TY - GEN
T1 - Automated Regulatory Classification of Mobile Medical Apps
AU - Chianumba, Ernest C.
AU - Kogolo, Franklyn
AU - Samuel, Raina
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025
Y1 - 2025
N2 - Mobile medical applications provide a variety of functionalities for users, from managing critical personal data to providing basic medical information. However, due to the variety of functionalities and lack of consistent and concrete regulatory oversight across app marketplaces, medical apps potentially pose a threat to users who are generally unaware of app capabilities. Therefore, in order to help legal experts quickly identify which regulatory body applies to medical apps used by consumers, we present a method to convert and plot the prose of both app descriptions and regulatory legalese into a vector space to facilitate rapid cosine similarity scoring. Our study demonstrates how to automate the regulation of mobile medical apps using descriptions in the language of regulatory bodies. Our results show a need for comprehensive regulatory oversight of medical apps, with 54.8% of apps on Google Play and 58% of apps on the Apple App Store.
AB - Mobile medical applications provide a variety of functionalities for users, from managing critical personal data to providing basic medical information. However, due to the variety of functionalities and lack of consistent and concrete regulatory oversight across app marketplaces, medical apps potentially pose a threat to users who are generally unaware of app capabilities. Therefore, in order to help legal experts quickly identify which regulatory body applies to medical apps used by consumers, we present a method to convert and plot the prose of both app descriptions and regulatory legalese into a vector space to facilitate rapid cosine similarity scoring. Our study demonstrates how to automate the regulation of mobile medical apps using descriptions in the language of regulatory bodies. Our results show a need for comprehensive regulatory oversight of medical apps, with 54.8% of apps on Google Play and 58% of apps on the Apple App Store.
KW - App Classification
KW - Cosine Similarity
KW - FDA
KW - FTC
KW - Mobile Medical Applications
KW - Natural Language Processing
KW - Regulatory Compliance
KW - Sentence Embeddings
UR - https://www.scopus.com/pages/publications/105010658458
U2 - 10.1109/CBMS65348.2025.00085
DO - 10.1109/CBMS65348.2025.00085
M3 - Conference contribution
AN - SCOPUS:105010658458
T3 - Proceedings - IEEE Symposium on Computer-Based Medical Systems
SP - 387
EP - 392
BT - Proceedings - 2025 IEEE 38th International Symposium on Computer-Based Medical Systems, CBMS 2025
A2 - Rodriguez-Gonzalez, Alejandro
A2 - Sicilia, Rosa
A2 - Prieto-Santamaria, Lucia
A2 - Papadopoulos, George A.
A2 - Guarrasi, Valerio
A2 - Cazzolato, Mirela Teixeira
A2 - Kane, Bridget
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 38th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2025
Y2 - 18 June 2025 through 20 June 2025
ER -