Evidence map›Paper›PMID 40260325›Full record

ReviewCureus2025

Artificial Intelligence-Based Chatbots in Chronic Disease Management: A Systematic Review of Applications and Challenges.

Dalia Saad Altom, Allaa Ibrahim Awad Taha, Alaa Abu Agla Mahmoud Hussein, Maisoon Ahmed Ibrahim Elshiekh, Ashwag Hassan Alata Abdelmajed, Fatima Ibrahim Abdalla Ibrahim, Sara Mohammed Abelgadir Mohammed, Mosab Mohamed Elamin Eltain Tifoor

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 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Trial
  2. Article
  3. Article
  4. Review
  5. Review
  6. Artificial Intelligence in Medicine: A Definition of Terms.Journal of community hospital internal medicine perspectives · 2026
    Article
  7. Review
  8. Review
  9. Review
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

8 authors.

Dalia Saad AltomFamily Medicine, Najran Armed Forces Hospital, Ministry of Defense Health Services, Najran, SAU.
Allaa Ibrahim Awad TahaGeneral Practice, Wad Madani Hospital, Wad Madani, SDN.
Alaa Abu Agla Mahmoud HusseinGeneral Practice, Mediclinic Airport Road Hospital, Abu Dhabi, ARE.
Maisoon Ahmed Ibrahim ElshiekhNephrology, Gezira Hospital for Renal Disease and Surgery, Wad Madani, SDN.
Ashwag Hassan Alata AbdelmajedInternal Medicine, Najran Armed Forces Hospital, Ministry of Defense Health Services, Najran, SAU.
Fatima Ibrahim Abdalla IbrahimInternal Medicine, Najran Armed Forces Hospital, Ministry of Defense Health Services, Najran, SAU.
Sara Mohammed Abelgadir MohammedInternal Medicine, Najran Armed Forces Hospital, Ministry of Defense Health Services, Najran, SAU.
Mosab Mohamed Elamin Eltain TifoorInternal Medicine, Najran Armed Forces Hospital, Ministry of Defense Health Services, Najran, SAU.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) is being used by an increasing number of conversational agents, sometimes known as chatbots. In applications related to health care, such as those that educate and assist patients with chronic illnesses, which are among the main causes of mortality in the 21st century, they are becoming more and more common. Chatbots powered by AI allows for more frequent and efficient engagement with these patients. This systematic review aimed to examine the traits, medical conditions, and AI architectures of conversational agents that are based on artificial intelligence and are specifically made for chronic illnesses. We searched four databases (Scopus, Web of Science, PubMed, and Cumulative Index to Nursing and Allied Health Literature [CINAHL]) to search for relevant studies using specific inclusion and exclusion criteria. Among these databases, we found 386 studies that were screened for duplicates and then assessed by inclusion and exclusion criteria. We included the 10 most relevant studies in this systemic review. There is a dearth of research on AI-based interactive agents for chronic illnesses, and what little is available is primarily quasi-experimental studies, including chatbots in prototype stages that employ natural language processing (NLP) and enable multimodal user engagement. Future studies could benefit from comparing and evaluating AI-based conversational bots within and between various chronic health disorders using evidence-based methodology. In addition to improving comparability, more structured development and standardized evaluation procedures could improve the caliber of chatbots created for certain chronic diseases and their subsequent effects on the target patients.

Indexed as

artificial intelligencechatbotschronic diseasesconversational agentshealthcare

Identifiers

PMID40260325
PMCPMC12011281

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.