Aleksandar Ćirković Show Affiliations »
Abstract
BACKGROUND: Consumer-oriented mobile self-diagnosis apps have been developed using undisclosed algorithms, presumably based on machine learning and other artificial intelligence (AI) technologies. The US Food and Drug Administration now discerns apps with learning AI algorithms from those with stable ones and treats the former as medical devices. To the author's knowledge, no self-diagnosis app testing has been performed in the field of ophthalmology so far. OBJECTIVE: The objective of this study was to test apps that were previously mentioned in the scientific literature on a set of diagnoses in a deliberate time interval, comparing the results and looking for differences that hint at "nonlocked" learning algorithms. METHODS: Four apps from the literature were chosen (Ada, Babylon, Buoy, and Your.MD). A set of three ophthalmology diagnoses (glaucoma, retinal tear, dry eye syndrome) representing three levels of urgency was used to simultaneously test the apps' diagnostic efficiency and treatment recommendations in this specialty. Two years was the chosen time interval between the tests (2018 and 2020). Scores were awarded by one evaluating physician using a defined scheme. RESULTS: Two apps (Ada and Your.MD) received significantly higher scores than the other two. All apps either worsened in their results between 2018 and 2020 or remained unchanged at a low level. The variation in the results over time indicates "nonlocked" learning algorithms using AI technologies. None of the apps provided correct diagnoses and treatment recommendations for all three diagnoses in 2020. Two apps (Babylon and Your.MD) asked significantly fewer questions than the other two (P<.001). CONCLUSIONS: "Nonlocked" algorithms are used by self-diagnosis apps. The diagnostic efficiency of the tested apps seems to worsen over time, with some apps being more capable than others. Systematic studies on a wider scale are necessary for health care providers and patients to correctly assess the safety and efficacy of such apps and for correct classification by health care regulating authorities. ©Aleksandar Ćirković. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 04.12.2020.
BACKGROUND: Consumer-oriented mobile self-diagnosis apps have been developed using undisclosed algorithms, presumably based on machine learning and other artificial intelligence (AI) technologies. The US Food and Drug Administration now discerns apps with learning AI algorithms from those with stable ones and treats the former as medical devices. To the author's knowledge, no self-diagnosis app testing has been performed in the field of ophthalmology so far. OBJECTIVE: The objective of this study was to test apps that were previously mentioned in the scientific literature on a set of diagnoses in a deliberate time interval, comparing the results and looking for differences that hint at "nonlocked" learning algorithms. METHODS: Four apps from the literature were chosen (Ada , Babylon, Buoy, and Your.MD). A set of three ophthalmology diagnoses (glaucoma , retinal tear , dry eye syndrome ) representing three levels of urgency was used to simultaneously test the apps ' diagnostic efficiency and treatment recommendations in this specialty. Two years was the chosen time interval between the tests (2018 and 2020). Scores were awarded by one evaluating physician using a defined scheme. RESULTS: Two apps (Ada and Your.MD) received significantly higher scores than the other two. All apps either worsened in their results between 2018 and 2020 or remained unchanged at a low level. The variation in the results over time indicates "nonlocked" learning algorithms using AI technologies. None of the apps provided correct diagnoses and treatment recommendations for all three diagnoses in 2020. Two apps (Babylon and Your.MD) asked significantly fewer questions than the other two (P<.001). CONCLUSIONS: "Nonlocked" algorithms are used by self-diagnosis apps . The diagnostic efficiency of the tested apps seems to worsen over time, with some apps being more capable than others. Systematic studies on a wider scale are necessary for health care providers and patients to correctly assess the safety and efficacy of such apps and for correct classification by health care regulating authorities. ©Aleksandar Ćirković. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 04.12.2020.
Entities: Chemical
Disease
Gene
Species
Keywords:
artificial intelligence; mHealth; machine learning; medical diagnosis; mobile apps
Year: 2020
PMID： 33275113 DOI： 10.2196/18097
Source DB: PubMed Journal: J Med Internet Res ISSN： 1438-8871 Impact factor: 5.428