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ESMO Real World Data and Digital Oncology is a peer-reviewed journal published by Elsevier, covering Radiomics and Machine Learning in Medical Imaging, Artificial Intelligence in Healthcare and Education, AI in cancer detection.
| Publisher | Elsevier |
|---|---|
| Reference style | Numbered (Elsevier) Numbered — [1], [2] in the text [1] A. Smith, B. Jones, C. Lee, A representative article title, ESMO Real World Data and Digital Oncology 12 (2023) 45–58.
Formats any DOI in ESMO Real World Data and Digital Oncology style. No sign-up. |
| Publishes research in | Radiomics and Machine Learning in Medical Imaging Artificial Intelligence in Healthcare and Education AI in cancer detection Machine Learning in Healthcare Cancer Genomics and Diagnostics |
| ISSN | 2949-8201 |
| Citation impact (2-yr) | 0.67 |
| h-index | 10 |
| i10-index | 10 |
| Total citations | 431 |
| Open access | Yes |
| Top institutions publishing here | Fondazione IRCCS Istituto Nazionale dei Tumori |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
Recent advancements in health care digitalization opened the collection and availability of big data, whose analysis requires artificial intelligence-based technologies to facilitate the development of predictive tools supporting decision making in clinical practice. In this context, the idea of constructing ‘digital worlds' to evaluate the performance of such novel tools becomes more attractive. Digital twins…
•Real-world evidence in oncology is evolving rapidly with many particularities.•This guidance provides key recommendations for reporting real-world evidence studies in oncology.•Recommendations are based on a review of current evidence and the authors' collective expert opinion.•Authors are a multidisciplinary group of experts from different institutions and countries.•Guidance is provided for full article development, including title, introduction,…
Background: Large language models encode clinical knowledge and can answer medical expert questions out-of-the-box without further training. However, this zero-shot performance is limited by outdated training data and lack of explainability impeding clinical translation. We aimed to develop a urology-specialized chatbot (UroBot) and evaluate it against state-of-the-art models as well as historical urologists' performance in…
•AI in oncology: enhances diagnosis, personalizes treatment, improves outcomes.•AI challenges: balances technology with empathy, trust, and patient care ethics.•AI and decision making: influences choices, empowers patients, respects autonomy.•Patient-centered AI: prioritizes patient care, supports doctor–patient relationship. BackgroundThe integration of Artificial Intelligence (AI) in oncology is a developing field, impacting the doctor-patient relationship and the efficacy of…
Background Avelumab first-line (1L) maintenance is recommended as the standard of care for patients with locally advanced or metastatic urothelial carcinoma (la/mUC) without disease progression following 1L platinum-based chemotherapy (PBC). We report results from READY, a real-world study of avelumab 1L maintenance in an Italian compassionate use program (CUP). Patients and methods In this prospective,…
