Year: 2026
Type: article
Abstract: Background: Precision oncology depends on integrating molecular, imaging, pathologic, and clinical data to improve diagnosis, prognosis, treatment selection, and monitoring. AI has become a major computational approach for extracting clinically useful information from these heterogeneous data streams, but clinical translation has outpaced consensus about evidentiary standards, regulation, reimbursement, and implementation. [4] Methods of literature search: This manuscript is a structured narrative review, not a formal PRISMA systematic review. English-language sources were prioritized. Evidence was drawn from PubMed-indexed primary studies, high-impact journals, ClinicalTrials.gov, FDA device documentation, EMA and European Commission materials, NICE assessments, WHO guidance, and selected manufacturer disclosures when public regulatory detail for CE-marked products was otherwise limited. The search window emphasized January 2018 through June 23, 2026 to capture the modern period of deep learning, foundation models, ctDNA/methylation classifiers, and AI-specific regulatory guidance. Databases and sources included PubMed/MEDLINE, ClinicalTrials.gov, FDA, EMA, EUR-Lex, European Commission health/digital policy pages, WHO, and NICE. Keywords included combinations of artificial intelligence, machine learning, deep learning, radiomics, natural language processing, precision oncology, pathology, radiology, liquid biopsy, ctDNA, drug discovery, clinical trial matching, FDA, EMA, CE mark, and IVDR. Included studies were primary clinical validations, prospective trials, pivotal regulatory summaries, and major implementation or governance papers; excluded materials were non-English sources, duplicates, purely technical proof-of-concept studies without clinical oncology relevance unless foundational, and opinion pieces without new evidence. [5]
Institution: Elisabeth-Krankenhaus Essen
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
OpenAlex ID: W7166321803