The Dark Side of AI in Healthcare: Perpetuating Racial Bias and Health Disparities
Exploring how Artificial Intelligence in healthcare can perpetuate racial biases, leading to worsening health disparities among Black patients.
By Darius Spearman (africanelements)
About the author: Darius Spearman is a professor of Black Studies at San Diego City College, where he has been pursuing his love of teaching since 2007. He is the author of several books, including Between The Color Lines: A History of African Americans on the California Frontier Through 1890. You can visit Darius online at africanelements.org
Key Takeaways
| Points | Details |
|---|---|
| AI’s Role | Initially promised efficiency but now under scrutiny for racial bias |
| Flawed Models | AI models like Large Language Learning Models provide inaccurate and racist information |
| Oversight | Lack of oversight leads to AI models absorbing biased information |
Introduction: The Double-Edged Sword of AI in Healthcare
Artificial Intelligence (AI) was once the golden child of healthcare, promising to revolutionize the industry with efficiency and accuracy. However, recent studies have shown that the technology might be doing more harm than good, especially when it comes to racial bias. This article aims to shed light on the dark side of AI in healthcare, focusing on how it perpetuates racial biases and worsens health disparities.
“A.I. has the potential to harm patients of color by perpetuating racist myths in healthcare settings.” (The Root)
The Stanford Warning: AI’s Potential Harm to Patients of Color
Stanford School of Medicine recently released a study that serves as a red flag for the use of AI in healthcare. The study warns that AI could perpetuate racist myths in healthcare settings, particularly affecting patients of color. This is a critical issue that needs immediate attention, as it directly impacts the quality of healthcare that Black patients receive.
The study is not the first of its kind but adds to a growing body of research that questions the ethical implications of AI in healthcare. It calls for more oversight and regulation to ensure that AI does not become a tool for reinforcing racial disparities in healthcare. The Stanford School of Medicine has been at the forefront of this research, emphasizing the need for immediate action.
The Flaws in AI Models
One of the most alarming findings of the Stanford study was the flawed nature of AI models, particularly Large Language Learning Models.
“The models repeatedly spouted information that was inaccurate and, or racist.”
(The Root)
For instance, one model suggested that Black and white patients have biologically different pain thresholds, a claim that has no scientific basis.
Flawed AI Models Include
- Large Language Learning Models
- ChatGPT
- Google’s Bard
The flaws in these AI models are not just random errors but systematic issues that arise from the way these models are trained. They rely on massive inputs from across the internet and textbooks, often absorbing outdated and biased information. This lack of oversight is a significant concern and calls for immediate action to rectify these flawed systems.
Lack of Oversight: A Recipe for Disaster
The issue of oversight, or rather the lack thereof, is a ticking time bomb in the realm of AI in healthcare. These AI models are trained on massive datasets that often include biased or outdated information. The Stanford study points out that,
“These models are flawed because they rely on massive inputs with little oversight.” (The Root)
The lack of oversight is not just a technical issue but a social justice concern. When AI models are trained on biased data, they perpetuate those biases, affecting real-world decisions in healthcare. This is particularly concerning for Black patients, who are already facing systemic health disparities.
Previous Alarms: Not the First Warning
It’s crucial to note that the Stanford study is not the first to ring the alarm bells on the issue of racial bias in AI. Previous research and journalistic investigations have pointed out similar concerns. For instance, The Washington Post found troubling results in AI data sets used by tech giants.
Table: Previous Studies on Racial Bias in AI
| Source | Key Findings |
|---|---|
| The Washington Post | Troubling results in AI data sets |
| MIT Technology Review | Biased algorithms affecting healthcare |
| ProPublica | Racial bias in criminal justice algorithms |
Despite these warnings, the adoption of AI in healthcare, media, and technology sectors doesn’t seem to be slowing down. This raises questions about the ethical considerations being made by companies and institutions that continue to use these flawed systems.
Worsening Health Disparities
The most immediate and concerning impact of racial bias in AI is the worsening of health disparities among Black patients. A study led by Stanford School of Medicine researchers found that AI chatbots like ChatGPT and Google’s Bard are perpetuating racist medical ideas.
“Experts worry these systems could cause real-world harms and amplify forms of medical racism that have persisted for generations.” (NBC News)
Areas Affected by Health Disparities
- Pain Management
- Cancer Treatment
- Maternal Health
- Mental Health Services
The real danger lies in the potential for these AI systems to reinforce race-based disparities in the quality of care patients receive. In radiology, for example, a patient’s race is not relevant to determining the presence or absence of disease or injury. Yet, these AI models could introduce racial bias into such critical healthcare decisions.
Reinforcing False Beliefs
The AI models in question are not just perpetuating systemic biases;
“In some cases, they appeared to reinforce long-held false beliefs about biological differences between Black and white people.” (NBC News)
This is a dangerous path, as these false beliefs have been used historically to justify unequal treatment and discrimination.
These false beliefs are not just academic or theoretical issues; they have real-world implications. They can affect everything from pain management protocols to diagnostic procedures, further deepening the health disparities that already exist.
The Real-World Consequences
The stakes are high when it comes to getting AI wrong in healthcare. Dr. Roxana Daneshjou from Stanford University emphasizes the dancers, particularly in exacerbating health disparities among Black patients.
“There are very real-world consequences to getting this wrong that can impact health disparities.” (NBC News)
Table: Real-World Consequences of Racial Bias in AI
| Area | Consequence |
|---|---|
| Pain Management | Incorrect pain assessment |
| Diagnostics | Misdiagnosis or delayed diagnosis |
| Treatment Plans | Inadequate or inappropriate treatment |
The consequences are not just limited to healthcare. They spill over into other areas like insurance, where biased AI models could result in higher premiums or denial of coverage based on false racial assumptions.
Increasing Patient Reliance on AI
As technology advances, patients are increasingly turning to AI chatbots to help diagnose symptoms. This growing reliance raises concerns about the reliability and accuracy of these AI systems, especially when they are flawed and biased.
AI Chatbots Used in Healthcare
- ChatGPT
- Google’s Bard
- IBM Watson
- HealthTap
The trend of relying on AI for healthcare advice is not slowing down, making it imperative to address the racial biases in these systems. Failure to do so could lead to incorrect diagnoses and inappropriate treatments, putting patients’ lives at risk.
Tech Companies Respond
In light of these alarming findings,
“Both OpenAI and Google said in response to the study that they have been working to reduce bias in their models.” (NBC News)
While this is a step in the right direction, it’s crucial to hold these companies accountable for making tangible changes.
The tech industry’s response is a glimmer of hope, but it’s not enough. Concrete actions, transparent algorithms, and third-party audits are essential to ensure that AI becomes a tool for equitable healthcare, not a perpetuator of racial bias and health disparities.
Conclusion: Navigating the Complex Intersection of AI, Racial Bias, and Healthcare Disparities
As we delve deeper into the age of technology, the role of Artificial Intelligence in healthcare becomes increasingly complex. While AI holds the promise of revolutionizing healthcare, it also poses significant risks, particularly in perpetuating racial bias and worsening health disparities. From flawed AI models to a lack of oversight, the challenges are manifold and have real-world consequences for Black patients.
Tech companies like OpenAI and Google have acknowledged these issues, but acknowledgment is just the first step. What’s needed now is action—concrete steps to ensure that AI serves as a tool for equitable healthcare, not as a perpetuator of systemic biases. As patients increasingly rely on AI for healthcare advice, the urgency to address these issues has never been greater. The future of equitable healthcare hangs in the balance, making it crucial for all stakeholders to act now.
