@Article{info:doi/10.2196/63149,
author="Downing, Gregory J
and Tramontozzi, Lucas M
and Garcia, Jackson
and Villanueva, Emma",
title="Harnessing Internet Search Data as a Potential Tool for Medical Diagnosis: Literature Review",
journal="JMIR Ment Health",
year="2025",
month="Feb",
day="11",
volume="12",
pages="e63149",
keywords="health; informatics; internet search data; early diagnosis; web search; information technology; internet; machine learning; medical records; diagnosis; health care; self-diagnosis; detection; intervention; patient education; internet search; health-seeking behavior; artificial intelligence; AI",
abstract="Background: The integration of information technology into health care has created opportunities to address diagnostic challenges. Internet searches, representing a vast source of health-related data, hold promise for improving early disease detection. Studies suggest that patterns in search behavior can reveal symptoms before clinical diagnosis, offering potential for innovative diagnostic tools. Leveraging advancements in machine learning, researchers have explored linking search data with health records to enhance screening and outcomes. However, challenges like privacy, bias, and scalability remain critical to its widespread adoption. Objective: We aimed to explore the potential and challenges of using internet search data in medical diagnosis, with a specific focus on diseases and conditions such as cancer, cardiovascular disease, mental and behavioral health, neurodegenerative disorders, and nutritional and metabolic diseases. We examined ethical, technical, and policy considerations while assessing the current state of research, identifying gaps and limitations, and proposing future research directions to advance this emerging field. Methods: We conducted a comprehensive analysis of peer-reviewed literature and informational interviews with subject matter experts to examine the landscape of internet search data use in medical research. We searched for published peer-reviewed literature on the PubMed database between October and December 2023. Results: Systematic selection based on predefined criteria included 40 articles from the 2499 identified articles. The analysis revealed a nascent domain of internet search data research in medical diagnosis, marked by advancements in analytics and data integration. Despite challenges such as bias, privacy, and infrastructure limitations, emerging initiatives could reshape data collection and privacy safeguards. Conclusions: We identified signals correlating with diagnostic considerations in certain diseases and conditions, indicating the potential for such data to enhance clinical diagnostic capabilities. However, leveraging internet search data for improved early diagnosis and health care outcomes requires effectively addressing ethical, technical, and policy challenges. By fostering interdisciplinary collaboration, advancing infrastructure development, and prioritizing patient engagement and consent, researchers can unlock the transformative potential of internet search data in medical diagnosis to ultimately enhance patient care and advance health care practice and policy. ",
issn="2368-7959",
doi="10.2196/63149",
url="https://mental.jmir.org/2025/1/e63149",
url="https://doi.org/10.2196/63149",
url="http://www.ncbi.nlm.nih.gov/pubmed/39813106"
}