Evidence map›Paper›PMID 42022146›Full record

SynthesisFrontiers in artificial intelligence2026

Leveraging chatbots for enhanced decision-making: a comprehensive literature review.

Phiwe M Simelane, Javeed Kittur

Abstract readSystematic Review
In one paragraph

Synthesis in Frontiers in artificial intelligence, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Phiwe M SimelaneElectrical Engineering, Gallogly College of Engineering, University of Oklahoma, Norman, OK, United States.
Javeed KitturElectrical Engineering, Gallogly College of Engineering, University of Oklahoma, Norman, OK, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Chatbots using large language models such as ChatGPT, Google Bard, etc., have become increasingly popular in recent years. The chatbot's ability to simulate conversations with users and process input data and respond based on that information has prompted researchers to investigate the applicability of chatbots in decision-making processes across multiple fields. Current literature has investigated the benefits and challenges of using chatbots in a broad context. This paper presents a systematic literature review of current literature discussing chatbots and decision-making processes, exploring the quality of the decision-making process and user perceptions of using chatbots in the decision-making process. Methods: This SLR aims to provide a comprehensive view of the disciplinary fields in which chatbot decision-making has been evaluated, how chatbots can be used in various fields, and how it compares to human decision-making. Thirty-six articles from seven databases were reviewed in this paper and categorized into six themes: benefits of using chatbot for decision-making, challenges of chatbot-supported decision-making, ethical considerations of using chatbot-supported decision-making, algorithms/tools used in designing chatbots, human vs. AI decision-making, and chatbot decision-making in different fields. Results: An analysis of these themes revealed (i) benefits of personalized recommendations in decision-making, (ii) issues with inconsistency in output, (iii) ethical concerns about chatbots using sensitive information to make decisions, (iv) ChatGPT's decision-making is the most studied, and (v) human vs. AI decision-making. Discussion: The practical and research implications of these findings are further explained in the paper.

Indexed as

chatbotChatGPTdecision-makingLLM-based chatbotssystematic literature review

Identifiers

PMID42022146
PMCPMC13095736

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.