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Resilience-aware MLOps for AI-based medical diagnostic system.

Viacheslav Moskalenko¹, Vyacheslav Kharchenko²

¹Department of Computer Science, Faculty of Electronics and Information Technologies, Sumy State University, Sumy, Ukraine.

Frontiers in Public Health

|April 11, 2024

Summary

This study introduces a new resilience-aware Machine Learning Operations (MLOps) methodology for AI medical diagnostics. It enhances AI system robustness against attacks and data drift, improving trustworthiness in healthcare applications.

Keywords:

MLOps image recognition medical diagnosis resilience robustness

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Area of Science:

Artificial Intelligence in Healthcare
Machine Learning Operations (MLOps)
Medical Diagnostics

Background:

Healthcare AI requires high responsibility, trustworthiness, and accountability.
Current Machine Learning Operations (MLOps) for medical AI lack resilience against adversarial attacks and data drift.
This article addresses the need for enhanced MLOps to improve AI medical diagnostic system resilience.

Purpose of the Study:

To propose a resilience-aware MLOps methodology for AI-based medical diagnostic systems.
To enhance the robustness and trustworthiness of AI medical diagnostics against various disruptive influences.
To integrate resilience optimization and uncertainty calibration into the MLOps lifecycle.

Main Methods:

Incorporated post-hoc resilience optimization, predictive uncertainty calibration, uncertainty monitoring, and graceful degradation into MLOps.
Utilized adapters and meta-adapters for resilience optimization, fine-tuned on synthetic disturbances.
Introduced a post-hoc uncertainty calibration model trained on in-distribution and out-of-distribution data.

Main Results:

Proposed a resilience-aware MLOps structure for medical diagnostics.
Experimentally confirmed increased robustness and adaptation speed in medical image recognition systems (DermaMNIST, BloodMNIST, PathMNIST).
Observed transformers exhibiting lower resilience than convolutional networks, potentially due to adapter/meta-adapter architecture.

Conclusions:

Novelty lies in separating basic model creation from resilience assurance for medical AI developers.
Resilience optimization enhances robustness and adaptation speed against disturbances.
Calibrated confidences improve the recognition of unabsorbed disturbances, boosting trustworthiness in medical AI.