Evidence map›Paper›PMID 40255778›Full record

ReviewCureus2025

Revolutionizing Medicine: Chatbots as Catalysts for Improved Diagnosis, Treatment, and Patient Support.

Syed A Abbas, Izmir Yusifzada, Sufia Athar

Abstract readReview
In one paragraph

Review in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

3 authors.

Syed A AbbasHealth, Delhi Public School - Modern Indian School, Doha, QAT.
Izmir YusifzadaHealth, Secondary School Number 210, Baku, AZE.
Sufia AtharDepartment of Obstetrics and Gynaecology, Al Wakra Hospital, Hamad Medical Corporation, Doha, QAT.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Chatbots have emerged as one of the revolutionary tools in healthcare that combine artificial intelligence to improve patient engagement in diagnosis, treatment, monitoring, and support. In this review, the application of chatbots in medicine over the past two decades is discussed, focusing on their application in several aspects of medicine, including oncology, psychiatry, and chronic disease management. A total of 38 clinically relevant studies were identified from a comprehensive literature search. These studies demonstrate that the efficacy of chatbots in early symptom detection, personalized treatment planning, and emotional support has a particular emphasis on addressing mental health needs. The review focuses on the role of chatbots in improving the communication gap between patients and health providers to achieve improved access to medical information and treatment adherence. The findings underscore the substantial promise of chatbots in enhancing healthcare delivery and reveal areas that need further exploration, particularly in radiology and advanced diagnostics. With the continuous rise in technological development, the use of chatbots is expected to become more central in streamlining health processes toward better patient outcomes and enhanced health literacy. This review, therefore, calls for continued investment in the incorporation of these chatbot innovations in healthcare systems for their full potential to be realized in patient care.

Indexed as

artificial intelligencechatbothealthpreventionscreening

Identifiers

PMID40255778
PMCPMC12009506

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.