Characterizing Artificial Intelligence Applications in Cancer Research: A Latent Dirichlet Allocation Analysis

Bach Xuan Tran; Carl A Latkin; Noha Sharafeldin; Katherina Nguyen; Giang Thu Vu; Wilson W S Tam; Ngai-Man Cheung; Huong Lan Thi Nguyen; Cyrus S H Ho; Roger C M Ho

doi:10.2196/14401

Characterizing Artificial Intelligence Applications in Cancer Research: A Latent Dirichlet Allocation Analysis

JMIR Med Inform. 2019 Sep 15;7(4):e14401. doi: 10.2196/14401.

Authors

Bach Xuan Tran^{1

2}, Carl A Latkin², Noha Sharafeldin^{3

4}, Katherina Nguyen⁵, Giang Thu Vu⁶, Wilson W S Tam⁷, Ngai-Man Cheung^{8

9}, Huong Lan Thi Nguyen¹⁰, Cyrus S H Ho¹¹, Roger C M Ho^{12

13

14}

Affiliations

¹ Institute for Preventive Medicine and Public Health, Hanoi Medical University, Hanoi, Vietnam.
² Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, United States.
³ Division of Hematology & Oncology, Department of Medicine, University of Alabama at Birmingham, Birmingham, AL, United States.
⁴ Institute for Cancer Outcomes and Survivorship, School of Medicine, University of Alabama at Birmingham, Birmingham, AL, United States.
⁵ Department of Science, Technology, and Society, Stanford University, Palo Alto, CA, United States.
⁶ Center of Excellence in Evidence-based Medicine, Nguyen Tat Thanh University, Ho Chi Minh, Vietnam.
⁷ Alice Lee Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
⁸ Center of Excellence in Artiﬁcial Intelligence in Medicine, Nguyen Tat Thanh University, Ho Chi Minh, Vietnam.
⁹ Information Systems Technology and Design, Singapore University of Technology and Design, Singapore, Singapore.
¹⁰ Institute for Global Health Innovations, Duy Tan University, Da Nang, Vietnam.
¹¹ Department of Psychological Medicine, National University Hospital, Singapore, Singapore.
¹² Center of Excellence in Behavior Medicine, Nguyen Tat Thanh University, Ho Chi Minh, Vietnam.
¹³ Institute for Health Innovation and Technology, National University of Singapore, Singapore, Singapore.
¹⁴ Department of Psychological Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.

PMID: 31573929
PMCID: PMC6774235
DOI: 10.2196/14401

Abstract

Background: Artificial intelligence (AI)-based therapeutics, devices, and systems are vital innovations in cancer control; particularly, they allow for diagnosis, screening, precise estimation of survival, informing therapy selection, and scaling up treatment services in a timely manner.

Objective: The aim of this study was to analyze the global trends, patterns, and development of interdisciplinary landscapes in AI and cancer research.

Methods: An exploratory factor analysis was conducted to identify research domains emerging from abstract contents. The Jaccard similarity index was utilized to identify the most frequently co-occurring terms. Latent Dirichlet Allocation was used for classifying papers into corresponding topics.

Results: From 1991 to 2018, the number of studies examining the application of AI in cancer care has grown to 3555 papers covering therapeutics, capacities, and factors associated with outcomes. Topics with the highest volume of publications include (1) machine learning, (2) comparative effectiveness evaluation of AI-assisted medical therapies, and (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 are largely driven by machine learning, artificial neural networks, and AI in various clinical practices.

Conclusions: The research landscapes show that the development of AI in cancer care is focused on not only improving prediction in cancer screening and AI-assisted therapeutics but also on improving other corresponding areas such as precision and personalized medicine and patient-reported outcomes.

Keywords: artificial intelligence; cancer; global; mapping; scientometrics.

©Bach Xuan Tran, Carl A Latkin, Noha Sharafeldin, Katherina Nguyen, Giang Thu Vu, Wilson WS Tam, Ngai-Man Cheung, Huong Lan Thi Nguyen, Cyrus SH Ho, Roger CM Ho. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 15.09.2019.