A Comparison Between Human and Generative AI Decision-Making Attributes in Complex Health Services

TL;DR

This paper compares human and Generative AI decision-making attributes in complex health services, highlighting their complementary strengths and suggesting cooperation and convergence for improved decision-making.

Contribution

It provides a novel comparison of human and AI decision attributes in health services, emphasizing their potential for collaboration and integration.

Findings

Humans and AI have complementary decision-making strengths.

Cooperation between humans and AI is more likely than competition.

Integration of both can enhance decision quality.

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.