TY  - JOUR
AU  - Dawadi, Research
AU  - Inoue, Mai
AU  - Tay, Jie Ting
AU  - Martin-Morales, Agustin
AU  - Vu, Thien
AU  - Araki, Michihiro
PY  - 2025
DA  - 2025/3/25
TI  - Disease Prediction Using Machine Learning on Smartphone-Based Eye, Skin, and Voice Data: Scoping Review
JO  - JMIR AI
SP  - e59094
VL  - 4
KW  - literature review
KW  - machine learning
KW  - smartphone
KW  - health diagnosis
AB  - Background: The application of machine learning methods to data generated by ubiquitous devices like smartphones presents an opportunity to enhance the quality of health care and diagnostics. Smartphones are ideal for gathering data easily, providing quick feedback on diagnoses, and proposing interventions for health improvement. Objective: We reviewed the existing literature to gather studies that have used machine learning models with smartphone-derived data for the prediction and diagnosis of health anomalies. We divided the studies into those that used machine learning models by conducting experiments to retrieve data and predict diseases, and those that used machine learning models on publicly available databases. The details of databases, experiments, and machine learning models are intended to help researchers working in the fields of machine learning and artificial intelligence in the health care domain. Researchers can use the information to design their experiments or determine the databases they could analyze. Methods: A comprehensive search of the PubMed and IEEE Xplore databases was conducted, and an in-house keyword screening method was used to filter the articles based on the content of their titles and abstracts. Subsequently, studies related to the 3 areas of voice, skin, and eye were selected and analyzed based on how data for machine learning models were extracted (ie, the use of publicly available databases or through experiments). The machine learning methods used in each study were also noted. Results: A total of 49 studies were identified as being relevant to the topic of interest, and among these studies, there were 31 different databases and 24 different machine learning methods. Conclusions: The results provide a better understanding of how smartphone data are collected for predicting different diseases and what kinds of machine learning methods are used on these data. Similarly, publicly available databases having smartphone-based data that can be used for the diagnosis of various diseases have been presented. Our screening method could be used or improved in future studies, and our findings could be used as a reference to conduct similar studies, experiments, or statistical analyses. 
SN  - 2817-1705
UR  - https://ai.jmir.org/2025/1/e59094
UR  - https://doi.org/10.2196/59094
DO  - 10.2196/59094
ID  - info:doi/10.2196/59094
ER  -