Building an artificial intelligence-powered medical image recognition smartphone application: what medical practitioners need to know

Anindya Pradipta Susanto, Hariyono Winarto, Alessa Fahira, Harits Abdurrohman, Arief Purnama Muharram, Ucca Ratulangi Widitha, Gilang Edi Warman Efirianti, Yehezkiel Alexander Eduard George, Kevin Toja

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)
51 Downloads (Pure)

Abstract

Emerging technologies powered by artificial intelligence (AI) have sparked hope of achieving better clinical outcomes among patients. One of the trends is the use of medical image recognition systems to screen, diagnose, or stratify risks of diseases. This technology may enhance sensitivity and specificity and thus, improve the accuracy and efficiency of disease diagnosis. Therefore, it is important and beneficial for healthcare providers to understand the basic concepts of AI so that they can develop and provide their own AI-powered technology. The purpose of this literature review is to provide (1) a simplified introduction to AI, (2) a brief review of studies on medical image recognition systems powered by AI, and (3) discuss some challenging aspects in this field. While there are various AI-powered medical image recognition systems, this paper mainly discusses those integrated in smartphone apps. Medical fields that have implemented image recognition models in smartphones include dermatology, ophthalmology, nutrition, neurology, respiratology, hematology, gynecology, and dentistry. Albeit promising, AI technology may raise challenges from the technical and social aspects of its application. Notable technical issues are limited dataset access and small datasets, especially for rare diseases. In a social context, the perspectives of all involved parties (physicians, patients, and engineers) must be considered.
Original languageEnglish
Article number101017
Pages (from-to)1-10
Number of pages10
JournalInformatics in Medicine Unlocked
Volume32
Early online date7 Aug 2022
DOIs
Publication statusPublished - 10 Aug 2022
Externally publishedYes

Bibliographical note

Copyright the Author(s) 2022. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

Keywords

  • Artificial intelligence
  • Deep learning
  • Medical image recognition
  • Smartphone applications

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