Artificial intelligence (AI) is used more and more in healthcare to help doctors make decisions, improve patient care, and simplify paperwork. But many people who run medical practices or handle IT in the United States face a big problem. Many AI programs used in healthcare have biases. These biases can cause unfair treatment and affect who gets care and how good that care is. This problem especially hurts racial and ethnic minority groups.
This article looks at where bias in healthcare AI comes from, what effects it has, recent rules to deal with it, and how AI automation tools might create or control these risks.
Bias in healthcare AI is a serious problem that starts in different ways. It can come from the data used to teach the AI, from how the AI is made, and from how it is used in real healthcare settings.
Data Bias happens when the data used to train AI does not represent all types of patients well. For example, many datasets have fewer people from racial minorities or rural areas. This means the AI may work better for some groups and worse for others. If the AI learns from past healthcare data where services were not equal, it may copy those unfair patterns in its advice.
Development Bias comes from choices made when building the AI, like picking the algorithm and deciding what information to use. If the developers do not think carefully about different patient groups or hospitals, the AI may favor some people over others. Some AI tools are only tested inside one hospital and not checked elsewhere, which can make them less fair.
Interaction Bias means bias that happens when AI is used in the real world. Different hospitals and regions have different ways of working, rules, and patient habits. AI trained in one place might not work well in another.
The main problem is what happens when these biased systems are used. Research mentioned by California Attorney General Rob Bonta showed that one popular healthcare AI sent white patients to special services more often than Black patients with the same needs. This was because the AI looked at past healthcare use, which was unfair to minorities.
These unfair results can stop some groups from getting good care. This causes worse health and keeps health differences between groups in the U.S. Medical practices trying to treat everyone equally may find that AI tools put patients from minority groups at a disadvantage.
The problems caused by bias in healthcare AI have caused officials and health leaders to act. In August 2022, California’s Attorney General Rob Bonta began a review of racial and ethnic bias in healthcare AI systems used by hospitals across the state. He asked 30 hospital CEOs for detailed information about the AI they use, its purposes, policies, and how they try to stop unfair effects. This shows that lawmakers know they need rules and openness to stop biased AI from hurting minorities more.
The review also noted that many AI systems in healthcare are not clear. Doctors and patients often do not know how these tools make decisions or what biases they might have. To follow state laws against discrimination, hospitals must now say which AI tools they use, how they work, and what training and checks they have for fairness.
Experts in AI ethics say it is important to check AI at every stage—from how it is made to how it is used in clinics. Matthew G. Hanna, a researcher, says bias can appear anytime, from data collection to user interaction. Fixing bias needs constant monitoring and updates as healthcare and patients change.
Kirk Stewart, an AI ethics expert, says that not showing how AI works, no accountability, and not enough human watching can lead to unfair or harmful decisions. Many AI models work like “black boxes” where no one understands how decisions are made. This lack of clarity causes doctors and patients to trust AI less.
Even with many studies on AI, it is hard to use AI in real healthcare because of rules and testing limits. By June 2020, only 62 AI tools in radiology had approval from the FDA. This low number shows it is tough to get AI cleared for use.
Most AI tools in healthcare are tested only with data inside one hospital or by looking back at old cases instead of testing in real life across many places. A review found only 6% of AI studies tested tools outside the places where they were made. Without testing in many settings, it is unclear how well AI will work everywhere.
AI often performs worse when used in different hospitals or with different patients, causing uneven and biased care. Rules rarely ask for ongoing checks after AI is released. This means problems with AI may stay hidden as these tools become common.
AI’s ethical problems go beyond bias. There are worries about privacy because AI deals with a lot of sensitive patient data. Keeping this data safe from hacks or misuse is very important. It is also hard to say who is responsible when AI makes mistakes. Developers, healthcare workers, and hospitals all play a part.
People also worry that AI might replace human jobs in healthcare. Instead of replacing workers, experts say AI should help health workers do their jobs better without taking over.
Fairness means AI must not hurt or leave out any group of patients. Trust needs clear ways to see how AI works, and humans must check AI results in case they are wrong or misleading.
To handle these worries, people from technology, ethics, healthcare, and government need to work together to create rules and guidelines.
Besides helping with medical decisions, AI now helps with office work in healthcare. For example, some AI tools help answer phones and handle patient communication. This can help reduce the workload for staff.
Medical administrators and IT managers can use AI to improve scheduling, patient contacts, and help desks. But they must carefully check these AI tools for fairness so no patients are hurt.
For example, automated phone systems should give fair service to all patients, including those who speak different languages or come from different cultures. This helps avoid problems for minority patients when making appointments or asking questions.
Automated reminders and follow-ups should not miss patients who often use healthcare less because of money or other issues. This avoids making unfair gaps bigger.
AI workflow tools need regular checks to find any problems caused by bias. Practice owners should add bias reviews and look at data on which patients use services. Training workers to watch AI and step in when needed is important to keep fairness in automated work.
When used carefully, AI automation can make clinics run more smoothly and help reduce barriers that hurt underserved groups in U.S. healthcare.
Racial and ethnic differences in health access and quality are still big issues in the U.S. Biased AI can make these problems worse if not controlled.
To keep AI fair, transparency is important. Developers need to share how AI works and what data they use. Healthcare groups must watch how AI behaves in real use.
State and federal officials, hospital leaders, and practice managers all must ask for careful testing and bias checks of AI. They also need policies and training to catch and stop unfair use.
As AI grows in healthcare, approval from regulators alone does not mean AI is fair or free from bias. Ongoing checks, human supervision, and ethical rules are needed to protect vulnerable patients and help AI improve care for all Americans.
Fixing bias in healthcare AI is important to make care fair for everyone. Medical administrators, owners, and IT staff have key roles in finding and managing these problems. They also must use AI tools in ways that support fair and easy access to care.
As of June 2020, there are only 62 FDA-approved AI applications for clinical use, indicating challenges in obtaining regulatory approval despite numerous publications in the field.
There exists a translational gap that prevents the actual use of AI systems in clinical practice, which includes challenges such as postmarket surveillance and software updates.
Clinical validation involves systematic evaluation of AI performance to ensure safety and efficacy in meeting clinical needs.
A review found that only 6% of studies provided external validation with multi-institutional data, raising concerns about the generalizability of AI tools.
Bias can lead to discriminatory outcomes, such as a commercial risk prediction tool showing significant racial bias affecting access to care.
FDA approval does not mandate peer-reviewed research, leading to many AI tools being evaluated based on retrospective data and internal performance only.
Existing guidelines include STARD, TRIPOD, and CLAIM, but these primarily focus on reporting in research rather than commercial AI products.
AI model performance may degrade when used in different clinical settings, indicating that regulatory clearance alone is insufficient for safety and efficacy.
AI has been characterized by hype, with exaggerated claims of its performance compared to clinicians, further complicating clinical translation.
External validation is crucial for understanding how AI models perform in real-world conditions, which may differ significantly from training environments.
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