From Uncertainty to Trust: Kernel Dropout for AI-Powered Medical Predictions

• URL: http://arxiv.org/abs/2404.10483v2
• Date: Sun, 02 Feb 2025 21:54:42 GMT
• Title: From Uncertainty to Trust: Kernel Dropout for AI-Powered Medical Predictions
• Authors: Ubaid Azam, Imran Razzak, Shelly Vishwakarma, Hakim Hacid, Dell Zhang, Shoaib Jameel,
• Abstract summary: AI-driven medical predictions with trustworthy confidence are essential for ensuring the responsible use of AI in healthcare applications. This paper proposes a novel approach to address these challenges, introducing a Bayesian Monte Carlo Dropout model with kernel modelling. We demonstrate significant improvements in reliability, even with limited data, offering a promising step towards building trust in AI-driven medical predictions.
• Score: 14.672477787408887
• License:
• Abstract: AI-driven medical predictions with trustworthy confidence are essential for ensuring the responsible use of AI in healthcare applications. The growing capabilities of AI raise questions about their trustworthiness in healthcare, particularly due to opaque decision-making and limited data availability. This paper proposes a novel approach to address these challenges, introducing a Bayesian Monte Carlo Dropout model with kernel modelling. Our model is designed to enhance reliability on small medical datasets, a crucial barrier to the wider adoption of AI in healthcare. This model leverages existing language models for improved effectiveness and seamlessly integrates with current workflows. Extensive evaluations of public medical datasets showcase our model's superior performance across diverse tasks. We demonstrate significant improvements in reliability, even with limited data, offering a promising step towards building trust in AI-driven medical predictions and unlocking its potential to improve patient care.

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