Introduction The management of cancer care generates vast amounts of data, collected in the clinical registry; however, the interpretation of these unstandardized and heterogeneous records is often challenging. Artificial intelligence (AI) is emerging as a promising tool in healthcare, particularly in Oncology, where early diagnosis, complex therapies, long term follow-up and the impact on quality of life are paramount. This technology offers significant opportunities to support clinical decision-making and optimize the management of clinical data. Nevertheless, the literature describing how AI is concretely applied to support decision-making and administrative workflow in oncology remains scattered.

Objective The aim is to identify evidence regarding the use of AI in Oncology, focusing on its role in supporting clinical decision-making and technical procedures across the entire cancer care continuum.

Methods This scoping review will analyze primary studies published between 2020 and 2025. Eligibility criteria will be determined based on the PCC framework: Population (cancer patients), Concept (AI), and Context (clinical records). Studies focusing solely on population registries, those lacking identifiable AI use, as well as narrative reviews, will be excluded.

Relevant literature will be identified through a systematic search in MEDLINE, EMBASE and CENTRAL. Additionally, forward citation tracking and review of existing systematic reviews will be conducted. Two independent reviewers will screen articles by titles and abstracts and consequently by full-text, using RAYYAN software. Data extraction will include the following items: cancer type, specific AI technique, the model or tool used, clinical utility, primary recipient (patient or health professional), stage of implementation, and reported outcomes or limitations.

Quality assessment or bias evaluation will not be performed. Findings will be synthesized narratively and, where appropriate, represented graphically.

Potential Results This research is expected to provide a comprehensive synthesis of AI applications in Oncology, highlighting the current role of this technology in enhancing the management of medical records and supporting clinical decision-making in the cancer care continuum. Expected outcomes include evidence of an upward trend in recent publications on the subject, a classification of techniques and models, a catalog of approved AI-based tools, distinctions between patient-oriented and healthcare professional support tools, and the identification of key limitations.

Conclusion The findings of this review will enable clinicians and patients to gain insights into the current landscape of AI in Oncology, identify future research directions, and develop evidence-based strategies for the responsible integration of AI into cancer care. The goal is to position this technology as a supportive tool that complements, rather than replaces, healthcare professionals.

The authors have declared no competing interest.

This study did not receive any funding

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