AI Bias in Healthcare: Black Patients Denied 46% of Needed Care

Artificial intelligence (AI) is transforming healthcare in ways that were once unimaginable.

We’re seeing faster diagnostics, smarter treatment plans, and improved outcomes for patients.

But there’s a problem—a big one.

AI, often praised for its neutrality, can unintentionally amplify systemic biases that already exist in healthcare.

Here’s a startling example:
A healthcare algorithm denied Black patients up to 46% of the care they needed because it relied on flawed metrics rooted in historical inequities.

This wasn’t just a technical error.

It was a stark reminder that technology is only as unbiased as the data and systems that create it.

If we’re not vigilant, AI could worsen disparities instead of solving them.

What Went Wrong? Understanding the 46% Gap

Let’s break down how a seemingly well-intentioned healthcare algorithm went so catastrophically wrong, resulting in Black patients receiving 46% less care than they needed.

The algorithm in question aimed to identify patients who required additional medical support. To do this, it relied on healthcare costs as a proxy for need. The assumption was simple: higher spending must indicate more severe conditions.

But here’s the issue: healthcare spending isn’t an accurate reflection of medical necessity.

Why Cost Metrics Are Misleading

In healthcare, spending doesn’t just depend on how sick a patient is—it’s heavily influenced by systemic factors. For example:

• Black patients historically receive fewer resources, even when their health conditions are more severe.
• Geographic disparities mean patients in underfunded areas often have limited access to services, driving down overall spending.

So, when the algorithm ranked patients based on spending, it didn’t account for these inequities. Instead, it reinforced them.

The Result: A Dangerous Oversight

By equating lower spending with lower need, the algorithm deprioritized Black patients for additional care—even in cases where their health conditions were worse than those of white patients.

Take chronic conditions like diabetes or hypertension, which disproportionately affect Black patients. Managing these illnesses often involves lower-cost, ongoing interventions. The algorithm overlooked this, favoring higher-cost cases that didn’t necessarily indicate greater urgency.

The gap wasn’t just a small oversight. Once corrected, researchers found that Black patients should have been allocated 46.5% more care than the system initially recommended. That’s nearly half of their needed care, left unmet.

A Lesson in Misguided Design

This failure wasn’t just about technical flaws—it was a misunderstanding of what “need” looks like in healthcare. By relying solely on cost data, the system reduced a complex human reality into a single, skewed number.

This isn’t an isolated case. It’s a stark reminder that when designing AI for healthcare, the choice of metrics matters. Without careful attention, algorithms can reinforce the very inequities they’re meant to solve.

The Ethical Challenges of AI in Healthcare

AI has the potential to revolutionize healthcare, but it also brings a host of ethical challenges that must be addressed to avoid unintended harm. Let’s break down five key concerns and how they can be tackled effectively.

1. Justice and Fairness

AI should be a tool for leveling the playing field in healthcare, not widening existing gaps. Unfortunately, biased training data often does just that—leading to discriminatory outcomes that disproportionately affect minority groups.

What’s happening? If datasets used to train AI are skewed—like including predominantly white patients while underrepresenting other racial groups—the resulting tools fail to perform equally well across diverse populations.

Real-world example: A diagnostic algorithm trained on biased data might be less accurate at detecting conditions like skin cancer in non-white patients, simply because it hasn’t “seen” enough cases in those groups.

How we fix it: Developers must prioritize building diverse and representative datasets and apply fairness constraints that actively correct for disparities. Think of it like quality control for equity—baked into every step of the process.

2. Transparency

AI systems often operate like mysterious “black boxes,” delivering decisions or recommendations without explaining how they arrived there. This lack of clarity can undermine trust, especially in life-and-death healthcare scenarios.

What’s the risk? Clinicians may hesitate to trust or act on AI outputs if they can’t justify the decision to their patients. And for patients, not understanding the “why” behind a decision can lead to confusion or even mistrust.

A real-world dilemma: A physician might ignore an AI recommendation for a treatment plan if they’re unsure whether it accounts for specific patient nuances—leading to underutilization of the technology.

The way forward: Build explainable AI systems that clearly communicate how decisions are made. For instance, offering insights like, “This diagnosis was based on these 3 specific patterns in your test results,” makes it easier for both clinicians and patients to understand and trust the technology.

3. Patient Consent and Confidentiality

AI thrives on data. Lots of it. But this reliance raises serious questions about privacy and informed consent.

The challenge: How do we ensure patients’ data is used responsibly while still collecting enough information to improve AI’s performance?

What’s at stake? Without proper safeguards, there’s a risk of violating patient trust or even exposing sensitive information to unauthorized parties.

Balancing act: Healthcare organizations should implement clear consent protocols that empower patients. For example:

• Provide easy-to-understand explanations of how data will be used.
• Offer opt-out options without penalties.
• Regularly update patients about how their data contributes to improving care.

Transparency isn’t just about compliance—it’s about respecting autonomy.

4. Accountability

When AI gets it wrong, the consequences can be severe. But figuring out who is responsible—developers, clinicians, or institutions—can be a legal and ethical minefield.

The big question: If an AI system recommends the wrong treatment and a patient is harmed, who’s at fault? The developer for flawed algorithms? The clinician for blindly trusting the output? Or the institution for deploying the tool?

Example scenario: An AI misidentifies a patient’s condition due to gaps in its training data. The clinician follows the AI’s suggestion, and the patient suffers harm. Accountability is murky, leaving everyone scrambling for answers.

The fix: Establish robust accountability frameworks that outline responsibilities at every stage—development, deployment, and use. This might include requiring developers to document training data sources and decision-making processes, while clinicians retain final authority over patient care.

5. Patient-Centered Care

AI is powerful, but it can’t replicate the human touch. Patients don’t just want accurate diagnoses; they need empathy, understanding, and trust—things only a human caregiver can provide.

The risk: Over-reliance on AI might erode the clinician-patient relationship. If a system is perceived as doing all the “thinking,” patients might feel alienated or undervalued.

A real-world concern: Imagine an AI suggesting a course of action, but a patient wants to discuss their fears or preferences with their doctor. Without that human connection, the patient’s emotional and psychological needs might go unmet.

A better approach: Design AI to enhance human caregiving, not replace it. For instance:

• Use AI to handle repetitive tasks like data analysis, freeing up clinicians to focus on patient interaction.
• Ensure that AI outputs are presented as suggestions or tools, not final decisions, leaving room for human judgment.

At its best, AI should act as a supporting player, amplifying the clinician’s ability to provide compassionate and effective care.

By addressing these challenges head-on, we can ensure AI doesn’t just revolutionize healthcare—it does so in a way that is ethical, equitable, and always focused on the people it’s meant to serve.

Building a Better Future: Strategies for Ethical AI

If we want AI to truly improve healthcare, we need to tackle its flaws head-on. It’s not just about fixing technical glitches—it’s about reshaping how we think and act at every stage of AI development. Here’s how we can make AI both effective and ethical.

1. Adopt Ethical Frameworks

Every great system starts with a solid foundation. For AI in healthcare, frameworks like SHIFT—Sustainability, Human-Centeredness, Inclusiveness, Fairness, and Transparency—provide a clear blueprint.

• Sustainability: AI systems should adapt to long-term healthcare needs, avoiding quick fixes that could lead to inequities.
• Human-Centeredness: Patients and caregivers should feel supported, not sidelined, by AI.
• Inclusiveness and Fairness: Diverse voices must be involved in development to ensure the AI works for everyone—not just the majority.
• Transparency: Everything, from data sources to decision-making processes, should be open and easy to understand.

Embedding these principles ensures AI aligns with both ethical standards and patient care goals.

2. Practice Algorithmovigilance

Think of this as the AI equivalent of routine health check-ups. Algorithmovigilance means keeping a constant eye on how systems perform over time to detect and fix biases before they cause harm.

Here’s what it involves:

• Regularly test AI systems with real-world data to spot disparities.
• Assemble multidisciplinary teams—including clinicians, data scientists, and ethicists—to evaluate performance from all angles.
• Have clear procedures to revise, retrain, or even deactivate systems that show signs of bias or errors.

This ongoing evaluation ensures that AI stays fair and reliable as it encounters new scenarios.

3. Balance Datasets

AI’s fairness depends on the data it’s trained on. If that data isn’t representative of the population, the system won’t be either.

To fix this:

• Oversample minority groups to ensure they’re well-represented in training datasets.
• Include diverse data types covering different ages, genders, ethnicities, and socioeconomic backgrounds.
• Use tools like bias detection algorithms to identify and address imbalances in datasets.

The result? AI that works for everyone, not just the groups with the most data.

4. Enhance Explainability

No one wants to hear, “Just trust the AI.” For healthcare AI to succeed, it needs to explain its decisions in ways that both clinicians and patients can understand.

Here’s how we do it:

• Provide clear reasoning behind every decision, such as highlighting which data points were most influential.
• Use visual aids like decision trees or heat maps to make outputs easier to grasp.
• Tailor explanations to your audience—a doctor might need a technical breakdown, while a patient might need simpler terms.

When people understand AI’s reasoning, they’re more likely to trust its recommendations.

Let’s Build Better AI

These strategies are about more than just improving technology—they’re about rethinking how we approach healthcare itself. By prioritizing fairness, transparency, and accountability, we can ensure AI helps everyone, not just a select few.

The future of ethical AI isn’t just possible—it’s necessary. And it starts with steps like these.

Breaking the “Black Box” Problem

Transparency isn’t just a nice-to-have in healthcare—it’s a must-have. Imagine sitting in a doctor’s office and hearing, “The AI says so.” No further explanation. No insight into how that decision was made. It’s not just frustrating—it’s downright dangerous.

That’s the “black box” problem. And it’s something we can’t afford to ignore.

Why Transparency Matters

Without transparency, AI feels like a mysterious oracle, spitting out decisions no one can fully understand or question. This isn’t just a technical issue—it erodes trust for both clinicians and patients.

• Clinicians hesitate to rely on AI outputs they can’t explain, reducing the system’s effectiveness.
• Patients feel alienated or uneasy when decisions about their health seem arbitrary or opaque.

Transparency is the bridge that connects trust, accountability, and effective use.

Two Keys to Solving the Problem

1. Explainability
AI systems must be able to articulate how they reach their conclusions. It’s not enough to provide an output—AI needs to show its work, just like a math problem.

For example:

• A diagnostic tool should point to specific patterns in medical images or lab results that informed its recommendation.
• A treatment-planning system might highlight which symptoms or risk factors carried the most weight in its decision.

This level of detail empowers clinicians to trust the system and patients to feel confident in their care.

2. Outcome Transparency
Patients and clinicians need a clear view of how AI makes its recommendations. This includes:

• Data origins: What datasets did the AI learn from? Are they diverse and representative?
• Decision-making processes: What factors influenced the AI’s output, and in what order?
• Confidence levels: How certain is the system about its recommendation? For instance, was a diagnosis 90% likely or just 55%?

Outcome transparency ensures that AI doesn’t just give an answer—it provides the context clinicians need to evaluate and validate that answer.

Moving Forward

Breaking the “black box” isn’t just about fixing AI; it’s about fixing how we use it. When patients and clinicians have a clear understanding of how decisions are made, trust grows, outcomes improve, and we unlock the full potential of AI in healthcare.

Because at the end of the day, healthcare isn’t just about answers—it’s about understanding.

The Role of Policy and Regulation

AI is revolutionizing healthcare, but without strong policies and regulations, it could just as easily cause harm as it can help. To keep up with rapid advancements, governments and institutions must adapt their frameworks to ensure fairness, safety, and trust.

Here’s how we can do it:

Mandated Bias Audits

Think of bias audits as regular check-ups for AI systems. They help identify and correct inequalities before they spiral into larger issues.

Here’s what these audits should involve:

• Development phase: Test datasets to ensure they’re diverse and representative.
• Pre-deployment: Validate that the algorithm performs equally well across all demographic groups.
• Ongoing use: Monitor AI systems in the real world to catch and fix any new biases that emerge.

By making bias audits a requirement, we can embed fairness into AI systems from day one and maintain it as they evolve.

Global Collaboration

AI doesn’t stop at borders, so our policies shouldn’t either. Countries can share knowledge, learn from each other’s mistakes, and create global standards for ethical AI.

Take the European Union’s General Data Protection Regulation (GDPR) as an example. It’s a model for balancing privacy with innovation. Its principles can guide healthcare AI too:

• Transparency: Patients need to know how their data is collected, stored, and used.
• Consent: Individuals should have clear options to opt in or out of data collection.
• Risk-based oversight: Systems with higher stakes—like diagnostic AI—should face stricter scrutiny.

This kind of global collaboration fosters consistency and ensures that all AI systems, no matter where they’re used, meet ethical standards.

A Vision for Patient-Centered AI

AI has the potential to transform healthcare into something faster, smarter, and more accessible for everyone. But here’s the catch—it won’t happen automatically. We need to make deliberate, conscious efforts to ensure that AI serves all patients equitably.

Building Trust Through Transparency

Trust is the backbone of patient-centered care, and AI needs to earn it. Patients and clinicians alike must feel confident that AI systems are working in their best interests.

This means:

• Clearly explaining how AI arrives at its conclusions.
• Ensuring patients know their data is handled securely and ethically.
• Creating systems that empower patients with personalized, understandable insights into their health.

When people understand and trust the technology, they’re more likely to embrace it.

Eliminating Bias for True Equity

One of the most exciting promises of AI is its ability to reduce disparities in healthcare. But that only works if the systems themselves are free of bias.

We need to:

• Train AI on diverse datasets that reflect the real world.
• Regularly test systems to ensure they work equally well across all populations.
• Incorporate feedback from underrepresented communities in both design and implementation.

By actively addressing bias, we can create AI that lifts everyone up, not just a privileged few.

Keeping the Focus on Healing

At its core, healthcare is about people. AI should never lose sight of that.

Rather than replacing human judgment, AI should enhance it. Imagine clinicians spending less time crunching numbers and more time building meaningful connections with their patients. That’s the kind of healthcare AI can help us achieve.

A Question We Must Keep Asking

As we build these systems, one question should guide every step: Is AI serving everyone fairly?

If the answer isn’t a clear yes, it’s time to go back and make it right.

AI should be a tool for healing, not harm. And with the right safeguards, accountability, and focus on equity, it can be just that—a powerful ally in the journey toward better healthcare for all.

Conclusion: Let’s Redefine Healthcare AI

AI in healthcare isn’t just about machines crunching data. It’s about the values we embed into those systems.

Will AI be a tool for fairness, equity, and better care? Or will it deepen the gaps that already exist?

The answer lies in our hands.

What We Need to Do

• Prioritize equity so every patient, regardless of their background, receives the care they need.
• Emphasize transparency to build trust among clinicians and patients.
• Focus on patient-centered care to ensure AI enhances, not replaces, the human connection.

The Time to Act is Now

AI is advancing rapidly, but so are its challenges. If we don’t address the biases, transparency gaps, and ethical concerns today, we risk creating more harm than good.

What do you think? Are we headed in the right direction?

Join the Conversation

Your voice matters in this critical moment. Share your thoughts in the comments below and let’s shape a future where healthcare AI is a force for fairness, innovation, and healing.

Together, we can redefine what healthcare looks like—for everyone.

References:

• Weiner, E. B., Dankwa-Mullan, I., Nelson, W. A., & Hassanpour, S. (2024). Ethical challenges and evolving strategies in the integration of artificial intelligence into clinical practice. Dartmouth College and George Washington University. Retrieved from https://arxiv.org/pdf/2412.03576
• Advancing health care AI through ethics, evidence and equity. American Medical Association https://www.ama-assn.org/practice-management/digital/advancing-health-care-ai-through-ethics-evidence-and-equity (2024).
• Ahmad, M. A. et al. Fairness in Healthcare AI. in 2021 IEEE 9th International Conference on Healthcare Informatics (ICHI) 554–555 (2021). doi:10.1109/ICHI52183.2021.00104.