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2025 | OriginalPaper | Chapter

Would You Trust an AI Doctor? Building Reliable Medical Predictions with Kernel Dropout Uncertainty

Authors : Ubaid Azam, Imran Razzak, Shelly Vishwakarma, Hakim Hacid, Dell Zhang, Shoaib Jameel

Published in: Web Information Systems Engineering – WISE 2024

Publisher: Springer Nature Singapore

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Abstract

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. 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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Footnotes
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Metadata
Title
Would You Trust an AI Doctor? Building Reliable Medical Predictions with Kernel Dropout Uncertainty
Authors
Ubaid Azam
Imran Razzak
Shelly Vishwakarma
Hakim Hacid
Dell Zhang
Shoaib Jameel
Copyright Year
2025
Publisher
Springer Nature Singapore
Book
Web Information Systems Engineering – WISE 2024

Print ISBN: 978-981-9605-72-9

Electronic ISBN: 978-981-9605-73-6

Copyright Year: 2025

DOI
https://doi.org/10.1007/978-981-96-0573-6_24

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