AIMC Journal:
ESMO real world data and digital oncology

Showing 1 to 10 of 13 articles

AI-HOPE lung cancer: a multicenter real-world registry integrating artificial intelligence for metastatic non-small-cell lung cancer.

ESMO real world data and digital oncology Aug 3, 2026
BACKGROUND: The AI-HOPE Lung Cancer study is a multicenter initiative designed to integrate artificial intelligence (AI) and real-world data to improve outcome prediction in patients with metastatic non-small-cell lung cancer treated with first-line ...
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Automatic prediction of patients early discontinuation in oncology clinical trials.

ESMO real world data and digital oncology Jul 23, 2026
BACKGROUND: Early discontinuation (ED) in clinical trials (CTs) is frequent and deleterious for the patients, the care team, and the study duration. ED comprises screening failure or discontinuation during the first month of the treatment phase, and ...
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Artificial intelligence in oncology: empowering clinicians for responsible integration.

ESMO real world data and digital oncology Jul 14, 2026
Artificial intelligence (AI) is rapidly reshaping oncology, from diagnosis to treatment planning and clinical research. This perspective defines the oncologist in the era of AI as a clinician able to critically interpret, supervise, and communicate A...
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Corrigendum to 'Machine learning for prediction of 30-day mortality in patients with advanced cancer: comparing pan-cancer and single-cancer models' [ESMO Real World Data and Digital Oncology 8 (2025), 100146].

ESMO real world data and digital oncology Jun 30, 2026
[This corrects the article DOI: 10.1016/j.esmorw.2025.100146.].
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Development and evaluation of a large language model-based, retrieval-augmented generation application for query response in early oncology clinical trials.

ESMO real world data and digital oncology Jun 15, 2026
BACKGROUND: Early-phase oncology trials involve complex protocols and extensive documents, making timely resolution of study queries challenging. We developed the Study Document Assistant (SDA), a retrieval-augmented generation (RAG) system that inte...
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The evolving physician-AI relationship: a five-tier framework for integrating intelligent systems into clinical practice and medical education.

ESMO real world data and digital oncology May 20, 2026
Artificial intelligence (AI) is increasingly entering oncology, with systems demonstrating physician-comparable performance in selected tasks such as imaging interpretation, digital pathology analysis, and clinical documentation. However, limitations...
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Accelerating real-world data collection using large language models in rare neoplasms: a bone sarcoma example.

ESMO real world data and digital oncology Apr 17, 2026
BACKGROUND: Real-world data collection in oncology remains a challenge due to the complex and unstructured format of medical notes. Recently, large language models (LLMs) have demonstrated success in extracting information from free-text data across ...
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Estimate renal cell carcinoma recurrence rates using electronic health records.

ESMO real world data and digital oncology Apr 14, 2026
BACKGROUND: Lack of readily available recurrence data has limited the use of electronic health records (EHR) for risk assessment of cancer recurrence and optimal patient management. This study aims to derive high-quality EHR recurrence data and estim...
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Multi-site validation of an AI-based biomarker test for determining ER, PR, and HER2 status from H&E-stained breast cancer slides.

ESMO real world data and digital oncology Apr 14, 2026
BACKGROUND: The assessment of estrogen/progesterone receptors (ER/PR) and human epidermal growth factor receptor 2 (HER2) is essential for managing breast cancer (BC) patients, as these biomarkers guide targeted therapies. Traditional methods such as...
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Investigating fine-tuning versus zero-shot learning for general large language models when predicting cancer survival from initial oncology consultation documents.

ESMO real world data and digital oncology Apr 7, 2026
BACKGROUND: Unstructured oncology consultation notes contain rich clinical information that may support survival prediction. Open-weight large language models (LLMs) can utilize these notes with zero-shot inference or fine-tuning, but their relative ...
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