Bias by Design

Artificial intelligence (AI) is becoming an essential tool in modern medicine, promising faster diagnoses, lower costs, and greater efficiency. Yet beneath this excitement lies a deeper concern: how data and profit shape whose health is valued most. AI in healthcare is not created in isolation; it is built on publicly funded research, patient information, and private investment. The National Institutes of Health (NIH) has emphasized that data-driven medicine must be developed responsibly, warning that “data is not an objective representation of the world… healthcare AI models that don’t account for bias often perform inadequately” [1]. When innovation becomes inseparable from financial interest, the pursuit of progress risks overshadowing the purpose of care.

AI systems promise to bridge gaps in access, but they can just as easily widen them. Their success depends on the quality and diversity of the data they learn from, data that reflect decades of unequal access to care. This makes bias not only a technical issue but a structural and moral one. When public institutions generate research and datasets that private companies later monetise, the line between scientific advancement and commercial exploitation blurs.

From Public Research to Biased Data

Much of the data used to train medical AI originates from publicly funded research. Hospitals and universities supported by grants from the NIH and other agencies produce large storages of patient data that later become the material for commercial algorithms. The Lancet Digital Health warns that “without careful dissection of the ways in which biases can be encoded into artificial intelligence (AI) health technologies, there is a risk of perpetuating existing health inequalities at scale” [2]. The same paper concludes that “one major source of bias is the data that underpins such technologies” [2].

Public data intended to improve care can, once commercialised, reproduce the very inequities it was meant to solve. Public funds build the foundation, but private firms often capture the profit. It is a pattern in which equity is lost between the grant and the bill. This hidden economy of medicine shows how the flow of data, capital, and ownership determines who benefits from innovation.

When Costs Define Care

The risks of biased AI became visible in a landmark Science investigation. Researchers Ziad Obermeyer and colleagues found that “an algorithm widely used in US hospitals to allocate health care to patients” systematically discriminated against Black patients because it used healthcare spending as a proxy for medical need [3]. The team wrote: “The bias arises because the algorithm predicts health-care costs rather than illness, but unequal access to care means that we spend less money caring for Black patients than for White patients” [3].

By equating cost with illness severity, the algorithm transformed economic inequality into medical inequality. In this behind-the-scenes aspect of AI, financial metrics, not clinical reality, determine who received help. What began as an effort to improve efficiency instead caused injustice, showing that fairness cannot be assumed simply because a system follows a mathematical algorithm.

Global Bias and Invisible Inequality

The effects of algorithmic bias reach far beyond the United States. In Frontiers in Public Health, researchers describe bias as “hidden in code, imperceptible in action” and “an urgent threat to consider” [4]. They note that many AI models “draw from data sets in populations which are unrepresentative of those in the low- and middle-income countries,” resulting in systems that “do not capture the cultural, linguistic, genetic, or environmental variety in underserved populations” [4]. When AI tools trained on Western or high-income populations are deployed globally, they risk reproducing existing inequities under the notion of innovation.

The Moral Cost of Efficiency

AI is often praised for making healthcare more efficient, but efficiency can have an ethical price. JAMA Health Forum warns that “as artificial intelligence (AI) algorithms become an increasingly integral part of health care … it is vital that rigorous processes to mitigate algorithmic bias are established” [5]. When algorithms are optimised for profit, reimbursement, or speed rather than equity, they risk perpetuating the very disparities they were built to overcome. As The Lancet Digital Health observes, “one major source of bias is the data that underpins such technologies” [2]. Efficiency without fairness becomes exploitation, a form of moral debt that medicine cannot afford to ignore.

Conclusion: Seeing What Is Hidden

The rise of AI in medicine reveals a recurring pattern: public funding builds the science, private entities shape the products, and patients ultimately bear the costs. The hidden economy of medicine thrives in this space between discovery and delivery. Fixing it requires transparency about how algorithms are trained, who owns medical data, and how benefits are shared. The goal is not just to make AI smarter, but fairer. If medicine is to be guided by technology, it must ensure that the future it programs serves everyone, not only those it can afford to see.

Reviewed by: Sehar Mahesh

Designed by: Selena Xiao

References:

[1] National Institutes of Health. (2025, July 18). Celi cautions developers, clinicians to beware of bias in healthcare AI models. NIH Record. https://nihrecord.nih.gov/2025/07/18/celi-cautions-developers-clinicians-beware-bias-healthcare-ai-models.

[2] Alderman, J. E., Palmer, J., Laws, E., McCradden, M. D., Ordish, J., Ghassemi, M., … Mackintosh, M. (2024). Tackling algorithmic bias and promoting transparency in health datasets: The STANDING Together consensus recommendations. The Lancet Digital Health. https://doi.org/10.1016/S2589-7500(24)00224-3.

[3] Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447–453. https://doi.org/10.1126/science.aax2342.

[4] Joseph, J. (2025). Algorithmic bias in public health AI: A silent threat to equity in low-resource settings. Frontiers in Public Health, 13. https://doi.org/10.3389/fpubh.2025.1643180.

[5] Ratwani, R. M., Fong, A., & Coiera, E. (2024). Patient safety and artificial intelligence in clinical care. JAMA Health Forum, 5(5), e241523. https://doi.org/10.1001/jamahealthforum.2024.1523.

A Million Dollar Cure That Became a Commodity Instead of A Lifeline

At 20 years old, Zoe Davis, a college student, was rushed to the hospital for severe pain in her abdomen and legs. At a young age, she was diagnosed with sickle cell disease (SCD) and suffered from severe pain. SCD is notorious for hospitalizing patients with chronic pain that often leaves them incapable of completing basic tasks. Zoe is one of the approximately 100,000 Americans living with the inherited blood disorder that causes red blood cells to be malformed.

Historically, patients have been limited to the same drugs that mitigate pain but fail to address SCD’s root cause. Approximately two years ago, that changed when the FDA approved two gene therapy treatments for SCD, Casgevy and Lyfgenia.

Both treatments address SCD by modifying the hemoglobin genes in stem cells to create healthier blood cells that are less likely to sickle. After the clinical trials were completed, 96.7% of patients reported no major crises in the following year.

When doctors first approached Zoe with the new treatment option, she was not entirely on board. What was a scientific parachute for the disease was immediately disregarded due to its financial burdens. The costs: 

Casgevy - $2.2 million.

Lyfgenia - $3.1 million. 

While this would not have been the cost incurred by Zoe herself after insurance and payment plans, the financial burden was still enough to create a deterrent in her mind. This is not an isolated incident. The science says that gene therapy treatments are effective, yet clinical application is scarce, and regular Americans face far too many obstacles to obtain them.

Traditional drugs are repeatedly manufactured and are able to become profitable through their mass volume and market infiltration. Gene therapies, on the other hand, are created to be a one-time curative treatment. This is the reasoning used by manufacturers for their price points. They argue that they are conducting these pricing models based on perceived value and save the individual from additional medical costs. 

The issue with this is that the pricing model does not consider the fact that much of the research behind CRISPR-based therapies and stem cell engineering is all publicly funded or supported by other academic institutions. Hence, when you truly consider where the funding originates from, it is actually the citizens’ taxes that pay for the research grants. The private manufacturers are able to sit and rake in the profits. This is not just the case for SCD drugs:

Hemgenix (Hemophilia B): $3.5 million

Zolgensma (Spinal Muscular Atrophy): $2.1 million

Luxturna (Retinal Dystrophy): $850K

While the diseases and the actual price points are different, it does not change the fact that the treatments are sold based on what the companies believe the ultrawealthy will pay.

In some cases, people will argue that the high prices of the cure are justified as the patient is not actually paying that total price. However, insurance companies are not lining up to cover these costs. In fact, for patients struggling with SCD, 60% rely on Medicaid. While Medicaid offers a drug rebate program and the Gene Therapy (CGT) Access Model to mitigate drug costs, we can estimate hundreds of thousands of dollars in out-of-pocket costs due to extraneous expenditures.

The chemotherapy drugs used within the SCD treatment regimen are known to cause infertility among other side effects, which require an additional $20,000+ a year to manage. Prior to gene therapy, patients were already struggling to find appropriate specialists and affordable care. With those added costs, gene therapy is simply unattainable for most.

Even for patients who decide they are interested in receiving treatment, it is not easy to obtain. Of those who are typically hospitalized, 93.4% are African American and 4.8% Hispanic. Patients from these demographics tend to lack access to specialized treatment centers and Medicaid-covered services. This points to the reality of American healthcare. The providers will only present themselves in locations that are inherently profitable. 

The larger patterns of society showcase that curative medicine is structurally incompatible with a health system that is focused on profitability. It also showcases the fundamental contradictions of medical innovation. The public funds the science. The private firms are able to patent it. The patients then end up paying top dollar based on the market.

How Can We Tackle This?

While it may feel as though this concept of gene therapy is a failure, that’s far from the case. A cure exists, and when people receive it, it works. If we create the infrastructure to deliver it widely, it has the potential to change tens of thousands of lives.

Pilot programs of Medicaid negotiate care prices and help reimburse hospitals to minimize patients' costs and increase coverage; however, this is not enough to reach equity. The current CMS Cell and Gene Therapy (CGT) Access Model utilizes outcomes-based payment structures that allow states to pay based on whether the patients are genuinely improving. This program provides some relief; however, it fails to cover other expenses such as traveling or loss of wages. Medicaid’s pilot program must be expanded into a permanent initiative that covers not only the total costs of treatment but also any miscellaneous expenses, which would lift pressure off patients.

New federal incentives need to prioritize hospital accreditation in high-incidence regions, rather than directing resources towards high-income areas. These resources need to come in the form of government subsidies. This would begin to create stable infrastructure and bring care closer to the communities that need it.

Increasing gene therapy access would reduce suffering from a disease that diminishes and takes the lives of so many unnecessarily. For Zoe, it might have meant no more visits to the hospital.

Reviewed by: Sehar Mahesh

Designed by: Sebastian Mardales

References:

[1] Hanson, M. (2025). Average Cost of Medical School. Education Data Initiative.

https://educationdata.org/average-cost-of-medical-school. 

[2] Lorena, M. (2019). The Cost of Applying to Medical School — A Barrier to Diversifying the Profession. The New England Journal of Medicine, 381: 1505-1508. https://www.nejm.org/doi/full/10.1056/NEJMp1906704.   

[3] Pisaniello, M.S. et al. (2019). Effect of medical student debt on mental health, academic performance and specialty choice: a systematic review. BMJ Open, 9(7). doi: 10.1136/bmjopen-2019-029980. 

[4] Christophers, B., Marr, M. C., & Pendergrast, T. R. (2022). Medical School Admission Policies Disadvantage Low-Income Applicants. The Permanente Journal, 26(2): 172–176. https://doi.org/10.7812/TPP/21.181.

[5] Kahn, M. & Sneed, E. J. (2015). Promoting the Affordability of Medical Education to Groups Underrepresented in the Profession: The Other Side of the Equation. AMA Journal of Ethics, 17(2):172-175. doi: 10.1001/virtualmentor.2015.17.2.oped1-1502.

Who Can Afford to Become a Doctor: The Cost of Applying to Medical School

Becoming a physician has always been demanding, but in recent decades, the financial burden associated with a medical degree has made it an even greater challenge. It is estimated that it will cost the class of 2030 $418,674 to obtain their medical degree, which includes their undergraduate tuition and fees [1]. This dramatic rise in tuition and related educational expenses has created a significant barrier for students from low-income backgrounds. Further, the high cost of medical school and its admission policies contribute to a lack of diversity within medical and healthcare fields. 

Over the past several decades, tuition at both public and private medical schools has steadily increased. The average private medical school charged $67,145 to residents in the AY 2024-2,5 and the average cost of medical school had a compound annual growth rate of 3.70% over 20 years [1]. These figures exclude living expenses, books, medical equipment, training courses, and exam fees such as the MCAT, USMLE, or other memberships, which contribute thousands of dollars to rising costs. As a result, the “median amount of educational debt held by medical school graduates…grew to $200,000 in 2018” [2]. Medical school debt can be negatively associated with mental well-being and academic outcomes and can drive students towards higher paying specialties [3]. Overall, a heavy financial burden can discourage low-income students, who often lack family financial support and must rely entirely on loans.

The finances of medical students represent an important barrier to becoming a physician, especially for students from disadvantaged backgrounds. To take the MCAT, it takes $345, and this does not include the cost of practice exams or preparatory courses in which “21.2% of students enroll, which typically cost between $2,000 and $10,000” [2]. Along with the MCAT, most schools require the use of the American Medical College Application Service, and the 2026 application fee is $175 for the first school, with an added $47 cost for each additional school. There is also a secondary application fee that can vary in cost, and a college service fee. Applicants must also face expenses for travel, overnight accommodations, and appropriate attire when visiting potential medical schools. Lower-income applicants also typically work part-time jobs, leaving less time for activities that can strengthen their medical school applications, such as research, volunteer work, and clinical experience. 

The consequence of this high financial barrier to the medical profession is a potential lack of diversity in the field. The fact is that “many applicants from low-income backgrounds do not have an equal opportunity to become ‘qualified’ because of structural barriers” [4]. They struggle to afford being a competitive applicant due to the cost of tests or lack of time and opportunity for prestigious experiences and shadowing opportunities [4]. Importantly, diversity is critical to the success of the medical field. Studies have shown that “underrepresented groups are more likely than whites to provide health care services in underserved communities” [5]. In 2011, the AAMC reported that “54.6 percent of African Americans, 36 percent of Hispanics, and 33.6 percent of American Indians or Alaska Natives had career plans to work in underserved areas, compared to 19.4 percent of Asians and 21.4 percent of whites” [5]. 

Despite these challenges, efforts are being made to reduce financial barriers and promote equity in medical education. The AAMC Fee Assistance Program gives financial aid and assorted testing resources to select students taking the MCAT. Certain medical schools have implemented tuition-free programs to attract a more adverse applicant pool, like New York University's School of Medicine. However, these efforts are not yet widespread enough to address systemic financial barriers. Ultimately, there must be a concerted national change in the cost of medical education as the need for physicians continues to grow. The future of healthcare should not depend on who can afford to become a physician, but on equitable opportunity. 

Reviewed by: Laila Khan-Farooqi

Designed by: Jennifer Liu

References:

[1] Hanson, M. (2025). Average Cost of Medical School. Education Data Initiative.

https://educationdata.org/average-cost-of-medical-school. 

[2] Lorena, M. (2019). The Cost of Applying to Medical School — A Barrier to Diversifying the Profession. The New England Journal of Medicine, 381: 1505-1508. https://www.nejm.org/doi/full/10.1056/NEJMp1906704.   

[3] Pisaniello, M.S. et al. (2019). Effect of medical student debt on mental health, academic performance and specialty choice: a systematic review. BMJ Open, 9(7). doi: 10.1136/bmjopen-2019-029980. 

[4] Christophers, B., Marr, M. C., & Pendergrast, T. R. (2022). Medical School Admission Policies Disadvantage Low-Income Applicants. The Permanente Journal, 26(2): 172–176. https://doi.org/10.7812/TPP/21.181.

[5] Kahn, M. & Sneed, E. J. (2015). Promoting the Affordability of Medical Education to Groups Underrepresented in the Profession: The Other Side of the Equation. AMA Journal of Ethics, 17(2):172-175. doi: 10.1001/virtualmentor.2015.17.2.oped1-1502.