Integrating AI into the Clinical Workflows Across the Cancer Care Continuum: Opportunities and Challenges
Abstract
Cancer cases are projected to hit 35 million worldwide by 2050, posing a significant burden on health care systems. The cancer care continuum has evolved to precision medicine practices, provisioning personalized treatments based on multimodal and multiomics data. Contextual analysis of such diverse, voluminous, spatiotemporal patient data is beyond human cognitive capacity. Artificial Intelligence (AI) technologies are reshaping the data mining paradigm in healthcare by delivering novel data-led insights in real time. AI-based methods for cancer risk predictions, diagnosis, prognosis, and therapeutics are developed, validated, and approved, indicating readiness for integration in clinical workflows. Additional validation of AI models using real-world data representing diverse populations is recommended to address clinical, technical, regulatory, ethical, and legal challenges, along with trust issues. Integrating AI tools into cancer care workflows to augment clinical decision-making, without compromising clinical autonomy and patient safety, is essential to address the increasing demand for cancer care by 2050.
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AI-based decision support is most impactful when embedded within the EHR, where it can extract structured and unstructured data, identify missing clinical information, and surface relevant guideline recommendations at the point of care.
“AI-based decision support is most impactful when embedded within the electronic health record (EHR), where it can extract structured and unstructured data, identify missing clinical information, and surface relevant guideline recommendations at the point of care.23”
From Artificial Intelligence in Oncology: Practical Applications Across Clinical Care, Scholarship, and Translation. (2026) · cites this paper as [23]
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Hundal, J., Veettil, A. A. V., Chung, C., & Hosseini, M. (2026) · American Society of Clinical Oncology Educational Book · doi:10.1200/edbk-26-517280 · Cited by 1 · see citing works
- Authors
- Kulkarni Kale, U., & Vemulapalli, G.
- Affiliations
- Citadel Precision Medicine India (CPMI), Hyderabad, Telangana, India
- City Of Hope National Medical Center (US)
- Venue
- The Cancer Journal
- Published
- 2025-11
- Volume
- 31
- Issue
- 6
- ISSN
- 1528-9117 (ISSN-L)
- 1540-336X
- OpenAlex
- W4416319127
- Cited by
- 1
- References
- 9
- Citations by year
- 2026: 1
- FWCI
- 0.06
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- The Cancer Journal (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- artificial intelligence in healthcare, cancer care continuum, clinical workflow integration, precision oncology, cancer risk prediction, cancer diagnosis, cancer prognosis, AI model validation, real-world data, clinical decision support, patient safety, clinical autonomy, AI ethics and regulation
- MeSH terms
- Artificial Intelligence, Continuity of Patient Care, Humans, Neoplasms, Workflow, Precision Medicine
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