Making Sense of Explainable AI in Healthcare and Exploring Its Current Impact and Future Possibilities
DOI:
https://doi.org/10.58445/rars.2143Keywords:
Explainable Artificial Intelligence (XAI), black box problem, medical imagingAbstract
Explainable Artificial Intelligence is creating ripples in healthcare by fixing long-standing issues in transparency, trust, and accountability of AI-driven decision-making. The "black box" problem of many AI models raises serious ethical and practical concerns because AI increasingly drives diagnostics, clinical workflow, and patient outcomes. XAI comes into play to shed light on how these systems make decisions, hence helping to build trust among both health professionals and patients.
This paper delves deep into the latest landscape of XAI in healthcare, illustrating its applications in medical imaging, predictive analytics, and patient engagement. In fact, XAI has been shown to improve clinical decision-making, increase transparency, and better arm patients with valuable insights. Challenges persist, however, with the complexity of AI models, lack of high-quality data, and the need for standardized evaluation metrics, which are very critical for its wide application. It discusses possible solutions regarding overcoming these challenges by developing novel XAI methods, integrating other AI technologies, and establishing cogent evaluation frameworks. Advancing explainability in XAI might well be the key to a healthcare revolution, ensuring ethical AI integration and enhancing trust and reliability in patient-centered care.
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