Reviewnpj health systems2025
Trust in AI-assisted health systems and AI's trust in humans.
Review in npj health systems, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 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
34 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Implementation of Total Body Photography for Skin Cancer Early Detection: A Systematic Review.International journal of dermatology · 2026Pooled it
- Bridging the trust-adoption gap for AI scribes in rural communities: A machine learning approach using the 2024 Canadian digital health survey.International journal of medical informatics · 2026Article
- Building health-literate artificial intelligence.Nature human behaviour · 2026Review
- A pragmatic risk-stratified framework for using large language models in intensive care medicine: A narrative review.Critical care and resuscitation : journal of the Australasian Academy of Critical Care Medicine · 2026Review
- Novelty to necessity: large language models in clinical practice-a call to action for physicians.Internal and emergency medicine · 2026Review
- Artificial intelligence in robotic urologic surgery: a scoping review.World journal of urology · 2026Article
- GPT Fusion: Reasoning-Based Integration of Specialized Convolutional Neural Networks for Melanoma Diagnosis.Research square · 2026Article
- Alignment of Policy, Practice, and Patient Safety for Trustworthy AI in Radiology.Radiology. Artificial intelligence · 2026Article
- From Bench to Bedside: The Path Toward Real-World Translation for Artificial Intelligence in Pancreatic Cancer Detection.Korean journal of radiology · 2026Review
- Financial incentives increase uptake and perceived effectiveness of autonomous medical AI, yet patients still seek human reconfirmation.NPJ digital medicine · 2026Article
- Why care resists coding: the moral limits of medical AI.BMC medical ethics · 2026Article
- Patients and donors trust in data authorization for AI-based medical research.Scientific reports · 2026Article
- Bridging functional bionanomaterials and the clinic: strategic communication as a translational enabler.Journal of nanobiotechnology · 2026Review
- Eye-Tracked Visual Attention to Anthropomorphic Appearance and Empathic Responses in AI Medical Conversational Agents: Dissociating Trust Gains from Attentional Synergy.Journal of eye movement research · 2026Article
- Healthcare Providers' Perspectives on Generative Artificial Intelligence (GenAI) Adoption, Adaptation, Assimilation, and Use in the United States.Healthcare (Basel, Switzerland) · 2026Article
- Extrinsic Trust as a Contractual Framework for Accountable AI in Health Care: Viewpoint.Journal of medical Internet research · 2026Article
- Ethical Imperatives for Retrieval-Augmented Generation in Clinical Nursing: Viewpoint on Responsible AI Use.JMIR medical informatics · 2026Article
- Community perspectives on health AI: hopes, concerns and implications for health systems and trustworthy AI.AI and ethics · 2026Article
- Nurses as guardians of time: the hidden clinical value of continuous care in geriatrics.Frontiers in public health · 2026Review
- Global English-language-dominated discourse on artificial intelligence in healthcare: a three-year longitudinal analysis of the #AIinHealthcare movement on X.Frontiers in digital health · 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
4 authors.
Funding
Abstract
Artificial intelligence (AI) is reshaping healthcare, promising improved diagnostics, personalized treatments, and streamlined operations. Yet a lack of trust remains a persistent barrier to widespread adoption. This Perspective examines the web of trust in AI-assisted healthcare systems, exploring the relationships it shapes, the systemic inequalities it can reinforce, and the technical challenges it poses. We highlight the bidirectional nature of trust, in which both patients and providers must trust AI systems, while these systems rely on the quality of human input to function effectively. Using models of care-seeking behavior, we explore the potential of AI to affect patients' decisions to seek care, influence trust in healthcare providers and institutions, and affect diverse demographic and clinical settings. We argue that addressing trust-related challenges requires rigorous empirical research, equitable algorithm design, and shared accountability frameworks. Ultimately, AI's impact hinges not just on technical progress but on sustaining trust, which may erode if biases persist, transparency falters, or incentives misalign.
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.