ReviewCureus2024
Human-Computer Interaction: A Literature Review of Artificial Intelligence and Communication in Healthcare.
Review in Cureus, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled 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.
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
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Stakeholder experience with artificial intelligence in healthcare: a bibliometric study of satisfaction, trust, acceptance, and patient engagement.Frontiers in digital health · 2026Pooled it
- Behavioral Mechanisms of a Digital Health Intervention for Self-Management in Type 2 Diabetes Mellitus: Prospective Longitudinal Cohort Study.JMIR mHealth and uHealth · 2026Article
- Concept2Brain: an AI model for predicting neurophysiological responses to text and pictures.Nature communications · 2026Article
- Review
- Goals of Care Discussions in Medical Training: Integrating Palliative Care for Holistic, Patient-Centered Care.Healthcare (Basel, Switzerland) · 2026Review
- Review
- Evaluation of AI-generated versus registered dietitian-authored nutrition responses: a cross-sectional study.mHealth · 2026Article
- Towards a sensitive process of assent in pediatric clinical trials: a qualitative study on the patient perspective.European journal of pediatrics · 2025Article
- Application of AI Communication Training Tools in Medical Undergraduate Education: Mixed Methods Feasibility Study Within a Primary Care Context.JMIR medical education · 2025Article
- Readability of patient education materials for lumbar disc replacement.Journal of spine surgery (Hong Kong) · 2025Article
- Integration of Generative AI with Human Expertise in HEOR: A Hybrid Intelligence Framework.Advances in therapy · 2025Review
- Transforming hematological research documentation with large language models: an approach to scientific writing and data analysis.Blood research · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The integration of artificial intelligence (AI) into healthcare communication has rapidly evolved, driven by advancements in large language models (LLMs) such as Chat Generative Pre-trained Transformer (ChatGPT). This literature review explores AI's role in patient-physician interactions, particularly focusing on its capacity to enhance communication by bridging language barriers, summarizing complex medical data, and offering empathetic responses. AI's strengths lie in its ability to deliver comprehensible, concise, and medically accurate information. Studies indicate AI can outperform human physicians in certain communicative aspects, such as empathy and clarity, with models like ChatGPT and the Medical Pathways Language Model (Med-PaLM) demonstrating high effectiveness in these areas. However, significant challenges remain, including occasional inaccuracies and "hallucinations," where AI-generated content is irrelevant or medically inaccurate. These limitations highlight the need for continued refinement in AI algorithms to ensure reliability and consistency in sensitive healthcare settings. The review underscores the potential of AI as a transformative tool in health communication while advocating for further research and policy development to mitigate risks and enhance AI's integration into clinical practice.
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.