Building an artificial intelligence-powered medical image recognition smartphone application: What medical practitioners need to know
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 spec...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-01-01
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| Series: | Informatics in Medicine Unlocked |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352914822001605 |
| _version_ | 1828178420205355008 |
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| author | Anindya Pradipta Susanto Hariyono Winarto Alessa Fahira Harits Abdurrohman Arief Purnama Muharram Ucca Ratulangi Widitha Gilang Edi Warman Efirianti Yehezkiel Alexander Eduard George Kevin Tjoa |
| author_facet | Anindya Pradipta Susanto Hariyono Winarto Alessa Fahira Harits Abdurrohman Arief Purnama Muharram Ucca Ratulangi Widitha Gilang Edi Warman Efirianti Yehezkiel Alexander Eduard George Kevin Tjoa |
| author_sort | Anindya Pradipta Susanto |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-12T05:14:35Z |
| format | Article |
| id | doaj.art-48d7c1a8e0764349b4bfa929af4f3e51 |
| institution | Directory Open Access Journal |
| issn | 2352-9148 |
| language | English |
| last_indexed | 2024-04-12T05:14:35Z |
| publishDate | 2022-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Informatics in Medicine Unlocked |
| spelling | doaj.art-48d7c1a8e0764349b4bfa929af4f3e512022-12-22T03:46:39ZengElsevierInformatics in Medicine Unlocked2352-91482022-01-0132101017Building an artificial intelligence-powered medical image recognition smartphone application: What medical practitioners need to knowAnindya Pradipta Susanto0Hariyono Winarto1Alessa Fahira2Harits Abdurrohman3Arief Purnama Muharram4Ucca Ratulangi Widitha5Gilang Edi Warman Efirianti6Yehezkiel Alexander Eduard George7Kevin Tjoa8Cluster of Medical Technology, Indonesian Medical Education and Research Institute, Central Jakarta, Indonesia; Faculty of Medicine, Universitas Indonesia, Central Jakarta, IndonesiaFaculty of Medicine, Universitas Indonesia, Central Jakarta, Indonesia; Division of Gynecologic Oncology, Obstetrics and Gynecology Department, Dr. Cipto Mangunkusumo Hospital, Central Jakarta, Indonesia; Corresponding author. Faculty of Medicine, Universitas Indonesia, Central Jakarta, Indonesia.Faculty of Medicine, Universitas Indonesia, Central Jakarta, IndonesiaSchool of Electrical Engineering and Informatics, Bandung Institute of Technology, Bandung, West Java, IndonesiaFaculty of Medicine, Universitas Indonesia, Central Jakarta, Indonesia; School of Electrical Engineering and Informatics, Bandung Institute of Technology, Bandung, West Java, IndonesiaFaculty of Medicine, Universitas Indonesia, Central Jakarta, IndonesiaFaculty of Medicine, Universitas Indonesia, Central Jakarta, IndonesiaFaculty of Medicine, Universitas Indonesia, Central Jakarta, IndonesiaFaculty of Medicine, Universitas Indonesia, Central Jakarta, IndonesiaEmerging 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.http://www.sciencedirect.com/science/article/pii/S2352914822001605Artificial intelligenceDeep learningMedical image recognitionSmartphone applications |
| spellingShingle | Anindya Pradipta Susanto Hariyono Winarto Alessa Fahira Harits Abdurrohman Arief Purnama Muharram Ucca Ratulangi Widitha Gilang Edi Warman Efirianti Yehezkiel Alexander Eduard George Kevin Tjoa Building an artificial intelligence-powered medical image recognition smartphone application: What medical practitioners need to know Informatics in Medicine Unlocked Artificial intelligence Deep learning Medical image recognition Smartphone applications |
| title | Building an artificial intelligence-powered medical image recognition smartphone application: What medical practitioners need to know |
| title_full | Building an artificial intelligence-powered medical image recognition smartphone application: What medical practitioners need to know |
| title_fullStr | Building an artificial intelligence-powered medical image recognition smartphone application: What medical practitioners need to know |
| title_full_unstemmed | Building an artificial intelligence-powered medical image recognition smartphone application: What medical practitioners need to know |
| title_short | Building an artificial intelligence-powered medical image recognition smartphone application: What medical practitioners need to know |
| title_sort | building an artificial intelligence powered medical image recognition smartphone application what medical practitioners need to know |
| topic | Artificial intelligence Deep learning Medical image recognition Smartphone applications |
| url | http://www.sciencedirect.com/science/article/pii/S2352914822001605 |
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