As we step into 2025, we have already seen how artificial intelligence is revolutionizing healthcare, bringing transformative advancements in precision medicine and patient care. From streamlining diagnostics to enabling personalized treatments, AI is reshaping the way we approach healthcare. However, a critical issue remains – I’m talking about AI bias. If not addressed, these biases—stemming from incomplete or unrepresentative data—can compromise fairness, equity, and the overall effectiveness of healthcare systems.

In this blog, I’m going to help you understand everything about AI bias in healthcare and precision medicine. Moreover, I’ll provide insights and strategies to help your healthcare organization mitigate these biases and promote algorithmic fairness.

Let’s jump-start!

What Are the Causes of AI Bias in Healthcare?

AI bias occurs when an algorithm produces systematically skewed outcomes due to factors such as:

• incomplete or unrepresentative data
• flawed assumptions
• the design of the AI model itself

In healthcare, bias can affect diagnosis, treatment recommendations, and access to patient care. These biases can result from multiple factors, including race, ethnicity, gender, socioeconomic status, and geographic location.

AI models rely on data for training, and if the data isn’t diverse or representative of all patient groups, the resulting algorithms may not perform equally well for everyone. This can lead to issues such as:

Fig: Impact of AI Bias on Healthcare Outcomes

• Data Imbalance: One of the critical challenges in developing AI systems for healthcare is data imbalance. Often, the datasets used to train these systems are heavily skewed towards specific demographic groups, particularly white male patients. This skew can lead to significant inaccuracies when the AI is applied to diagnose or predict conditions for women, people of color, or other underrepresented populations.For example, a diagnostic model trained predominantly on data from white male patients might misinterpret symptoms or fail to recognize conditions that present differently in women or individuals from diverse ethnic backgrounds. This lack of representative data compromises the accuracy of the AI. It also raises ethical concerns about equity and fairness in medical treatment.
• Historical Inequities: Healthcare systems worldwide have long been shaped by historical and systemic inequalities. These have created disparities in access to medical care and treatments. These inequalities are often mirrored in the data that AI systems use for training. If an AI model is trained on data that reflects these biases—such as unequal treatment protocols or access to care—it may inadvertently reinforce these disparities.For instance, if historical data shows that certain treatments were predominantly given to affluent patients, an AI system might prioritize those treatments for similar demographic groups, perpetuating the cycle of inequality. Thus, without careful consideration and correction of these biases, AI could exacerbate existing healthcare inequities rather than mitigate them.
• Bias in Medical Research: Medical research, particularly clinical trials, has historically underrepresented certain groups, including women, people of color, and older adults. This lack of diversity in clinical trial data results in a narrow understanding of how different populations respond to treatments. When AI systems are trained on such biased data, they are likely to generate predictions and recommendations that are not universally applicable.For example, an AI model predicting the efficacy of a new drug might produce accurate results for the demographic groups well-represented in the trial but fail to do so for underrepresented groups. This can lead to a misalignment in treatment outcomes, where certain populations receive less effective or even harmful recommendations. Addressing these biases in medical research is crucial to developing AI systems that are inclusive and equitable.
• Algorithmic Design: Algorithmic design is a critical factor in the potential bias of AI models, especially in healthcare. The choices made during model development—such as which variables to include, how to structure the model, and which populations to prioritize—can introduce significant bias.For example, an AI model that focuses primarily on clinical indicators like blood pressure while neglecting social determinants of health, such as housing or income levels, may produce skewed results. This oversight can lead to inaccurate predictions for individuals whose health outcomes are heavily influenced by social factors. It thereby perpetuates disparities.

To mitigate these biases, it’s essential to design AI systems that incorporate diverse data and consider the broader context of patient health. This will ensure more equitable and accurate healthcare solutions.

Now, let’s move on to the next part of learning how bias impacts precision medicine!

How does AI Bias Impact Precision Medicine?

Precision medicine focuses on personalizing treatments based on a patient’s genetic, environmental, and lifestyle factors. AI has the potential to improve precision medicine by recognizing patterns and predicting outcomes with greater accuracy. However, biases in AI models can diminish these advantages. This can result in less effective or even harmful outcomes.

Here are a few of them:

Fig: Impact of AI Bias on Precision Medicine

Note: The consequence of a biased AI model can further lead to ethical concerns and compromise patient safety. Refer to the diagram below to understand how:

Fig: Consequence of a biased AI model

So far, we’ve discussed the problem, but you might be wondering what comes next, right? Well, keep reading to understand the effective ways to mitigate AI bias in healthcare and precision medicine.

Learn how our tailored GenAI solutions can help identify risk factors and personalize care plans for enhanced patient outcomes.

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How to Mitigate AI Bias in Healthcare?

AI’s potential to revolutionize healthcare and precision medicine is immense, however, its benefits can be undermined if the algorithms are biased. To tackle this roadblock, algorithmic fairness works as a key concept. It ensures that these systems are equitable, transparent, and accessible to all patient populations, regardless of their background.

Algorithmic fairness in healthcare involves strategies to identify and mitigate biases in AI models. These biases may result in unfair, unequal, or discriminatory healthcare outcomes.

Refer to these actionable approaches to address algorithmic fairness in healthcare AI. There are practical examples to demonstrate how these methods can be applied:

Fig: Approaches to mitigate Healthcare AI bias

Continue reading about the points in detail!

1. Ensure diverse and representative data collection:

One of the primary causes of bias in healthcare AI is skewed or incomplete data. Training AI systems with unrepresentative data can lead to algorithms that underperform for groups like racial minorities, women, or those with lower socioeconomic status.

Here’s what you can do to address this AI bias:

• Collect diverse datasets: Ensure that AI models are trained on datasets that are representative of all demographic groups. This includes not only demographic factors like race, gender, and age but also other health-related variables such as socioeconomic status, geographic location, and underlying health conditions.
• Ensure inclusion of rare conditions: For precision medicine, which involves tailoring treatments to individual genetic profiles, it’s important to include data from underrepresented genetic variants and rare diseases to avoid biases in treatment recommendations.

For example:

The case of facial recognition in dermatology: An AI tool developed for diagnosing skin conditions from images (e.g., melanoma) was found to perform poorly for people with darker skin tones because it was primarily trained on images of lighter skin. This led to higher rates of misdiagnosis for people of color.

Solution: To address this, researchers curated a more diverse dataset of skin images, ensuring the model included patients with a range of skin tones and types. This helped improve the accuracy of the diagnosis across all racial groups.

2. Collaborate with healthcare experts and clinical trial data:

To mitigate bias, developers can adopt fairness-aware algorithms that explicitly account for fairness during the training and prediction phases. These algorithms aim to reduce disparities between different demographic groups.

Here’s what you can do:

• Pre-processing techniques: Before training, balance datasets to ensure that each group is adequately represented, and no group is overrepresented.
• In-processing fairness: During the algorithm’s training phase, apply fairness constraints or regularization methods that adjust the model to ensure fairness metrics are met.
• Post-processing fairness: After the model has been trained and predictions have been made, adjust the results so that the outcomes are equitable across groups.

For example:

Predicting cardiac risk: An AI model used to predict the risk of cardiovascular diseases (CVD) could use data like age, gender, blood pressure, and cholesterol levels. However, historical data may underrepresent women. It may lead to a model that is less accurate for female patients.

Solution: Researchers could apply a fairness constraint during training that adjusts the model’s prediction process, ensuring equal predictive accuracy for both men and women. This method is often referred to as ”equalized odds“, where the error rates for both groups are minimized to ensure fairness.

Extra read: Read how AI and big data are transforming healthcare analytics.

3. Promote explainability and transparency in AI models:

For AI models to be fair and trustworthy, they must be transparent, and healthcare providers and patients need to understand how decisions are made. Explainability (also known as interpretability) is essential to ensure that biased outcomes can be traced back to their sources and corrected.

Here are the actions to be taken to address this bias:

• Use interpretable models: When possible, use interpretable machine learning algorithms (for example, decision trees, linear regression) rather than “black-box” models (such as deep neural networks). If black-box models are necessary, employ techniques like SHAP (Shapley Additive Explanations) or LIME (Local Interpretable Model-agnostic Explanations) to explain individual predictions.
• Provide actionable insights: Ensure that explanations are not just for clinicians but also patients, so they can make informed decisions based on the AI’s predictions.

For example:

Predicting sepsis: An AI system used in hospitals to predict sepsis may make a recommendation for treatment. If the algorithm’s prediction is based on a complex neural network, the physician may not understand why it flagged a certain patient as high-risk. This limited openness regarding information can lead to trust issues.

Solution: Using SHAP values to explain the prediction would highlight the factors influencing the decision (for example, changes in vital signs, lab results, etc.). This transparency can help clinicians trust the system and adjust their treatment plans accordingly. It can also help them ensure that biases related to specific patient characteristics (such as ethnicity or age) are not skewing the predictions.

4. Monitor and audit continuously for fairness:

Once the AI systems are deployed in healthcare settings, they must be continuously monitored and audited to ensure they remain fair and effective over time. This is especially important as new data is collected, and patient populations evolve.

Here are the necessary actions that can be taken:

• Regular audits: Conduct routine fairness audits to evaluate how well AI systems perform across different demographic groups. This should be standard practice to identify any potential emerging biases after deployment.
• Post-deployment analysis: Track the real-world impact of AI recommendations to detect and address unintended consequences, such as certain groups receiving poorer outcomes.

For example:

AI in predictive diagnostics: Suppose an AI model is used in emergency rooms to predict the likelihood of stroke. After initial deployment, audits reveal that the system underperforms for elderly patients or people from certain minority groups.

Solution: A continuous monitoring process would flag these disparities, prompting an update to the model or data collection process. This would ensure that it better serves these patient populations. This could involve adjusting the algorithm to account for factors like age-related health risks or incorporating data that represents the unique health challenges faced by elderly patients.

5. Conduct inclusive clinical trials:

To develop AI models that truly reflect diverse populations, clinical trials must be inclusive and representative of all demographic groups. Ensuring diversity in clinical studies is essential for gathering data that accounts for the unique needs and conditions of underrepresented groups.

Here are the actions that can be taken:

• Active recruitment: Prioritize enrolling participants from underrepresented populations, such as racial minorities, women, and rural communities, to ensure diverse data collection.
• Collaborations: Partner with community organizations to reach underrepresented groups and address potential barriers to participation, such as lack of access or mistrust in the healthcare system.

For example:

AI in cardiovascular disease detection: A cardiovascular AI model initially trained on predominantly male participants may show lower accuracy in detecting heart disease in women.

Solution: Conducting inclusive clinical trials that ensure equal representation of genders will provide balanced data would be a solution. This would enable the AI model to identify cardiovascular risks with higher accuracy for both men and women.

6. Establish ethical oversight and regulatory guidelines:

Regulatory frameworks and ethical oversight ensure that AI systems in healthcare are fair and transparent. Clear ethical guidelines and standards need to be established and enforced.

Here are the actions that can be taken:

• Incorporate ethics boards: Involve interdisciplinary teams – clinicians, ethicists, data scientists, and patient representatives – in the design, deployment, and evaluation of AI systems.
• Implement regulatory oversight: Governments and organizations should set up guidelines for AI fairness and transparency in healthcare, like the EU’s Artificial Intelligence Act, which includes standards for fairness and accountability.

For example:

FDA Regulation of AI in Healthcare: In the United States, the FDA (Food and Drug Administration) has begun regulating certain AI tools used in healthcare, ensuring that they meet safety and fairness standards. The FDA’s Digital Health Innovation Action Plan promotes the development of safe and effective AI technologies, with fairness considerations as a core part of its mission.

Solution: AI systems used for precision medicine, such as those recommending personalized cancer treatments, would be subject to FDA review. The review would ensure the system is based on diverse and representative data, with built-in fairness metrics, to ensure all patients receive the most appropriate treatment.

So, following the above strategies and addressing bias at every stage—from data collection to model deployment—will not only enhance the accuracy of AI systems but also foster trust among patients and healthcare providers.

Take the next leap of digital healthcare transformation with us at Nitor Infotech. Write to us with your thoughts and additionally explore our cutting-edge software development services to seize a win in 2025.