ReviewCureus2025
Artificial Intelligence-Based Chatbots in Chronic Disease Management: A Systematic Review of Applications and Challenges.
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.
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.
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.
Who cites it
9 citing papers in PubMed.
- Behavioral Economics and Medication Adherence for Hypertension: A Randomized Clinical Trial.Journal of the American College of Cardiology · 2026Trial
- Behavior Change Content and Implementation of Large Language Model-Driven Conversational Agents in Cardiometabolic Care: Scoping Review.Journal of medical Internet research · 2026Article
- Artificial intelligence (AI)-assisted full-course case management for primary liver cancer: System design, preliminary implementation, and practical considerations.Global health & medicine · 2026Article
- Review
- "Your Digital Doctor Will Now See You": A Narrative Review of VR and AI Technology in Chronic Illness Management.Healthcare (Basel, Switzerland) · 2026Review
- Artificial Intelligence in Medicine: A Definition of Terms.Journal of community hospital internal medicine perspectives · 2026Article
- Artificial intelligence applications in oxaliplatin-based chemotherapy for colon cancer: advancing prognosis, toxicity prediction, and dose personalization.Frontiers in pharmacology · 2026Review
- Review
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
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
Identifiers
What OpenQuestion holds
Registered trials
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.