Healthcare Algorithm Bias: Approaches for Lessening Its Impact
Healthcare AI, with its potential to outperform human experts in diagnosing diseases and predicting patient outcomes, is transforming the medical field. However, like any technology, AI is not immune to bias, which can lead to discriminatory outcomes and exacerbate health disparities. Here's how strategies are being developed to mitigate algorithm bias in healthcare.
A comprehensive debiasing strategy is essential, encompassing technical, operational, organizational, and ethical approaches. Technical strategies involve using tools to identify bias sources and data traits affecting model accuracy. Operational strategies improve data collection processes through internal auditing groups and third-party audits to catch bias early. Organizational strategies foster workplaces with transparent metrics and inclusive cultures to manage bias actively.
Improving human-driven processes is another crucial aspect. This includes identifying and addressing biases in training data and model evaluation, which may require redesigning workflows, training staff, and fostering cultural awareness to reduce ingrained biases.
Adopting ethical frameworks, such as the Fairness, Accountability, and Transparency (FAT) framework and AI ethics guidelines tailored for healthcare, guides the design, development, validation, and deployment of algorithms with fairness and accountability in mind.
Implementing diverse and representative data collection is key to ensuring training datasets reflect all patient populations fairly. This includes careful data annotation and labeling practices to avoid underrepresentation that leads to biased outcomes.
Designing transparent and explainable algorithms allows clinicians and users to understand how decisions are made, promoting trust and enabling detection of potential biases. Rigorous testing and validation on diverse patient groups continually throughout the AI lifecycle help detect and mitigate identified biases before deployment and during real-world use.
Replacing harmful race-based corrections in clinical algorithms with race-neutral or context-aware approaches that consider social determinants of health reduces racial bias without loss of diagnostic accuracy. This requires continuous evaluation and contextual adaptation.
A multidisciplinary team, including ethicists, social scientists, clinicians, epidemiologists, and diverse community representatives, integrates multiple perspectives and expertise on bias and fairness in the development process. Promoting diversity within AI development teams ensures that minority viewpoints are represented, increasing chances of early bias detection and culturally sensitive solutions.
Ongoing monitoring and feedback mechanisms post-deployment detect performance shifts, data drifts, or emergent biases in AI tools, combined with iterative improvements informed by real-world feedback.
In practice, the test of CheXNet, an AI model, outperformed 6 radiologists from Stanford University in diagnosing pneumonia from chest x-rays. However, cases like the UnitedHealth Group, where a bias in the algorithm reduced the number of black patients identified for extra care, highlight the need for these strategies. The AI triage process, based solely on symptoms and preexisting conditions, can be biased due to disparities based on race and social economic status.
Historical bias in AI systems can arise when the data collected to train an AI system no longer reflects the current reality. Representation bias in AI systems results from how the training data is defined and sampled from the population. Measurement bias occurs when training data features or measurements differ from real-world data, particularly in image recognition systems where the data is mainly collected from one type of camera while real-world data is from multiple types of cameras.
The use of AI in healthcare carries legal risks, as demonstrated by cases of algorithmic bias leading to discrimination against individuals. As AI continues to play an increasingly significant role in healthcare, it is essential to prioritise these strategies to ensure fair and equitable patient care for all.
In light of the AI model, CheXNet, outperforming human experts in diagnosing pneumonia yet potentially perpetuating disparities, it's crucial to prioritize strategies that combat bias in AI healthcare systems. Strategies include adopting ethical frameworks, such as the Fairness, Accountability, and Transparency (FAT) framework, and implementing diverse and representative data collection to avoid underrepresentation and measurement bias. Ongoing monitoring and feedback mechanisms post-deployment help detect performance shifts and iterative improvements, ensuring AI delivers fair and equitable patient care, bridging the gap in health-and-wellness, particularly in the context of medical-conditions and technology.
