A systematic literature review on the application of explainable artificial intelligence in healthcare

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Description of Research Project

Widespread artificial intelligence (AI) technology has integrated into numerous facets of daily life and industry, driven by advancements in machine learning, deep learning, and generative AI. One concern with unprecedented advancements in AI based methodological development is that AI based decision making process often lacks transparency. AI models must be explainable, to provide stakeholders with meaningful information about how these models make decisions. To understand the reasoning behind the AI models, explainable artificial intelligence (XAI) techniques must be implemented. This project will provide a systematic literature review on the application of explainable artificial intelligence in healthcare. The healthcare industry is crucial for societal well-being and economic growth, providing essential services, creating jobs, driving innovation, and offering financial security against health shocks, making it a massive economic sector while fundamentally supporting human life and quality of life. The significance of this project is to highlight best practices for implementing XAI effectively in healthcare industry and the key considerations and challenges involved, along with an analysis of the emerging trends and the potential of XAI in driving continuous improvement in healthcare industry.

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Undergraduate Student Responsibilities

• Conduct systematic literature review
• Summarize findings
• Write report

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Qualifications Required

• Strong reading and writing skills
• Analytical and problem-solving skills.
• Familiar with generative AI tools and explainable artificial intelligence.