ArticleSultan Qaboos University medical journal2026
Applications of Chatbots in Improving Patient Care Outcomes:
Article in Sultan Qaboos University medical journal, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Article
- Evaluating AI Chatbots in Prosthodontics Education: A Quantitative MCQ-Based Assessment.International journal of dentistry · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This review explored the application of chatbots in healthcare, focusing on patient monitoring, personalised care and medical services. It examined the potential of chatbots to improve patient outcomes through artificial intelligence-driven technologies, addressing challenges such as data security and system integration. This scoping review, conducted from January to March 2025, adhered to the PRISMA-ScR guidelines. A thorough literature search was performed across Web of Science, Scopus and PubMed, using keywords such as "patient care", "outcome", and "chatbot*". After screening for relevance and applying inclusion criteria, a total of 70 articles were analysed, focusing on chatbots' roles in improving patient care outcomes, data management and communication. Data charting was conducted by using a data extraction form to capture study characteristics, chatbot applications, outcomes and reported challenges. This study highlighted the surge in chatbot applications in healthcare from 2018 to 2024, focusing on 7 key themes: (1) increasing access to healthcare, (2) patient education and awareness (3) supporting clinical decision-making, (4) improving patient-healthcare professional communication, (5) chronic disease and symptom management, (6) telehealth and remote monitoring and (7) administrative support and workflow optimisation. Chatbots demonstrate significant potential to enhance patient care outcomes by improving access, communication, education and chronic disease management. Despite growing adoption of chatbots, challenges related to data security and system integration remain. Future research should focus on standardised evaluation frameworks and real-world clinical effectiveness.
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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.