Cited by 34

The Limitations of Artificial Intelligence in Head and Neck Oncology

Rao, Karthik N., Fernandez-Alvarez, Veronica, Guntinas-Lichius, Orlando, Sreeram, M. P., de Bree, Remco, Kowalski, Luiz P., Forastiere, Arlene, Pace-Asciak, Pia +9 more·2025·Advances in Therapy

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1007/s12325-025-03198-4

Cites

9 of 9 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • 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 measured
  • The 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”

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 51

  1. [2]Lee JC, Hamill CS, Shnayder Y, Buczek E, Kakarala K, Bur AM. Exploring the role of artificial intelligence chatbots in preoperative counseling for head and neck cancer surgery. Laryngoscope. 2024;134(6):2757–61. 38126511 PMC12374230 · DOI
  2. [3]Broggi G, Maniaci A, Lentini M, Palicelli A, Zanelli M, Zizzo M, et al. Artificial intelligence in head and neck cancer diagnosis: a comprehensive review with emphasis on radiomics, histopathological, and molecular applications. Cancers. 2024;16(21):3623. 39518063 PMC11545333 · DOI
  3. [4]Hassan M, Kushniruk A, Borycki E. Barriers to and facilitators of artificial intelligence adoption in health care: scoping review. JMIR Hum Factors. 2024;29(11): e48633. PMC11393514 39207831 · DOI · Paper page
  4. [6]Marchi F, Bellini E, Iandelli A, Sampieri C, Peretti G. Exploring the landscape of AI-assisted decision-making in head and neck cancer treatment: a comparative analysis of NCCN guidelines and ChatGPT responses. Eur Arch Oto-Rhino-Laryngol. 2024;281(4):2123–36. 38421392 · DOI
  5. [7]Rao KN, Arora RD, Dange P, Nagarkar NM. NLP AI models for optimizing medical research: demystifying the concerns. Indian J Surg Oncol. 2023;14(4):854–8. 38187847 PMC10767031 · DOI
  6. [8]Bansal P. Prompt engineering importance and applicability with generative AI. J Comput Commun. 2024;12(10):14–23. · DOI · Paper page
  7. [9]Neha F, Bhati D, Shukla DK, Amiruzzaman M. ChatGPT: transforming healthcare with AI. AI. 2024;5(4):2618–50. · DOI
  8. [11]Ahmed MI, Spooner B, Isherwood J, Lane M, Orrock E, Dennison A. A systematic review of the barriers to the implementation of artificial intelligence in healthcare. Cureus. 2023;15(10):e46454. 37927664 PMC10623210
  9. [12]Shephard AJ, Bashir RMS, Mahmood H, Jahanifar M, Minhas F, Raza SEA, et al. A fully automated and explainable algorithm for predicting malignant transformation in oral epithelial dysplasia. NPJ Precis Oncol. 2024;28(8):137. PMC11213925 38942998 · DOI
  10. [13]Dörrich M, Hecht M, Fietkau R, Hartmann A, Iro H, Gostian AO, et al. Explainable convolutional neural networks for assessing head and neck cancer histopathology. Diagn Pathol. 2023;18(1):121. 37924082 PMC10623808 · DOI
  11. [15]Chen RJ, Wang JJ, Williamson DFK, Chen TY, Lipkova J, Lu MY, et al. Algorithm fairness in artificial intelligence for medicine and healthcare. Nat Biomed Eng. 2023;7(6):719–42. 37380750 PMC10632090 · DOI · Paper page
  12. [16]Dekkers OM, von Elm E, Algra A, Romijn JA, Vandenbroucke JP. How to assess the external validity of therapeutic trials: a conceptual approach. Int J Epidemiol. 2010;39(1):89–94. 19376882 · DOI · Paper page
  13. [17]Willemink MJ, Koszek WA, Hardell C, Wu J, Fleischmann D, Harvey H, et al. Preparing medical imaging data for machine learning. Radiology. 2020;295(1):4–15. 32068507 PMC7104701 · DOI · Paper page
  14. [18]Merone L, Tsey K, Russell D, Nagle C. Sex inequalities in medical research: a systematic scoping review of the literature. Womens Health Rep. 2022;3(1):49–59. PMC8812498 35136877
  15. [19]Daitch V, Turjeman A, Poran I, Tau N, Ayalon-Dangur I, Nashashibi J, et al. Underrepresentation of women in randomized controlled trials: a systematic review and meta-analysis. Trials. 2022;23(1):1038. 36539814 PMC9768985 · DOI
  16. [20]Straw I, Wu H. Investigating for bias in healthcare algorithms: a sex-stratified analysis of supervised machine learning models in liver disease prediction. BMJ Health Care Inform. 2022;29(1):e100457. 35470133 PMC9039354 · DOI
  17. [21]Peters SAE, Woodward M. A roadmap for sex- and gender-disaggregated health research. BMC Med. 2023;13(21):354. PMC10500779 37704983 · DOI
  18. [22]Bretthauer M, Løberg M, Holme Ø, Adami HO, Kalager M. Deep learning and cancer biomarkers: recognising lead-time bias. The Lancet. 2021;397(10270):194. 33453776 · DOI
  19. [23]Özer M. Is artifical intelligence hallucinating? Turk J Psychiatry. 2024;35(4):333–5. PMC11681264 39398861
  20. [24]Khosravi M, Zare Z, Mojtabaeian SM, Izadi R. Artificial intelligence and decision-making in healthcare: a thematic analysis of a systematic review of reviews. Health Serv Res Manag Epidemiol. 2024;5(11):23333928241234864. PMC10916499 38449840
  21. [26]Zwaan L. Cognitive bias in large language models: implications for research and practice. NEJM AI. 2024;1(12):AIe2400961. · DOI
  22. [27]Ahmad A, Premanandan S, Cajander Å, Langegård U, Uereten E, Tiblom EY. A qualitative study with informal caregivers and healthcare professionals for individuals with head and neck cancer on the usage of AI Chatbots. Stud Health Technol Inform. 2024;22(316):751–5. 39176903
  23. [28]Siala H, Wang Y. SHIFTing artificial intelligence to be responsible in healthcare: a systematic review. Soc Sci Med. 2022;1(296): 114782. 35152047 · DOI · Paper page
  24. [29]Ronen O, Robbins KT, de Bree R, Guntinas-Lichius O, Hartl DM, Homma A, et al. Standardization for oncologic head and neck surgery. Eur Arch Oto-Rhino-Laryngol. 2021;278(12):4663–9. 33982178 · DOI
  25. [30]Shellenberger TD, Weber RS. Multidisciplinary team planning for patients with head and neck cancer. Oral Maxillofac Surg Clin N Am. 2018;30(4):435–44. 30173901 · DOI
  26. [31]Mäkitie AA, Alabi RO, Ng SP, Takes RP, Robbins KT, Ronen O, et al. Artificial intelligence in head and neck cancer: a systematic review of systematic reviews. Adv Ther. 2023;40(8):3360–80. 37291378 PMC10329964 · DOI
  27. [32]Temple SWP, Rowbottom CG. Gross failure rates and failure modes for a commercial ai-based auto-segmentation algorithm in head and neck cancer patients. J Appl Clin Med Phys. 2024;25(6): e14273. 38263866 PMC11163497 · DOI
  28. [33]Najjar R. Redefining radiology: a review of artificial intelligence integration in medical imaging. Diagnostics. 2023;13(17):2760. 37685300 PMC10487271 · DOI
  29. [34]Montezuma D, Oliveira SP, Tolkach Y, Boor P, Haragan A, Carvalho R, et al. Annotation practices in computational pathology: a European Society of Digital and Integrative Pathology (ESDIP) survey study. Lab Investig J Tech Methods Pathol. 2024;28: 102203. 39615882
  30. [35]Aldoseri A, Al-Khalifa KN, Hamouda AM. Re-thinking data strategy and integration for artificial intelligence: concepts, opportunities, and challenges. Appl Sci. 2023;13(12):7082. · DOI
  31. [36]Das SK, Dasgupta RK, Roy SD, Shil D. AI in Indian healthcare: from roadmap to reality. Intell Pharm. 2024;2(3):329–34.
  32. [37]Maleki Varnosfaderani S, Forouzanfar M. The role of AI in hospitals and clinics: transforming healthcare in the 21st Century. Bioengineering. 2024;11(4):337. 38671759 PMC11047988 · DOI
  33. [38]Petersson L, Larsson I, Nygren JM, Nilsen P, Neher M, Reed JE, et al. Challenges to implementing artificial intelligence in healthcare: a qualitative interview study with healthcare leaders in Sweden. BMC Health Serv Res. 2022;22(1):850. 35778736 PMC9250210 · DOI · Paper page
  34. [39]Brady AP, Allen B, Chong J, Kotter E, Kottler N, Mongan J, et al. Developing, purchasing, implementing and monitoring AI tools in radiology: practical considerations. A multi-society statement from the ACR, CAR, ESR, RANZCR and RSNA. Radiol Artif Intell. 2024;6(1):e230513. 38251899 PMC10831521 · DOI
  35. [41]Habli I, Lawton T, Porter Z. Artificial intelligence in health care: accountability and safety. Bull World Health Organ. 2020;98(4):251–6. 32284648 PMC7133468 · DOI · Paper page
  36. [42]Ledro C, Nosella A, Dalla PI. Integration of AI in CRM: challenges and guidelines. J Open Innov Technol Mark Complex. 2023;9(4): 100151. · DOI · Paper page
  37. [43]Gilbert S, Kather JN. Guardrails for the use of generalist AI in cancer care. Nat Rev Cancer. 2024;24(6):357–8. 38627556 · DOI
  38. [44]Gerke S, Minssen T, Cohen G. Ethical and legal challenges of artificial intelligence-driven healthcare. Artificial intelligence in healthcare. New York: Elsevier; 2020. p. 295–336.
  39. [45]Svempe L. Exploring impediments imposed by the medical device regulation EU 2017/745 on software as a medical device. JMIR Med Inform. 2024;5(12): e58080. PMC11413540 39235850 · DOI
  40. [46]Liu Y, Yu W, Dillon T. Regulatory responses and approval status of artificial intelligence medical devices with a focus on China. NPJ Digit Med. 2024;18(7):255. PMC11410966 39294318 · DOI
  41. [48]Alonso A, Siracuse JJ. Protecting patient safety and privacy in the Era of artificial intelligence. Semin Vasc Surg. 2023;36(3):426–9. 37863615 · DOI
  42. [49]Pham TD, Teh MT, Chatzopoulou D, Holmes S, Coulthard P. Artificial intelligence in head and neck cancer: innovations, applications, and future directions. Curr Oncol. 2024;31(9):5255–90. 39330017 PMC11430806 · DOI
  43. [50]Barbierato E, Gatti A. The challenges of machine learning: a critical review. Electronics. 2024;13(2):416. · DOI
  44. [51]Deshmukh A, Rao KN, Arora RD, Nagarkar NM, Singh A, Shetty OS. Molecular insights into oral malignancy. Indian J Surg Oncol. 2022;13(2):267–80. 35782825 PMC9240170 · DOI
  45. [52]Verlingue L, Boyer C, Olgiati L, Brutti Mairesse C, Morel D, Blay JY. Artificial intelligence in oncology: ensuring safe and effective integration of language models in clinical practice. Lancet Reg Health - Eur. 2024;1(46): 101064. PMC11406067 39290808 · DOI
  46. [53]Sorte SR, Rawekar A, Rathod SB. Understanding AI in healthcare: perspectives of future healthcare professionals. Cureus. 2024;16(8):e66285. 39238760 PMC11376319
  47. [54]Ueda D, Kakinuma T, Fujita S, Kamagata K, Fushimi Y, Ito R, et al. fairness of artificial intelligence in healthcare: review and recommendations. Jpn J Radiol. 2024;42(1):3–15. 37540463 PMC10764412 · DOI · Paper page
  48. [56]Alonso C, Berg A, Kothari S, Papageorgiou C, Rehman S. Will the AI revolution cause a great divergence? J Monet Econ. 2022;1(127):18–37. · DOI
  49. [57]Asaadi S, Martins KN, Lee MM, Pantoja JL. Artificial intelligence for the vascular surgeon. Semin Vasc Surg. 2023;36(3):394–400. 37863611 · DOI
  50. [59]Broekhuizen T, Dekker H, de Faria P, Firk S, Nguyen DK, Sofka W. AI for managing open innovation: opportunities, challenges, and a research agenda. J Bus Res. 2023;1(167): 114196. · DOI
  51. [60]Wolff RF, Moons KGM, Riley RD, Whiting PF, Westwood M, Collins GS, et al. PROBAST: a tool to assess the risk of bias and applicability of prediction model studies. Ann Intern Med. 2019;170(1):51–8. 30596875 · DOI · Paper page

Cited by

Sorted by citations

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.
Venue
Advances in Therapy
Published
2025-04-29
Volume
42
Issue
6
Pages
2559-2568
ISSN
  • 0741-238X
  • 1865-8652
Cited by
34
References
9
Language
en

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review