The Limitations of Artificial Intelligence in Head and Neck Oncology
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Clinical adoption of multimodal XAI in oncology is constrained by limited interpretability and fragmented use of RWD.
“Multimodal explainable artificial intelligence (XAI) is transforming oncology, yet its clinical adoption is constrained by limited interpretability and fragmented use of real-world data (RWD), including electronic medical records, geospatial data, and social determinants of health.”
From X-CART: a multimodal real-world data pipeline for explainable AI-driven patient navigation in radiation oncology (2026) · cites this paper as [2]
The 'Lack of Understanding and Trust' section says opaque AI models impede clinical adoption, and the 'Data-Related Challenges' section describes inconsistent, poorly standardized datasets. The paper discusses XAI as a potential solution, but does not establish that multimodal XAI itself is constrained by fragmented use of real-world data, including electronic medical records, geospatial data, and social determinants of health. No page numbers are available in our copy. (LLM, conf=0.85)
Experimental — accuracy not yet measuredThe complexity of AI models can result in a 'black box' problem, in which lack of explanatory transparency can limit provider confidence and clinical adoption.
“However, the complexity of these models can result in a ‘black box’ problem, in which a lack of explanatory transparency driving recommendations can limit provider confidence in results and clinical adoption.2”
From X-CART: a multimodal real-world data pipeline for explainable AI-driven patient navigation in radiation oncology (2026) · cites this paper as [2]
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Ferrara, E. (2023) · Sci · doi:10.3390/sci6010003 · Cited by 875 · see citing works
Legal and human rights issues of AI: Gaps, challenges and vulnerabilities
Rodrigues, R. (2020) · Journal of Responsible Technology · doi:10.1016/j.jrt.2020.100005 · Cited by 494 · cited 1× · see citing works
“Key issues include informed consent, data ownership, algorithm accountability, and liability for errors [39, 40].”
Drawbacks of artificial intelligence and their potential solutions in the healthcare sector.
khan, B., Fatima, H., Qureshi, A., Kumar, S., Hanan, A., Hussain, J., & Abdullah, S. (2023) · Biomedical Materials & Devices · doi:10.1007/s44174-023-00063-2 · Cited by 461 · see citing works
Palaniappan, K., Lin, E. Y. T., & Vogel, S. (2024) · Healthcare · doi:10.3390/healthcare12050562 · Cited by 301 · see citing works
Artificial intelligence in oncology: current applications and future perspectives
Luchini, C., Pea, A., & Scarpa, A. (2022) · British Journal of Cancer · doi:10.1038/s41416-021-01633-1 · Cited by 281 · see citing works
Kiseleva, A., Kotzinos, D., & De Hert, P. (2022) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2022.879603 · Cited by 278 · see citing works
The Integration of Artificial Intelligence into Clinical Practice
Karalis, V. D. (2024) · Applied Biosciences · doi:10.3390/applbiosci3010002 · Cited by 255 · see citing works
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Zink, J. A., Rashid, R., Chinthala, L., Kheirinejad, S., White, B. M., Kheirkhah Rahimabad, P., Hashtarkhani, S., Kumsa, F. A., Brett, C. L., Davis, R. L., et al. (2026) · ESMO Real World Data and Digital Oncology · doi:10.1016/j.esmorw.2026.100771 · Cited by 1 · see citing works
- Authors
- Rao, K. N., Fernandez-Alvarez, V., Guntinas-Lichius, O., Sreeram, M. P., de Bree, R., Kowalski, L. P., Forastiere, A., Pace-Asciak, P., Rodrigo, J. P., Saba, N. F., Ronen, O., Florek, E., Randolph, G. W., Sanabria, A., Vermorken, J. B., Hanna, E. Y., & Ferlito, A.
- ORCID
- Rao, Karthik N.
- Venue
- Advances in Therapy
- Published
- 2025-04-29
- Volume
- 42
- Issue
- 6
- Pages
- 2559-2568
- ISSN
- 0741-238X
- 1865-8652
- PMC
- PMC12085315
- Cited by
- 34
- References
- 9
- Language
- en
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