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The role of data bias in AI-driven healthcare decision-making: Impacts and mitigation approaches
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School of Business |
Bachelor's thesis
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en
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28
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The use of artificial intelligence (AI) is increasingly being used in various fields, including healthcare. AI has the potential to enhance efficiency, optimize processes, and reduce costs, making it a valuable tool for businesses and organizations. In healthcare, AI-driven solutions can significantly improve patient outcomes and quality of care. However, to realize these benefits, it is crucial to ensure that AI is developed and implemented responsibly.
AI-based systems rely on data for training, making the representativeness and quality of data crucial in ensuring fairness and reliability. When the training data is biased, AI systems can produce skewed outcomes, potentially leading to misdiagnoses and worsening health disparities. Understanding the risks, impacts, and mitigation strategies of data bias is therefore crucial. Despite the growing use of AI in healthcare, existing literature lacks a comprehensive examination of the consequences of data bias and how to mitigate them in healthcare field. Thus, this study aims to fill that gap by exploring the issue within healthcare field specifically.
Through a literature review, this study identified multiple impacts of data bias in AI-driven healthcare solutions. The impacts were categorized in four key themes: misdiagnosis and unfair outcomes, loss of trust in healthcare systems, resource misallocation, and ethical and legal consequences. Additionally, the study examines various mitigation strategies, grouped into following main categories: data processing approaches, education of clinicians and patients, and ethical and regulatory considerations. By examining these issues, this research contributes to a better understanding of data bias in healthcare AI and highlights the importance of developing fair and transparent AI systems.