20oncology+paper+explained)