ReviewSeminars in ultrasound, CT, and MR2023
Artificial Intelligence in Breast X-Ray Imaging.
Review in Seminars in ultrasound, CT, and MR, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Ethical implications of the use of AI-based technologies for medical image classification systems in screening: a qualitative systematic review.Health technology assessment (Winchester, England) · 2026Pooled it
- Systematic Review and Meta-Analysis of AI-Assisted Mammography and the Systemic Immune-Inflammation Index in Breast Cancer: Diagnostic and Prognostic Perspectives.Medicina (Kaunas, Lithuania) · 2025Pooled it
- Enhancing breast cancer detection with AI for early diagnosis and recurrence prediction.Oncoscience · 2026Review
- Tidal Volume Monitoring via Surface Motions of the Upper Body-A Pilot Study of an Artificial Intelligence Approach.Sensors (Basel, Switzerland) · 2025Article
- Artificial Intelligence in Medical Metaverse: Applications, Challenges, and Future Prospects.Current medical science · 2024Review
- Comparative Analysis of M4CXR, an LLM-Based Chest X-Ray Report Generation Model, and ChatGPT in Radiological Interpretation.Journal of clinical medicine · 2024Article
- Rapid genomic sequencing for genetic disease diagnosis and therapy in intensive care units: a review.NPJ genomic medicine · 2024Review
- Validation of a Deep Learning Chest X-ray Interpretation Model: Integrating Large-Scale AI and Large Language Models for Comparative Analysis with ChatGPT.Diagnostics (Basel, Switzerland) · 2023Article
- Daring discourse: artificial intelligence in pain medicine, opportunities and challenges.Regional anesthesia and pain medicine · 2023Article
- Defining medical liability when artificial intelligence is applied on diagnostic algorithms: a systematic review.Frontiers in 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
4 authors.
Funding
Abstract
This topical review is focused on the clinical breast x-ray imaging applications of the rapidly evolving field of artificial intelligence (AI). The range of AI applications is broad. AI can be used for breast cancer risk estimation that could allow for tailoring the screening interval and the protocol that are woman-specific and for triaging the screening exams. It also can serve as a tool to aid in the detection and diagnosis for improved sensitivity and specificity and as a tool to reduce radiologists' reading time. AI can also serve as a potential second 'reader' during screening interpretation. During the last decade, numerous studies have shown the potential of AI-assisted interpretation of mammography and to a lesser extent digital breast tomosynthesis; however, most of these studies are retrospective in nature. There is a need for prospective clinical studies to evaluate these technologies to better understand their real-world efficacy. Further, there are ethical, medicolegal, and liability concerns that need to be considered prior to the routine use of AI in the breast imaging clinic.
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.