Literature DB >> 31980086

Implementation of artificial intelligence in medicine: Status analysis and development suggestions.

Yifan Xiang1, Lanqin Zhao1, Zhenzhen Liu1, Xiaohang Wu1, Jingjing Chen1, Erping Long1, Duoru Lin1, Yi Zhu2, Chuan Chen2, Zhuoling Lin1, Haotian Lin3.   

Abstract

The general public's attitudes, demands, and expectations regarding medical AI could provide guidance for the future development of medical AI to satisfy the increasing needs of doctors and patients. The objective of this study is to investigate public perceptions, receptivity, and demands regarding the implementation of medical AI. An online questionnaire was designed to investigate the perceptions, receptivity, and demands of general public regarding medical AI between October 13 and October 30, 2018. The distributions of the current achievements, public perceptions, receptivity, and demands among individuals in different lines of work (i.e., healthcare vs non-healthcare) and different age groups were assessed by performing descriptive statistics. The factors associated with public receptivity of medical AI were assessed using a linear regression model. In total, 2,780 participants from 22 provinces were enrolled. Healthcare workers accounted for 54.3 % of all participants. There was no significant difference between the healthcare workers and non-healthcare workers in the high proportion (99 %) of participants expressing acceptance of AI (p = 0.8568), but remarkable distributional differences were observed in demands (p < 0.001 for both demands for AI assistance and the desire for AI improvements) and perceptions (p < 0.001 for safety, validity, trust, and expectations). High levels of receptivity (approximately 100 %), demands (approximately 80 %), and expectations (100 %) were expressed among different age groups. The receptivity of medical AI among the non-healthcare workers was associated with gender, educational qualifications, and demands and perceptions of AI. There was a very large gap between current availability of and public demands for intelligence services (p < 0.001). More than 90 % of healthcare workers expressed a willingness to devote time to learning about AI and participating in AI research. The public exhibits a high level of receptivity regarding the implementation of medical AI. To date, the achievements have been rewarding, and further advancements are required to satisfy public demands. There is a strong demand for intelligent assistance in many medical areas, including imaging and pathology departments, outpatient services, and surgery. More contributions are imperative to facilitate integrated and advantageous implementation in medical AI.
Copyright © 2019 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Current implementation; Future development; Medical artificial intelligence; Public demand

Mesh:

Year:  2019        PMID: 31980086     DOI: 10.1016/j.artmed.2019.101780

Source DB:  PubMed          Journal:  Artif Intell Med        ISSN: 0933-3657            Impact factor:   5.326


  9 in total

1.  AI-enhanced solutions during COVID-19: Current trends and future innovations.

Authors:  Faisal A Nawaz; Abdul Rahman Khan; Thomas Boillat
Journal:  Ann Med Surg (Lond)       Date:  2022-07-13

Review 2.  The role of artificial intelligence in pancreatic surgery: a systematic review.

Authors:  D Schlanger; F Graur; C Popa; E Moiș; N Al Hajjar
Journal:  Updates Surg       Date:  2022-03-02

Review 3.  A Survey on the Role of Artificial Intelligence in Biobanking Studies: A Systematic Review.

Authors:  Gopi Battineni; Mohmmad Amran Hossain; Nalini Chintalapudi; Francesco Amenta
Journal:  Diagnostics (Basel)       Date:  2022-05-09

Review 4.  Implementation Frameworks for Artificial Intelligence Translation Into Health Care Practice: Scoping Review.

Authors:  Fábio Gama; Daniel Tyskbo; Jens Nygren; James Barlow; Julie Reed; Petra Svedberg
Journal:  J Med Internet Res       Date:  2022-01-27       Impact factor: 5.428

Review 5.  Perceptions and Needs of Artificial Intelligence in Health Care to Increase Adoption: Scoping Review.

Authors:  Han Shi Jocelyn Chew; Palakorn Achananuparp
Journal:  J Med Internet Res       Date:  2022-01-14       Impact factor: 5.428

6.  Knowledge, attitude, and practice of artificial intelligence among doctors and medical students in Pakistan: A cross-sectional online survey.

Authors:  Zaboor Ahmed; Khurram Khaliq Bhinder; Amna Tariq; Muhammad Junaid Tahir; Qasim Mehmood; Muhammad Saad Tabassum; Muna Malik; Sana Aslam; Muhammad Sohaib Asghar; Zohaib Yousaf
Journal:  Ann Med Surg (Lond)       Date:  2022-03-14

7.  Profiling of the Conjunctival Bacterial Microbiota Reveals the Feasibility of Utilizing a Microbiome-Based Machine Learning Model to Differentially Diagnose Microbial Keratitis and the Core Components of the Conjunctival Bacterial Interaction Network.

Authors:  Zhichao Ren; Wenfeng Li; Qing Liu; Yanling Dong; Yusen Huang
Journal:  Front Cell Infect Microbiol       Date:  2022-04-26       Impact factor: 6.073

8.  Readiness to Embrace Artificial Intelligence Among Medical Doctors and Students: Questionnaire-Based Study.

Authors:  Thomas Boillat; Faisal A Nawaz; Homero Rivas
Journal:  JMIR Med Educ       Date:  2022-04-12

9.  Development of an Interoperable and Easily Transferable Clinical Decision Support System Deployment Platform: System Design and Development Study.

Authors:  Junsang Yoo; Jeonghoon Lee; Ji Young Min; Sae Won Choi; Joon-Myoung Kwon; Insook Cho; Chiyeon Lim; Mi Young Choi; Won Chul Cha
Journal:  J Med Internet Res       Date:  2022-07-27       Impact factor: 7.076

  9 in total

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