Files and links (1)
Preprint (Author's original)CC BY V4.0, Open
Preprint
A Comparison Between Human and Generative AI Decision-Making Attributes in Complex Health Services
arXiv (Cornell University)
Cornell University
13/05/2025
Metrics
1 Record Views
Abstract
A comparison between human and Generative AI decision-making attributes in complex health services is a knowledge gap in the literature, at present. Humans may possess unique attributes beneficial to decision-making in complex health services such as health policy and health regulation, but are also susceptible to decision-making flaws. The objective is to explore whether humans have unique, and/or helpful attributes that contribute to optimal decision-making in complex health services. This comparison may also shed light on whether humans are likely to compete, cooperate, or converge with Generative AI. The comparison is based on two published reviews: a scoping review of human attributes [1] and a rapid review of Generative AI attributes [2]. The analysis categorizes attributes by uniqueness and impact. The results are presented in tabular form, comparing the sets and subsets of human and Generative AI attributes. Humans and Generative AI decision-making attributes have complementary strengths. Cooperation between these two entities seems more likely than pure competition. To maintain meaningful decision-making roles, humans could develop their unique attributes, with decision-making systems integrating both human and Generative AI contributions. These entities may also converge, in future.
Details
- Title
- A Comparison Between Human and Generative AI Decision-Making Attributes in Complex Health Services
- Creators
- Nandini Doreswamy - Southern Cross UniversityLouise Horstmanshof - Southern Cross University
- Publication Details
- arXiv (Cornell University)
- Publisher
- Cornell University
- Number of pages
- 10
- Identifiers
- 991013285436302368
- Academic Unit
- Human Sciences; Faculty of Health
- Language
- English
- Resource Type
- Preprint