Objective: To conduct a systematic review on Artificial Intelligence-Mediated Communication (AIMC) behavioral interventions in cancer prevention/control and substance use.
Methods: Eight databases were searched from 2017 to 2022 using the Population Intervention Control Outcome Study (PICOS) framework. We synthesized findings of AIMC-based interventions for adult populations in cancer prevention/control or substance use, applying SIGN Methodology Checklist 2 for quality assessments and reviewing retention and engagement.
Results: Initial screening identified 187 studies; seven met inclusion criteria, involving 2768 participants. Females comprised 67.6% (n = 1870). Mean participant age was 42.73 years (SD = 7.00). Five studies demonstrated significant improvements in substance use recovery, physical activity, genetic testing, or dietary habits.
Conclusions: AIMC shows promise in enhancing health behaviors, but further exploration is needed on privacy risks, biases, safety concerns, chatbot features, and serving underserved populations.
Implications: There is a critical need to foster comprehensive fully powered studies and collaborations between technology developers, healthcare providers, and researchers. Policymakers can facilitate the responsible integration of AIMC technologies into healthcare systems, ensuring equitable access and maximizing their impact on public health outcomes.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1093/tbm/ibaf007 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Leveraging artificial intelligence-mediated communication for cancer prevention and control and drug addiction: A systematic review.
Transl Behav Med
January 2025
Department of Social and Behavioral Sciences, School of Public Health, Virginia Commonwealth University, 830 East Main Street, Richmond, VA 23219, USA.
Objective: To conduct a systematic review on Artificial Intelligence-Mediated Communication (AIMC) behavioral interventions in cancer prevention/control and substance use.
Methods: Eight databases were searched from 2017 to 2022 using the Population Intervention Control Outcome Study (PICOS) framework. We synthesized findings of AIMC-based interventions for adult populations in cancer prevention/control or substance use, applying SIGN Methodology Checklist 2 for quality assessments and reviewing retention and engagement.
Usefulness of the artificial intelligence-mediated virtual chromoendoscopy in peroral cholangioscopy.
Endoscopy
December 2023
Department of Gastroenterology and Hepatology, Okayama University Hospital, Okayama, Japan.
Computer Vision-Based Artificial Intelligence-Mediated Encoding-Decoding for Multiplexed Microfluidic Digital Immunoassay.
ACS Nano
July 2023
College of Food Science and Technology, Huazhong Agricultural University, Wuhan, 430070, Hubei China.
Article Synopsis
- A new digital immunoassay system has been developed that uses microparticles and AI to detect multiple targets with high sensitivity.
- This system works by encoding information in the properties of microspheres, allowing for quick identification and quantification of various substances in a single test.
- The technology is integrated with a microfluidic platform and can analyze multiple inflammatory markers and antibiotics in just 30 minutes, making it a promising tool for clinical diagnosis and environmental monitoring.
The Steep Road to Artificial Intelligence-mediated Radiology.
Radiol Artif Intell
March 2023
Department of Radiology, University Hospital Tuebingen, Tuebingen, Germany (S.U.); Department of Radiology, University of Cambridge School of Clinical Medicine, Box 218, Cambridge Biomedical Campus, Hills Road, Cambridge CB2 0QQ, England (S.U., R.W.); and Research Center Medical Image Analysis and AI (MIAAI), Danube Private University, Krems, Austria (R.W.).
Feature Tracking and Segmentation in Real Time via Deep Learning in Vitreoretinal Surgery: A Platform for Artificial Intelligence-Mediated Surgical Guidance.
Ophthalmol Retina
March 2023
Department of Ophthalmology and Visual Sciences - Illinois Eye and Ear Infirmary, University of Illinois at Chicago, Chicago, Illinois; Richard and Loan Hill Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, Illinois. Electronic address:
Purpose: This study investigated whether a deep-learning neural network can detect and segment surgical instrumentation and relevant tissue boundaries and landmarks within the retina using imaging acquired from a surgical microscope in real time, with the goal of providing image-guided vitreoretinal (VR) microsurgery.
Design: Retrospective analysis via a prospective, single-center study.
Participants: One hundred and one patients undergoing VR surgery, inclusive of core vitrectomy, membrane peeling, and endolaser application, in a university-based ophthalmology department between July 1, 2020, and September 1, 2021.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!