ArticleESMO real world data and digital oncology2026
Development and validation of a human-supervised AI-augmented living oncology evidence platform: a breast cancer pilot study.
Article in ESMO real world data and digital oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Oncologists face increasing difficulty staying current with rapidly evolving clinical data, guidelines, and regulatory updates. Building and maintaining an annotated clinical trial evidence library is time- and labor-intensive. To address this challenge, we developed and validated a living oncology evidence platform (Living-OEP) for breast cancer (BC), powered by an agentic artificial intelligence (AI) system that supports daily, human-conducted, AI-augmented systematic literature review (SLR). Materials and methods: The agentic AI system, incorporating GPT-4.1 and o3 (OpenAI) and Claude [Anthropic, PBC, San Francisco, CA] Sonnet-4 (Anthropic), was designed to emulate expert-led, Cochrane-compliant SLR workflows. Guided by a human-developed annotation manual, the system decomposes tasks, self-debugs, and validates outputs. Training data included 29 236 clinical trial abstracts across BC, lung, and prostate cancer, each annotated with four review and 32 extraction variables. Structured data were integrated with guideline-based treatment pathways, forming a real-time, evidence-linked OEP. Accuracy was benchmarked against 1997 human annotations. Living-OEP evidence quality was compared with AI chatbots (ChatGPT [OpenAI, San Francisco, CA], Perplexity [Perplexity AI, Inc., San Francisco, CA], Consensus [Consensus, Boston, MA], and OpenEvidence [OpenEvidence Inc., Miami, FL]) using six criteria across eight BC treatment scenarios. Results: The agentic AI review accuracy ranged from 95.1% to 97.2%. Extraction accuracy ranged from 51.1% to 99.4%, with three variables still undergoing iterative refinement. Compared with other AI tools, the Living-OEP provided more comprehensive and accurate evidence and linked all data to original publications and Food and Drug Administration labels. Conclusions: A human-conducted, AI-augmented living SLR integrated with guidelines and regulatory data can provide real-time evidence support. Future studies are required to evaluate its impact on physician workflows, clinical decision-making, and implementation in oncology practice.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.