AI bias in healthcare decision-making
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AI Bias in Healthcare Decision-Making: Key Issues and Impacts
Artificial intelligence (AI) is rapidly transforming healthcare decision-making, but bias in AI systems remains a major concern. Bias can arise at multiple stages of AI development and deployment, leading to substandard clinical decisions and the risk of perpetuating or worsening existing healthcare disparities, especially for underrepresented or disadvantaged groups 1235679.
Sources of AI Bias in Healthcare
Bias in healthcare AI can originate from several sources:
- Data Collection and Representation: Many AI models are trained on datasets that do not adequately represent the diversity of the patient population. This underrepresentation can result in models that perform poorly for certain groups, such as racial minorities or those with lower socioeconomic status 124569.
- Labeling and Annotation: Expert-annotated labels used for supervised learning may reflect implicit cognitive biases or substandard care practices, further embedding bias into AI systems 19.
- Model Development and Evaluation: Overreliance on performance metrics without subgroup analysis can obscure bias. Models may perform well overall but fail specific subgroups, leading to inequitable care 14910.
- Deployment and User Interaction: How clinicians interact with AI tools and the context in which these tools are used can introduce additional bias, especially if the tools are not continuously monitored for fairness 147.
Clinical Consequences of AI Bias
AI bias can have significant clinical consequences:
- Misdiagnosis and Suboptimal Care: Biased AI models may misdiagnose or underestimate the needs of certain patient groups, leading to inappropriate or delayed care 2469.
- Exacerbation of Health Disparities: If not addressed, AI bias can reinforce and even worsen existing health inequities, particularly for marginalized populations 1257.
- Lack of Generalizability: Models trained on non-representative data may not generalize well to broader or different patient populations, reducing their clinical utility 129.
Strategies for Detecting and Mitigating AI Bias
Researchers and practitioners have proposed and implemented several strategies to address AI bias in healthcare:
- Diverse and Representative Data Collection: Ensuring datasets include a wide range of patient demographics is critical for building fair AI models 1235679.
- Bias Detection and Monitoring: Continuous monitoring of AI performance across demographic subgroups, using fairness metrics such as statistical parity and predictive equity, helps identify and address bias in real time 4910.
- Model Transparency and Explainability: Emphasizing model interpretability and explainable AI can help clinicians and stakeholders understand and trust AI recommendations, making it easier to spot and correct bias 139.
- Stakeholder Engagement: Involving diverse stakeholders, including patients, clinicians, and ethicists, ensures that multiple perspectives are considered in AI development and deployment 378.
- Standardized Reporting and Validation: Rigorous validation through clinical trials and standardized bias reporting are essential before real-world implementation 110.
Ethical and Policy Considerations
Ethical considerations such as transparency, accountability, and patient privacy are central to responsible AI use in healthcare. Balancing the benefits of AI with the risks of bias requires ongoing collaboration, training, and policy development to ensure equitable outcomes for all patients 378.
Conclusion
AI bias in healthcare decision-making is a complex, multi-faceted challenge that can undermine the promise of AI to improve patient care. Addressing bias requires a comprehensive approach, including diverse data collection, continuous monitoring, stakeholder engagement, and ethical oversight. By implementing these strategies, the healthcare sector can harness the benefits of AI while promoting fairness and equity for all patient populations 12345678+2 MORE.
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Most relevant research papers on this topic
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Bias in medical AI: Implications for clinical decision-making
Biases in medical AI can lead to substandard clinical decisions and perpetuate healthcare disparities, requiring careful mitigation through diverse data sets, statistical debiasing, and standardized bias reporting.
2024·
48citations
·James L. Crosset al.PLOS Digital Health
2
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Addressing bias in big data and AI for health care: A call for open science
AI algorithms in healthcare need to be fairer by addressing algorithmic bias in big datasets, which can lead to fatal outcomes, misdiagnoses, and lack of generalization.
Rigorous JournalHighly Cited
2021·
394citations
·Natalia Nororiet al.Patterns
3
3
PP25 Artificial Intelligence In Healthcare Decision-Making: Addressing Challenges, Ethical Considerations, And Bias
AI in healthcare decision-making holds promise but must address ethical considerations and biases to revolutionize decision-making, improve patient outcomes, and ensure responsible and ethical use.
Systematic Review
2024·
0citations
·Rito Bergemannet al.International Journal of Technology Assessment in Health Care
4
4
Abstract 1970: Protecting against algorithmic bias of AI-based clinical decision making tools in oncology
Our AI-based clinical decision-making tool effectively detects and combats algorithmic bias in oncology decision-making tools, ensuring consistent and fair performance across diverse patient groups.
2023·
2citations
·Renee Georgeet al.Cancer Research
5
5
Addressing Algorithmic Bias and the Perpetuation of Health Inequities: An AI Bias Aware Framework
Algorithmic bias in AI/ML healthcare can perpetuate racial disparities, requiring a four-step process to mitigate it and ensure fairness for all populations.
2022·
62citations
·R. Agarwalet al.Health Policy and Technology
6
6
Bias in AI-based models for medical applications: challenges and mitigation strategies
AI systems in surgery can suffer from bias, impacting disadvantaged populations, and detecting and mitigating bias is crucial for creating fair and generalizable AI technology.
Highly Cited
2023·
143citations
·M. Mittermaieret al.NPJ Digital Medicine
7
7
Considerations for addressing bias in artificial intelligence for health equity
Addressing bias in AI/ML systems can improve health equity and ensure better healthcare outcomes for all.
Highly Cited
2023·
135citations
·M. Abràmoffet al.NPJ Digital Medicine
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8
AI-Assisted Decision-making in Healthcare
AI-assisted decision-making in healthcare can be ethically and responsibly implemented when considering accountability, transparency, potential harms, and clinicians' professional roles and integrity.
Highly Cited
2019·
178citations
·T. Lysaghtet al.Asian Bioethics Review
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9
Algorithmic fairness in artificial intelligence for medicine and healthcare
AI in healthcare can lead to inequitable care due to algorithmic biases, but emerging technologies like disentanglement, federated learning, and model explainability can help mitigate these biases.
Highly Cited
2023·
217citations
·Richard J. Chenet al.Nature Biomedical Engineering
10
10
Unmasking bias in artificial intelligence: a systematic review of bias detection and mitigation strategies in electronic health record-based models
This review highlights evolving strategies to mitigate bias in EHR-based AI models, emphasizing the need for standardized reporting and real-world testing and evaluation for ethical AI in healthcare.
Systematic Review
2024·
30citations
·Feng Chenet al.Journal of the American Medical Informatics Association : JAMIA