Artificial Intelligence (AI) and Machine Learning (ML) have captured the imagination of healthcare innovators worldwide, promising precision diagnostics, individualized therapies, and operational efficiencies. Yet, this transformative potential is shadowed by a critical limitation—the black box phenomenon. In simple terms, black-box AI refers to models that can make highly accurate predictions or decisions, but whose internal logic is opaque even to their developers. When applied to medicine, this lack of explainability raises significant ethical, clinical, and legal concerns.
The failure of much-hyped IBM Watson for Oncology is the most telling case of ambition colliding with opacity.
Understanding the Black Box in Medical AI
Deep learning algorithms analyse enormous datasets using stacked neural networks in order to identify sophisticated patterns. Those kinds of algorithms can sometimes perform better than classic statistical methodologies, but the latter often cannot clarify why one particular recommendation or one particular diagnosis was rendered. This directly contradicts the fundamental precepts of medicine—the evidence-based practice, the informed consent, the shared decision-making.
When the AI programs function as black boxes, the doctors find themselves in the following dilemma: Should the recommendation, as non-interpretable, be accepted, or should it in spite of all the statistical brilliance, be overridden? This dilemma is further heightened when the AI program is deployed in high-stakes specialties like oncology or neurosurgery.
The Watson for Oncology Debacle: Dreams vs. Reality
After Watson bested the human champions in game show Jeopardy in 2011, IBM revealed its new direction in healthcare. Watson for Oncology was created jointly with the Memorial Sloan Kettering Cancer Center (MSKCC) as software that would offer treatment suggestions to oncologists by processing enormous amounts of medical publications as well as patient information.
The promise was extraordinary: democratize expert-level cancer care across the globe. Indian hospitals, including Manipal Hospitals, joined the global experiment. However, within a few years, Watson for Oncology was beset by clinical criticisms and disappointing performance.
A confidential internal IBM report was leaked online, showing that Watson had made “unsafe and incorrect” cancer treatment suggestions in test scenarios. STAT News reported that one of the scenarios was for recommending a treatment that would further exacerbate a patient’s bleeding disorder [1].
Dr. Lynda Chin, the University of Texas MD Anderson Cancer Center’s one-time chief innovation officer, whose center cancelled its Watson venture, spoke frankly:
“Teaching a machine to read a record is a lot harder than anyone thought.”
— Dr. Lynda Chin, MD Anderson Cancer Center [2].
She later added a crucial ethical concern:
“How can we design an environment that can assure the most basic principle in the practice of medicine: Do no harm?”[2].
One of the MD Anderson physicians interviewed for this exposé anonymously was candid:
“This is one piece of shit We purchased it for marketing purposes as well as in hopes you’d bring the vision to reality. We can’t utilize it for the majority of the cases.” [3].
IBM divested the Watson Health business by 2021, bringing to an end the ambitious foray into medical AI.
International Responses: Between Prudence and Conviction
Early optimism about Watson was high. Dr. Larry Norton of MSKCC, one of the key partners in the creation of Watson, once stated:
“What Watson is going to let us do is take that wisdom and encapsulate it in a form people without much experience… can have a wise counsellor by their side at all times.” [4]
The dichotomy of expectation versus reality, however, became manifest when global implementations did not converge with regional conditions. India, for instance, has extremely varied oncological profiles, patient literacy, resource-limited conditions, and disease burden. Educating the AI using Western datasets oftentimes translated into mediocre performance in Indian clinical practice settings.
Nathan Levitan, at the time Chief Medical Officer of IBM Watson Health, recognized these challenges:
“We acknowledge that worldwide physician needs differ, and every physician will employ the kinds of information created by Watson most applicable to his or her patient needs.”【5】
Black Box Problems: What’s at Stake?
Watson debacle illuminates the extent to which murky AI in medicine can fail in many specialties:
- Clinical Trust: Doctors will not implement tools that they cannot comprehend or defend.
- Patient Safety: Erroneous or unaccounted recommendations can cause harm.
- Regulatory and legal risk: Liability is uncertain without explainability.
- Bias Propagation: Models trained on skewed datasets may perform poorly across diverse populations.
These issues are magnified in developing countries where AI is seen as a shortcut to compensating for specialist shortages. Without careful scrutiny, this can do more harm than good.
The Future Ahead: Ensuring AI is Transparent and Trustworthy
To prevent another Watson-like failure, the next generation of AI in medicine must adopt the following principles:
1. Explainable AI (XAI)
The AI models need to provide interpretable explanations of their recommendations. Such tools as SHAP (Shapley Additive Explanations) and LIME (Local Interpretable Model-Agnostic Explanations) are already taking the first steps towards unpacking the black-box models
2. Human-in-the-Loop Systems
Instead of supplanting physicians, AI must aid them. Radiology platforms, for example, Aidoc and PathAI, set the best examples in which doctors cross-verify the AI findings before taking action upon them.
3. Local Data, Local Models
Indian AI startups Niramai and Qure.ai focus on training on locally developed datasets to capture national epidemiology as well as clinical practice.
“AI’s short‑term impact is exaggerated, long‑term underestimated… You will only see the impact of AI after 10 years… When over 90% of hospitals don’t have basic electronic medical records, where is the AI going to transform healthcare?”
— Dr. Devi Shetty, Founder, Narayana Health [6].
4. Strong Ethical and Regulatory Frameworks
India’s new Digital India Act and the Health Data Management Policy in the National Digital Health Mission (NDHM) try to set limits for algorithmic accountability as well as data protection.
5. Collaborative Development
Clinical AI must be co-designed with clinicians, ethicists, data scientists, and patients. WHO’s 2021 report on Ethics and AI in Health provides clear direction towards fairness, accountability, and inclusiveness【7】.
Closing remarks: From Hype to Humility
Watson for Oncology’s case is one of failed expectations, not of the technology, but of implementation and expectations. Medical AI has no business chasing headlines but in assisting patients clearly, compassionately, and responsibly. Its future is in human-centered AI—that is, transparent, culturally aware, rigorously tested. While India and the world embark upon the era of AI, the goal must not be replacing doctors with machines, but enhancing their potential to care, connect, and cure—with explainable algorithms.
Dr. Prahlada N.B
MBBS (JJMMC), MS (PGIMER, Chandigarh).
MBA in Healthcare & Hospital Management (BITS, Pilani),
Postgraduate Certificate in Technology Leadership and Innovation (MIT, USA)
Executive Programme in Strategic Management (IIM, Lucknow)
Senior Management Programme in Healthcare Management (IIM, Kozhikode)
Advanced Certificate in AI for Digital Health and Imaging Program (IISc, Bengaluru).
Senior Professor and former Head,
Department of ENT-Head & Neck Surgery, Skull Base Surgery, Cochlear Implant Surgery.
Basaveshwara Medical College & Hospital, Chitradurga, Karnataka, India.
My Vision: I don’t want to be a genius. I want to be a person with a bundle of experience.
My Mission: Help others achieve their life’s objectives in my presence or absence!
My Values: Creating value for others.
References:
- Ross C, Swetlitz I. IBM’s Watson supercomputer recommended ‘unsafe and incorrect’ cancer treatments, internal documents show. STAT News. 2018.
- STAT News. IBM Watson Can’t Live Up to the Hype. 2017. https://www.statnews.com
- Henrico Dolfing. Case Study: IBM Watson for Oncology. 2020. https://www.henricodolfing.com
- Wired. How IBM’s Watson Went from Jeopardy! to Cancer Treatment—And Failed. 2019.
- The ASCO Post. Confronting the Criticisms Facing Watson for Oncology. 2019.
- Devi Shetty quoted in Forbes India. 2019.
- World Health Organization. Ethics and Governance of Artificial Intelligence for Health. 2021.
Thankyou Dr Prahalad for the elaborate interesting write up on AI & ML
especially about IBM Whatson
Appreciate your in-depth knowledge of the subject.
Surprised to see your accolades obtained from various prestigious institutions. Kudos for the same.
Proud of you.
All The Best for your future ventures👍
Reply*Dear Dr. Prahlada N.B Sir*,
Your article, *Unboxing the Black Box – Lessons from IBM Watson for Oncology and the Future of Medical AI*, is a beacon of insight in the complex world of medical technology. Your ability to dissect the challenges and potential of AI in healthcare is truly commendable. I'd like to add a few thoughts to your excellent analysis.
The story of IBM Watson for Oncology serves as a cautionary tale, reminding us that technology, no matter how advanced, must be wielded with care and transparency. It's akin to a skilled surgeon who not only needs precision but also empathy and understanding. As you aptly pointed out, the black box phenomenon in AI can lead to mistrust among clinicians and potential harm to patients. It's crucial that we prioritize explainability and human oversight in AI systems.
Your emphasis on the importance of local data and context-specific AI models resonates deeply. Just as a map is most useful when tailored to a specific terrain, AI in healthcare must be adapted to the unique needs and conditions of different populations. The work being done by Indian AI startups like Niramai and (link unavailable) is a step in the right direction.
I'm particularly inspired by your vision for the future of medical AI, which emphasizes transparency, cultural awareness, and rigorous testing. It's a reminder that technology should augment, not replace, the human touch in healthcare. As you said, the goal must be to enhance the potential of doctors to care, connect, and cure—with explainable algorithms that support, rather than obscure, medical decision-making.
Thank you for sharing your expertise and perspectives with us. Your dedication to creating value for others is evident in your work, and I'm sure your article will spark meaningful discussions in the medical and tech communities.
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