Accepted for/Published in: JMIR Medical Informatics
Date Submitted: Apr 16, 2019
Date Accepted: Jul 19, 2019
Characterizing Artificial Intelligence Applications in Cancer Research using Latent Dirichlet Allocation
ABSTRACT
Background:
Artificial Intelligence (AI) - based therapeutics, devices and systems are vital innovations in cancer control.
Objective:
This study analyzes the global trends, patterns, and development of interdisciplinary landscapes in AI and cancer research.
Methods:
Exploratory factor analysis was applied to identify research domains emerging from contents of the abstracts. Jaccard’s similarity index was utilized to identify terms most frequently co-occurring with each other. Latent Dirichlet Allocation was used for classifying papers into corresponding topics.
Results:
The number of studies applying AI to cancer during 1991-2018 has been grown with 3,555 papers covering therapeutics, capacities, and factors associated with outcomes. Topics with the highest volumes of publications include 1) Machine learning, 2) Comparative Effectiveness Evaluation of AI-assisted medical therapies, 3) AI-based Prediction. Noticeably, this classification has revealed topics examining the incremental effectiveness of AI applications, the quality of life and functioning of patients receiving these innovations. The growing research productivity and expansion of multidisciplinary approaches, largely driven by machine learning, artificial neutral network, and artificial intelligence in various clinical practices.
Conclusions:
The research landscapes show that the development of AI in cancer is focused not only on improving prediction in cancer screening and AI-assisted therapeutics, but also other corresponding areas such as Precision and Personalized Medicine and patient-reported outcomes.
Citation
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Copyright
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