Version 1
: Received: 9 June 2024 / Approved: 11 June 2024 / Online: 12 June 2024 (11:20:20 CEST)
How to cite:
Bagheri, M.; Bagheritaba, M.; Alizadeh, S.; Parizi, M. S.; Matoufinia, P.; Luo, Y. AI-Driven Decision-Making in Healthcare Information Systems: A Comprehensive Review. Preprints2024, 2024060790. https://doi.org/10.20944/preprints202406.0790.v1
Bagheri, M.; Bagheritaba, M.; Alizadeh, S.; Parizi, M. S.; Matoufinia, P.; Luo, Y. AI-Driven Decision-Making in Healthcare Information Systems: A Comprehensive Review. Preprints 2024, 2024060790. https://doi.org/10.20944/preprints202406.0790.v1
Bagheri, M.; Bagheritaba, M.; Alizadeh, S.; Parizi, M. S.; Matoufinia, P.; Luo, Y. AI-Driven Decision-Making in Healthcare Information Systems: A Comprehensive Review. Preprints2024, 2024060790. https://doi.org/10.20944/preprints202406.0790.v1
APA Style
Bagheri, M., Bagheritaba, M., Alizadeh, S., Parizi, M. S., Matoufinia, P., & Luo, Y. (2024). AI-Driven Decision-Making in Healthcare Information Systems: A Comprehensive Review. Preprints. https://doi.org/10.20944/preprints202406.0790.v1
Chicago/Turabian Style
Bagheri, M., Parisa Matoufinia and Yang Luo. 2024 "AI-Driven Decision-Making in Healthcare Information Systems: A Comprehensive Review" Preprints. https://doi.org/10.20944/preprints202406.0790.v1
Abstract
Artificial Intelligence (AI) is revolutionizing decision-making procedures in healthcare information systems (HIS), increasing the efficacy, accuracy, and personalization of healthcare. Notwithstanding remarkable progresses, yet there remain gaps in the holistic comprehension and implication of AI-driven technologies across various HIS scenarios. These gaps, consisting limited integration of AI across a variety of HIS parts and inadequate emphasize on interpretability and privacy, motivate our study to conduct a structured evaluation of recent AI applications in this domain. In this study, we present a novel taxonomy for the AI implication in medical decision-making. We analyzed 30 articles and categorized them into six groups including: Clinical Decision Support Systems (CDSS), Predictive Analytics, Natural Language Processing (NLP), Computer-Aided Diagnostics (CAD), Robotic-Assisted Surgery, and Virtual Health Assistants (VHAs/chatbots). Our study aims to propose a holistic perspective of the fast evolving perspective of AI in HIS. The majority of the investigated studies were published from 2023 and 2022, with Springer as the leading publisher (33%). Python and MATLAB were the most well-known simulation languages, applied in 48% and 20% of the papers, respectively, reflecting the technical trends in the area. By emphasizing on pivotal criteria like interpretability, accuracy, and privacy, this research aims to elucidate the crucial issues and development driving AI-driven decision-making in HIS.
Keywords
Information Systems; Artificial Intelligence; Healthcare; Clinical Decision Support Systems; Predictive Analytics; Natural Language Processing; Computer-aided /diagnostics; Robot-assisted Surgery; Virtual Health Assistants and Chatbots
Subject
Public Health and Healthcare, Public Health and Health Services
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
