Neural architecture search for medicine: A survey

Abstract

In this article we examined research on using neural architecture search (NAS) in medical applications, prompted by the current shortage of health care professionals relative to patient volumes. We explored the current state of NAS development in various medical fields, evaluated its performance, and examined potential future directions of NAS in medicine. We conducted a comprehensive search for articles published between 2019 and 2024, using the search string (Neural Architecture Search) OR (NAS) AND (medicine) OR (medical) OR (disease) OR (cardiovascular system) OR (MRI). We identified relevant studies published by Elsevier, IEEE, MDPI (IJERPH, Mathematics, Sensors), Nature, and SpringerLink, specifically focusing on experimental NAS applications in medical contexts. Data from 62 articles were collected, revealing a predominant use of NAS for image data classification, particularly in neurological research. Moreover, NAS demonstrated superior model performance compared with conventional deep learning methods. It is anticipated that future developments in NAS models for medical applications will lead to greater ease of use and enhanced efficacy as well as reduced computational resource consumption, thereby helping to mitigate health care workforce shortages and improve diagnostic accuracy. In addition to its application in diagnosis, NAS holds promise in everyday health monitoring, which could potentially enable the early detection of diseases, empowering people to receive the care that need and live healthier lives.

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