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Conversational artificial intelligence interventions to support smoking cessation: A systematic review and meta-analysis

Truvace record
TRV-2026-2759raw data (JSON)
Version
v1Published
First published
Evidence type
Systematic review or meta-analysis
Sources
1 sourcePeer-reviewed
sha256
6925cdf3e845…check it

Background Conversational artificial intelligence (chatbots and dialogue systems) is an emerging tool for tobacco cessation that has the potential to emulate personalised human support and increase engagement. We aimed to determine the effect of conversational artificial intelligence interventions with or without standard tobacco cessation interventions on tobacco cessation outcomes among adults who smoke, compared to no intervention, placebo intervention or an active comparator. Methods A comprehensive search o…

The gain

A meta-analysis of three randomised trials found smokers using conversational AI-enhanced cessation interventions were 29% more likely to be abstinent at 6 months than control participants (RR 1.29, 95% CI 1.13 to 1.46).

What this doesn’t fix

The five trials differed in setting, methods, interventions, participants and end-points, loss to follow-up was generally high and risk of bias was high overall, so the authors urge caution.

Sources

1 on file
  1. DIGITAL HEALTHdoi:10.1177/20552076231211634
    Peer-reviewed2023-01-01

What the review found when it combined the studies

These are the review’s own pooled results, copied from its abstract. Truvace did not recalculate them.

  • Smoking abstinence at 6-month follow-up among adults using conversational AI cessation interventions versus controls

    Risk ratio 1.29 (95% CI 1.13 to 1.46)

    search to June 2022

    The review’s own words
    The random effects meta-analysis ( Figure 2 ) of abstinence outcomes at 6-month follow-up ( n = 3) found that participants in the conversational AI intervention were significantly more likely to quit smoking compared to participants in control conditions (RR = 1.29, 95% CI (1.13, 1.46), p < 0.001).

Code checks every number above against the review’s text before it is shown. The short labels are an AI model’s reading of that text, so the review’s own sentence sits under each one. A pooled figure is only as strong as the studies behind it. How Truvace uses pooled results

Cite this record

v1

Truvace Impact Record TRV-2026-2759, v1: “Conversational artificial intelligence interventions to support smoking cessation: A systematic review and meta-analysis.” Truvace, 2026-11-12. /record/TRV-2026-2759 (accessed on the day you copy it). sha256 6925cdf3e8456661…

Change history

1 version

Every change made to this record since it was first published, and why. Nothing has changed since it went up.

  1. Publishedv16925cdf3e845…

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