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Cocreating an Automated mHealth Apps Systematic Review Process With Generative AI: Design Science Research Approach.

Guido Giunti^1,2,3, Colin P Doherty^1,3,4

¹Academic Unit of Neurology, School of Medicine, Trinity College Dublin, Dublin, Ireland.

JMIR Medical Education

|February 12, 2024

Summary

Generative AI can automate systematic reviews for mobile health (mHealth) apps, saving time and resources. This approach aids researchers, but limitations in methodology and data quality exist.

Keywords:

AI AI tool ChatGPT apps design science research eHealth evidence-base generative AI generative artificial intelligence language model mHealth mHealth intervention mobile device qualitative study software code systematic review

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Area of Science:

Health Informatics
Artificial Intelligence in Research

Background:

Mobile health (mHealth) services are rapidly expanding, increasing the need for systematic reviews.
Systematic reviews are essential but are time-consuming and resource-intensive.
Generative AI offers a potential solution for automating these review tasks.

Approach:

Employed design science research methodology.
Utilized generative AI (ChatGPT) for cocreation to develop automated systematic review software code.
Developed a Python script to extract and analyze mHealth app data from the Google Play Store.

Key Points:

Generative AI successfully automated keyword searching in mHealth app descriptions.
Conversational AI assisted in debugging and refining the Python script.
Automated results were compared to traditional systematic review outputs.

Conclusions:

Generative AI shows significant potential for automating mHealth app systematic reviews.
This method can benefit researchers with limited coding expertise.
Limitations include potential bias and reliance on training data quality.