Applications of Artificial Intelligence-Guided Clinical Decision Support in Disaster Medicine: An International Delphi Study

Jeffrey Michael Franc^1,2,3*Manuela Verde²Joseph Bonney⁴Kevin KC Hung⁵Joseph Cuthbertson⁶Liqaa A Raffee⁷Eduardo Serra⁸Marta Caviglia²

• ¹Universtiy of Alberta Faculty of Medicine and Dentistry, Edmonton, Canada
• ²Universita degli Studi del Piemonte Orientale Amedeo Avogadro, Vercelli, Italy
• ³Beth Israel Deaconess Medical Center, Boston, United States
• ⁴Komfo Anokye Teaching Hospital, Kumasi, Ghana
• ⁵The Chinese University of Hong Kong, Hong Kong, Hong Kong, SAR China
• ⁶The University of Notre Dame Australia, Perth, Australia
• ⁷Jordan University of Science and Technology, Irbid, Jordan
• ⁸Centro Único Coordinador de Ablación e Implante, Tierra del Fuego, Argentina

Background: Since the 1950's, artificial intelligence (AI) technologies have been beyond the reach of most disaster medicine (DM) practitioners. With the introduction of ChatGPT in 2022, there has been a surge of proposed applications for AI in disaster medicine. However, AI development is largely guided by vendors in high-income countries, and little is known of the needs of practitioners. This study provides an international perspective on the clinical problems that DM practitioners would like to see addressed by AI. Materials and Methods: A three round online Delphi study was performed by 131 international DM experts. In round one, experts were asked: "What specific clinical questions or problems in Disaster Medicine would you like to see addressed by artificial intelligence guided clinical decision support?" Statements from the first round were analyzed and collated for subsequent rounds where participants rated statements on a 7-point linear scale for importance. Results: In round one, 77 participants gave 539 proposed statements which were collated into 47 statements for subsequent rounds. In round two, 89 participants gave 3008 ratings with no statements reaching consensus. In round three, 63 participants gave 2942 ratings: Five statements reached consensus: distribution of disaster patients within the hospital, estimating the size of the affected population, hazard vulnerability analysis, acquisition and distribution of resources, and transportation routing. Experts tended to disagree with the use of AI for ethics, mental health, cultural sensitivity, or difficult treatment decisions. Conclusions: In this online Delphi study DM practitioners expressed a preference for AI tools that would help with the logistical support of their clinical responsibilities. Participants appeared to have much less support for the use of AI in making difficult or critical decisions. Development of AI for clinical decision support should focus on the needs of the users and be guided by an international perspective.