ReviewHealthcare (Basel, Switzerland)2022
The Artificial Intelligence in Digital Radiology: Part 1: The Challenges, Acceptance and Consensus.
Review in Healthcare (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 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
15 citing papers in PubMed.
- Surveying the Digital Cytology Workflow in Italy: An Initial Report on AI Integration Across Key Professional Roles.Healthcare (Basel, Switzerland) · 2025Article
- Navigating the AI landscape in SMEs: Overcoming internal challenges and external obstacles for effective integration.PloS one · 2025Article
- Revolutionizing Radiology with Natural Language Processing and Chatbot Technologies: A Narrative Umbrella Review on Current Trends and Future Directions.Journal of clinical medicine · 2024Review
- The Integration of Deep Learning in Radiotherapy: Exploring Challenges, Opportunities, and Future Directions through an Umbrella Review.Diagnostics (Basel, Switzerland) · 2024Review
- Beyond the Clinic Walls: Examining Radiology Technicians' Experiences in Home-Based Radiography.Healthcare (Basel, Switzerland) · 2024Article
- A Fully Automated Post-Surgical Brain Tumor Segmentation Model for Radiation Treatment Planning and Longitudinal Tracking.Cancers · 2023Article
- Advancing Dermatological Care: A Comprehensive Narrative Review of Tele-Dermatology and mHealth for Bridging Gaps and Expanding Opportunities beyond the COVID-19 Pandemic.Healthcare (Basel, Switzerland) · 2023Review
- Human-Machine Collaboration in Diagnostics: Exploring the Synergy in Clinical Imaging with Artificial Intelligence.Diagnostics (Basel, Switzerland) · 2023Article
- The Artificial Intelligence in Teledermatology: A Narrative Review on Opportunities, Perspectives, and Bottlenecks.International journal of environmental research and public health · 2023Review
- Ten Years of TeleHealth and Digital Healthcare: Where Are We?Healthcare (Basel, Switzerland) · 2023Article
- Artificial Intelligence and Machine Learning Based Intervention in Medical Infrastructure: A Review and Future Trends.Healthcare (Basel, Switzerland) · 2023Review
- The Regulation of Artificial Intelligence in Digital Radiology in the Scientific Literature: A Narrative Review of Reviews.Healthcare (Basel, Switzerland) · 2022Review
- Artificial Intelligence in Public Health: Current Trends and Future Possibilities.International journal of environmental research and public health · 2022Article
- Assistive Technologies, Robotics, Automatic Machines: Perspectives of Integration in theHealthcare (Basel, Switzerland) · 2022Article
- Ethics and Automated Systems in the Health Domain: Design and Submission of a Survey on Rehabilitation and Assistance Robotics to Collect Insiders' Opinions and Perception.Healthcare (Basel, Switzerland) · 2022Article
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
Artificial intelligence is having important developments in the world of digital radiology also thanks to the boost given to the research sector by the COVID-19 pandemic. In the last two years, there was an important development of studies focused on both challenges and acceptance and consensus in the field of Artificial Intelligence. The challenges and acceptance and consensus are two strategic aspects in the development and integration of technologies in the health domain. The study conducted two narrative reviews by means of two parallel points of view to take stock both on the ongoing challenges and on initiatives conducted to face the acceptance and consensus in this area. The methodology of the review was based on: (I) search of PubMed and Scopus and (II) an eligibility assessment, using parameters with 5 levels of score. The results have: (a) highlighted and categorized the important challenges in place. (b) Illustrated the different types of studies conducted through original questionnaires. The study suggests for future research based on questionnaires a better calibration and inclusion of the challenges in place together with validation and administration paths at an international level.
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.