Challenges of Accurate and Efficient AutoML

Embedded Artificial Intelligence (AI) is becoming increasingly important in the field of healthcare where such AI enabled devices are utilized to assist physicians, clinicians, and surgeons in their diagnosis, rehabilitation and therapy planning. However, it is still a challenging task to come up wi...

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Bibliographic Details
Published in:IEEE/ACM International Conference on Automated Software Engineering : [proceedings] pp. 1834 - 1839
Main Authors: Dey, Swarnava, Ghose, Avik, Das, Soumik
Format: Conference Proceeding
Language:English
Published: IEEE 11.09.2023
Subjects:
automation
AutoML
Computational modeling
Deep Learning
IoT
Machine learning
Planning
Software algorithms
Software design
software engineering
Surgery
Task analysis
ISSN:2643-1572
Online Access:Get full text
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Summary:Embedded Artificial Intelligence (AI) is becoming increasingly important in the field of healthcare where such AI enabled devices are utilized to assist physicians, clinicians, and surgeons in their diagnosis, rehabilitation and therapy planning. However, it is still a challenging task to come up with an accurate and efficient machine learning model for resource-limited devices that work 24\times 7 .. It requires both intuition and experience. This dependence on human expertise and reliance on trial-and-error-based design methods create impediments to the standard processes of effort estimation, design phase planning, and generating service-level agreements for projects that involve AI-enabled MedTech devices. In this paper, we present AutoML search from an algorithmic perspective, instead of a more prevalent optimization or black-box tool view. We briefly present and point to case studies that demonstrate the efficacy of the automation approach in terms of productivity improvements. We believe that our proposed method can make AutoML more amenable to the applications of software engineering principles and also accelerate biomedical device engineering, where there is a high dependence on skilled human resources.
ISSN:2643-1572
DOI:10.1109/ASE56229.2023.00182