ArticleBMC health services research2022
Physicians' preferences and willingness to pay for artificial intelligence-based assistance tools: a discrete choice experiment among german radiologists.
Article in BMC health services research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses 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
14 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Preferences for AI-Enabled Health Care Technologies: Systematic Review of Discrete Choice Experiments and Reporting Quality Assessment Using the DIRECT Checklist.Journal of medical Internet research · 2026Pooled it
- The Evolving Landscape of Discrete Choice Experiments in Health Economics: A Systematic Review.PharmacoEconomics · 2025Pooled it
- Exploring tuberculosis physicians' preferences for AI explainability in China: a protocol for a discrete choice experiment.BMJ open · 2026Article
- Gender-dependent preferences of medical professionals in treatment choices for irritable bowel syndrome: results from an exploratory study.BMC gastroenterology · 2026Article
- The introduction and adoption of artificial intelligence in systematic literature reviews: a discrete choice experiment.BMJ open · 2025Article
- Artificial intelligence in radiation therapy treatment planning: A discrete choice experiment.Journal of medical radiation sciences · 2025Article
- Women's Preferences and Willingness to Pay for AI Chatbots in Women's Health: Discrete Choice Experiment Study.Journal of medical Internet research · 2025Article
- Article
- Physicians and AI in healthcare: insights from a mixed-methods study in Poland on adoption and challenges.Frontiers in digital health · 2025Article
- Preferences for attributes of an artificial intelligence-based risk assessment tool for HIV and sexually transmitted infections: a discrete choice experiment.BMC public health · 2024Article
- Artificial Intelligence in Healthcare: A Study of Physician Attitudes and Perceptions in Jeddah, Saudi Arabia.Cureus · 2024Article
- Radiology Residents' Perceptions of Artificial Intelligence: Nationwide Cross-Sectional Survey Study.Journal of medical Internet research · 2023Article
- An experimental machine learning study investigating the decision-making process of students and qualified radiographers when interpreting radiographic images.PLOS digital health · 2023Article
- Artificial intelligence in coronary computed tomography angiography: Demands and solutions from a clinical perspective.Frontiers in cardiovascular medicine · 2023Review
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
backgroundArtificial Intelligence (AI)-based assistance tools have the potential to improve the quality of healthcare when adopted by providers. This work attempts to elicit preferences and willingness to pay for these tools among German radiologists. The goal was to generate insights for tool providers and policymakers regarding the development and funding of ideally designed and priced tools. Ultimately, healthcare systems can only benefit from quality enhancing AI when provider adoption is considered.
methodsSince there is no established market for AI-based assistance tools in radiology yet, a discrete choice experiment was conducted. Respondents from the two major German professional radiology associations chose between hypothetical tools composed of five attributes and a no-choice option. The attributes included: provider, application, quality impact, time savings and price. A conditional logit model was estimated identifying preferences for attribute levels, the no-choice option, and significant subject-related interaction effects.
results114 respondents were included for analysis of which 46% were already using an AI-based assistance tool. Average adoption probability for an AI-based tool was 81% (95% CI 77.1% - 84.4%). Radiologists preferred a tool that assists in routine diagnostics performing at above-radiologist-level quality and saves 50% in diagnostics time at a price-point of €3 per study. The provider is not a significant factor in the decisions. Time savings were considered more important than quality improvements (i.e., detecting more anomalies).
conclusionsRadiologists are overall willing to invest in AI-based assistance tools. Development, funding, and research regarding these tools should, however, consider providers' preferences for features of immediate everyday and economic relevance like time savings to optimize adoption.
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.