Problems With AI in Healthcare: The Unbundling of Modern Medicine
The global market for artificial intelligence in healthcare is exploding, set to reach nearly $22 billion in 2025 and grow at a staggering rate of over 38% annually. This torrent of investment promises a revolution: AI that can diagnose disease with superhuman accuracy, personalize treatments down to the genetic level, and streamline inefficient hospital workflows. Yet, beneath this veneer of progress lies a series of profound challenges that threaten to undermine the very essence of medicine.
These aren't just technical glitches or policy footnotes. As I argue in my book, The Great Unbundling, they are fundamental consequences of separating, or "unbundling," human capabilities. For millennia, the practice of medicine has been a bundled enterprise. A doctor's analytical mind was bundled with their intuition, their diagnostic skill with their empathetic touch, their clinical judgment with their ethical responsibility. AI is a wedge, systematically prying these capabilities apart. Understanding the problems with AI in healthcare requires us to look at the fallout from this great unbundling.
This article will serve as a guide for the AI-curious professional seeking to grasp the real-world stakes, the philosophical inquirer questioning the future of human health, and the aspiring ethicist who must navigate these new, complex landscapes. We will explore the primary issues with AI in healthcare, moving beyond the hype to confront the urgent risks and challenges ahead.
The Unbundling of Clinical Judgment: AI's Double-Edged Sword
One of the most celebrated promises of AI is the unbundling of raw analytical power from the traditional physician. AI models can screen thousands of medical images or patient data points in seconds, identifying patterns the human eye might miss.
Separating Diagnosis from Intuition
The results can be astounding. Studies have shown AI models achieving remarkable accuracy rates, in some cases even outperforming human specialists. For instance, certain AI systems have demonstrated up to 94% accuracy in identifying conditions that were initially missed by human doctors. This unbundling of pure diagnostic processing is powerful, offering the potential to catch diseases earlier and save lives.
The Negative Impact: Losing the "Art" of Medicine
However, this separation carries a significant risk of AI in healthcare: the erosion of holistic clinical judgment. A diagnosis is more than a pattern matched in a dataset. It is an understanding of a human being's life, context, fears, and values.
- Context is King: A human physician can factor in a patient's hesitation, their family history shared in conversation, or the socioeconomic factors impacting their health—data points that rarely make it into the algorithm.
- The Intuitive Leap: Experienced doctors often describe an intuitive sense—a "gut feeling"—that something is wrong, even when the initial data is inconclusive. This intuition is a form of subconscious pattern matching built over a lifetime of experience, one that is difficult to replicate and is lost when judgment is purely computational.
The primary challenge here is not to stop the unbundling of diagnostics, but to ensure it doesn't discard the irreplaceable "art" of medicine in the process.
Algorithmic Bias: A Major Risk of AI in Healthcare
Perhaps the most widely discussed negative impact of artificial intelligence in healthcare is algorithmic bias. AI systems learn from data, and if the data reflects existing societal biases, the AI will not only replicate but amplify them at an unprecedented scale.
How Biased Data Creates Racist AI
The most infamous example of this is the case of an algorithm developed by Optum, which was used to predict the health needs of millions of Americans. The algorithm used a seemingly logical proxy for health needs: healthcare costs. However, it failed to account for the fact that, due to a complex mix of systemic inequality and historical distrust, Black patients often spend less on healthcare than white patients with the same level of illness.
The result was a catastrophic failure. A groundbreaking 2019 study in Science revealed that the algorithm systematically underestimated the health needs of the sickest Black patients. The study's author, Ziad Obermeyer, estimated that the algorithm's racial bias reduced the amount of care Black patients received by over 50%. While researchers were able to work with the company to reduce this specific bias by 84% by re-tuning the algorithm to focus on biological indicators instead of cost, the case serves as a stark warning.
The Negative Impact on Health Equity
This is not an isolated incident. The core problem with AI in healthcare is that it can codify and accelerate existing health disparities.
- Racial Disparities: Data from the Kaiser Family Foundation shows that as of 2022, Black infants were more than twice as likely to die as white infants (10.9 vs 4.5 per 1,000 births). An AI system trained on data reflecting this reality without explicit correction could deprioritize neonatal resources for Black communities.
- Gender and Ethnic Gaps: Nonelderly American Indian/Alaskan Native (19%) and Hispanic (18%) people are more than twice as likely to be uninsured as their white counterparts (7%). AI models that use insurance status or frequency of care as inputs risk further marginalizing these populations.
Without intentional design and rigorous auditing, AI becomes a high-tech engine for perpetuating old prejudices, one of the most severe risks of artificial intelligence in healthcare.
Data Privacy and Security: The Unseen Challenges
AI systems are voracious, requiring massive troves of patient data to function. This centralization of sensitive health information creates a treasure chest for malicious actors and a hornet's nest of ethical dilemmas.
The Vulnerability of Our Most Personal Data
The healthcare industry is already a prime target for cyberattacks. The cost of a healthcare data breach is the highest of any industry, averaging an astonishing $9.77 million in 2024. The scale is terrifying: in just the first six months of 2025, data breaches have already affected over 28.5 million individuals in the U.S.
As hospitals increasingly rely on cloud-based AI, they expand the "attack surface," creating more entry points for hackers. A breach doesn't just expose financial information; it exposes diagnoses, genetic information, and mental health records—data that can be used for blackmail, discrimination, and profound personal violation.
Unbundling Data from Ownership
A more subtle, philosophical issue with AI in healthcare is the question of data ownership. When you consent to a medical procedure, who owns the resulting data? Is it you, the hospital, or the third-party AI company whose algorithm analyzes your scan? This unbundling of data from the individual it describes is a central theme of The Great Unbundling. Capitalism, the engine of this process, incentivizes the commodification of this data, often with little transparency or recourse for the patient whose life it represents.
The Erosion of Human Connection and Trust
Beyond the technical and data-centric challenges lies a more human problem: the risk of unbundling empathy from the act of caring.
When Your Doctor is an Algorithm
The patient-doctor relationship is built on a foundation of trust and human connection. Being seen, heard, and understood by another person is, in itself, a form of healing. What happens to this dynamic when the diagnostic authority shifts to an impersonal black box? The negative impact of AI in healthcare could be a world of technically perfect, but emotionally sterile, medical interactions that leave patients feeling more like data points than people.
Deskilling the Human Professional
There is also a significant long-term risk of AI in healthcare for medical professionals themselves. An over-reliance on AI for diagnostic support could lead to the "deskilling" of a generation of doctors. If the machine always has the answer, the human incentive to develop deep clinical intuition and master complex diagnostic reasoning may atrophy over time, leaving us dangerously dependent on the systems we've created.
The Great Re-bundling: Charting a Human-Centered Path Forward
Acknowledging the problems with AI in healthcare is not a call for its abolition. The potential benefits are too great to ignore. Instead, it is a call for a conscious and deliberate response—what I term in The Great Unbundling as "The Great Re-bundling." This is the human effort to re-integrate our capabilities in new ways, harnessing the power of the unbundled machine without sacrificing our values.
For Healthcare Professionals: The Clinician as AI Conductor
The doctor of the future may not be a simple diagnostician but an "AI conductor." Their most crucial skill will be to orchestrate the inputs and outputs of various AI tools, critically evaluate their recommendations, catch their inevitable biases, and re-bundle the cold, analytical output with human empathy, wisdom, and ethical judgment.
For Patients: Advocating for Meaningful Care
As patients, we must become advocates for our own human-centered care. This means asking questions: How is AI being used to inform my diagnosis? What data is being used, and how is it protected? We must insist that the final arbiter of our health is a human being who can look us in the eye.
For Policymakers: Building Ethical Guardrails
We urgently need new rules for this new era. Governments and regulatory bodies must mandate transparency in how healthcare algorithms are built, require independent audits for bias, and establish clear lines of accountability when an AI system causes harm.
Beyond the Algorithm: Reasserting Human Value
The myriad problems with AI in healthcare—from algorithmic bias and data security to the erosion of the patient-doctor relationship—all stem from the same core process: the unbundling of human capabilities. AI is a mirror reflecting both our incredible ingenuity and our deepest societal flaws.
The path forward is not to halt this technological progress but to master it. We must build a future where AI serves as a powerful tool, wielded by human professionals who can re-bundle its computational power with the empathy, ethical oversight, and holistic judgment that define the very best of human care.