IBM Watson, heal thyself: How IBM overpromised and underdelivered on AI health care
Abstract
IN 2014, IBM OPENED swanky new headquarters for its artificial intelligence division, known as IBM Watson. Inside the glassy tower in lower Manhattan, IBMers can bring prospective clients and visiting journalists into the "immersion room," which resembles a miniature planetarium. There, in the darkened space, visitors sit on swiveling stools while fancy graphics flash around the curved screens covering the walls. It's the closest you can get, IBMers sometimes say, to being inside Watson's electronic brain.
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Many recommendations for treatment by Watson for Oncology have been shown to be incorrect and potentially harmful.
“For example, many of the recommendations for treatment by “Watson for Oncology” (IBM) have been shown to be incorrect and potentially harmful [30].”
From Current Challenges and Future Opportunities for XAI in Machine Learning-Based Clinical Decision Support Systems: A Systematic Review (2021) · cites this paper as [30]
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Parker, S. K., Ballard, T., Billinghurst, M., Collins, C. G., Dollard, M. F., Griffin, M. A., Johal, W., Jorritsma, K., Kowalkiewicz, M., Kyndt, E., et al. (2025) · Australian Journal of Management · doi:10.1177/03128962251331813 · Cited by 37 · see citing works
“…recorded an 80% failure rate in healthcare applications, with little or no positive impact on care and billions of dollars wasted, largely due to an inability to integrate the technology into the existing work systems and culture, including a failure to consider work design (Strickland, 2019).”
Peer perceptions of clinicians using generative AI in medical decision-making
Yang, H., Dai, T., Mathioudakis, N. N., Knight, A. M., Nakayasu, Y., & Wolf, R. M. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01901-x · Cited by 24 · see citing works
“IBM ’ s Watson represented a more recent attempt to augment medical decision-making through machine learning applications, particularly in oncology, but encountered mixed results due to data and implementationcomplexitiesandinconsistentclinicalimpact 10 .”
Artificial Intelligence and Machine Learning
Mich, L. (2020) · Springer eBooks · doi:10.1007/978-3-030-05324-6_25-1 · Cited by 30 · see citing works
Gao, D., Shenoy, R., Yi, S., Lee, J., Xu, M., Rong, Z., Deo, A., Nathan, D., Zheng, J., Williams, R. S., et al. (2023) · Advanced Materials · doi:10.1002/adma.202210484 · Cited by 22 · see citing works
A review of reinforcement learning for natural language processing and applications in healthcare
Liu, Y., Wang, H., Zhou, H., Li, M., Hou, Y., Zhou, S., Wang, F., Hoetzlein, R., & Zhang, R. (2024) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocae215 · Cited by 30 · see citing works
AI and the Future of Skills, Volume 2
OECD (2023) · Educational research and innovation · doi:10.1787/a9fe53cb-en · Cited by 38 · see citing works
Utilizing large language models for gastroenterology research: a conceptual framework
Berry, P., Dhanakshirur, R. R., & Khanna, S. (2025) · Therapeutic Advances in Gastroenterology · doi:10.1177/17562848251328577 · Cited by 21 · see citing works
AI agent in healthcare: applications, evaluations, and future directions
Zhao, L., Liu, S., Xin, T., Tan, J., Wang, X., Li, Y., Bian, Z., Chen, Y., Kong, F., Bian, J., et al. (2026) · npj Artificial Intelligence · doi:10.1038/s44387-026-00076-4 · Cited by 26 · see citing works
The Use of Artificial Intelligence in Health and Ethical Problems
Özdemir, L., & Bilgin, A. (2021) · Sağlık ve Hemşirelik Yönetimi Dergisi · doi:10.54304/shyd.2021.63325 · Cited by 23 · see citing works
““Watson Sağlık Platformu”, kılavuzlardan, uygulamalardan, tıbbi dergilerden ve ders kitaplarından elde ettiği bilgilerle hastanın tıbbi kaydındaki bilgileri değerlendirmektedir (Strickland, 2019).”
Can artificial intelligence improve cancer treatments?
Derbal, Y. (2022) · Health Informatics Journal · doi:10.1177/14604582221102314 · Cited by 21 · see citing works
Has machine learning over-promised in healthcare?
Ge, W., Lueck, C. J., Suominen, H., & Apthorp, D. (2023) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2023.102524 · Cited by 22 · see citing works
Applications and Future Prospects of Medical LLMs: A Survey Based on the M-KAT Conceptual Framework
Chang, Y., Yin, J., Li, J., Liu, C., Cao, L., & Lin, S. (2024) · Journal of Medical Systems · doi:10.1007/s10916-024-02132-5 · Cited by 19 · see citing works
“Preliminary attempts at AI medical assistance have revealed a profound disconnect between traditional machine learning and deep learning methods and the actual workflows of healthcare practitioners [37].”
Products of Positionality: How Tech Workers Shape Identity Concepts in Computer Vision
Scheuerman, M. K., & Brubaker, J. R. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3641890 · Cited by 20 · see citing works
“Computer vision, a specific domain of machine learning focused on visual pattern recognition, has, for better or worse, permeated social media (e.g., [2, 22]), art (e.g., [41, 74]), hiring ([37, 60]), health (e.g., [62, 70]), advertising (e.g., [35]) and more.”
BackgroundAbedi, M., Alshybani, I., Shahadat, M., & Murillo, M. S. (2023) · Qeios · doi:10.32388/md04b0.2 · Cited by 20 · see citing works
To err is human: Bias salience can help overcome resistance to medical AI
Isaac, M. S., Wang, R. J.-H., Napper, L. E., & Marsh, J. K. (2024) · Computers in Human Behavior · doi:10.1016/j.chb.2024.108402 · Cited by 21 · see citing works
A Survey on IBM Watson and Its Services
Kumar, A., Tejaswini, P., Nayak, O., Kujur, A. D., Gupta, R., Rajanand, A., & Sahu, M. (2022) · Journal of Physics Conference Series · doi:10.1088/1742-6596/2273/1/012022 · Cited by 23 · see citing works
“AI and analytics solution will collect and analyze information from its massive source of information to assist hospitals, healthcare providers and care givers learn and apply innovative models [7].”
BackgroundSelf-Learning Fuzzy Automaton With Input and Output Fuzzy Sets for System Modelling
Ying, H., & Lin, F. (2022) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2022.3192890 · Cited by 19 · see citing works
“This is partially because development of such a product is not only technically challenging but also risky and costly (e.g., failure of IBM’s 5-billion USD Watson project [40]).”
Background
- Authors
- Strickland, E.
- Affiliations
- Hacettepe University (TR)
- Corresponding author
- Strickland, Eliza
- Venue
- IEEE Spectrum
- Published
- 2019-04
- Volume
- 56
- Issue
- 4
- Pages
- 24-31
- ISSN
- 0018-9235 (ISSN-L)
- 1939-9340
- OpenAlex
- W2929028679
- Cited by
- 402
- References
- 0
- Citations by year
- 2026: 45 · 2025: 46 · 2024: 63 · 2023: 93 · 2022: 52 · 2021: 65 · 2020: 29 · 2019: 9
- FWCI
- 12.53
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Spectrum (publishedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Technology and Human Factors in Education and Health
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Keywords
- IBM Watson, artificial intelligence in healthcare
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