ArticleRadiology. Artificial intelligence2021
Integrating Al Algorithms into the Clinical Workflow.
Open access · greenAbstract read
In one paragraph
Article in Radiology. Artificial intelligence, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.
0numbers the graph read from it
0cells of the map it votes in
40citing papers in PubMed
2.6field-weighted citation impact, top 10% of its field
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
40 citing papers in PubMed, 67 citations in OpenAlex.
- Artificial intelligence in hepatocellular carcinoma diagnosis and AI-assisted treatment support: Current landscape and future directions.Liver research (Beijing, China) · 2026Review
- Integrating artificial intelligence into paediatric interventional radiology: a review of emerging applications and future directions.Pediatric radiology · 2026Review
- From Pixels to Prediction: Reviewing the Role of Artificial Intelligence in Body Composition Analysis.Journal of cachexia, sarcopenia and muscle · 2026Review
- Perceptions of Health Care Professionals on the Integration and Use of AI in Clinical Cancer Care: Interview Study.JMIR human factors · 2026Article
- Who's afraid of synthetic data? Hybrid approaches to deliver medical digital twins.Informatics in medicine unlocked · 2026Article
- Artificial intelligence drives the identification and screening of novel antibiotics and antimicrobial peptides.Briefings in bioinformatics · 2026Review
- AI and Big Data in Oncology: A Physician-Centered Perspective on Emerging Clinical and Research Applications.Cancer innovation · 2026Review
- Evolution of CT perfusion software in stroke imaging: from deconvolution to artificial intelligence.European radiology · 2026Review
- Multimodal Large Language Models in Medical Imaging: Current State and Future Directions.Korean journal of radiology · 2025Review
- Current Perspectives on the Artificial Intelligence in Critical Care Medicine.Anesthesiology clinics · 2025Review
- AI in Dentistry: Innovations, Ethical Considerations, and Integration Barriers.Bioengineering (Basel, Switzerland) · 2025Review
- Artificial intelligence for early detection of diabetes mellitus complications via retinal imaging.Journal of diabetes and metabolic disorders · 2025Review
- AI-Powered Object Detection in Radiology: Current Models, Challenges, and Future Direction.Journal of imaging · 2025Review
- A Cloud-Based System for Automated AI Image Analysis and Reporting.Journal of imaging informatics in medicine · 2025Article
- Transforming Echocardiography: The Role of Artificial Intelligence in Enhancing Diagnostic Accuracy and Accessibility.Internal medicine (Tokyo, Japan) · 2025Review
- Upskilling or deskilling? Measurable role of an AI-supported training for radiology residents: a lesson from the pandemic.Insights into imaging · 2025Article
- Transforming Medical Imaging: The Role of Artificial Intelligence Integration in PACS for Enhanced Diagnostic Accuracy and Workflow EfficiencyCurrent medical imaging · 2025Review
- Digital Information Ecosystems in Modern Care Coordination and Patient Care Pathways and the Challenges and Opportunities for AI Solutions.Journal of medical Internet research · 2024Article
- Comparing the Diagnostic Performance of GPT-4-based ChatGPT, GPT-4V-based ChatGPT, and Radiologists in Challenging Neuroradiology Cases.Clinical neuroradiology · 2024Article
- Comparative Accuracy of ChatGPT 4.0 and Google Gemini in Answering Pediatric Radiology Text-Based Questions.Cureus · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 1 institution in 1 country.
Krishna JuluruDepartment of Radiology, Memorial Sloan-Kettering Cancer Center, 1275 York Ave, Box 29, New York, NY 10065 (K.J., H.H.S., K.N.K.M., P.E., A.E.R., J.F.); Department of Radiology, Duke University Medical Center, Durham, NC (C.R.); NVIDIA, Santa Clara, Calif (B.G.); Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, Md (E.S.); and Department of Radiology, Stanford University, Stanford, Calif (D.L.R.).ORCID 0000-0001-8203-8894
Hao-Hsin ShihDepartment of Radiology, Memorial Sloan-Kettering Cancer Center, 1275 York Ave, Box 29, New York, NY 10065 (K.J., H.H.S., K.N.K.M., P.E., A.E.R., J.F.); Department of Radiology, Duke University Medical Center, Durham, NC (C.R.); NVIDIA, Santa Clara, Calif (B.G.); Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, Md (E.S.); and Department of Radiology, Stanford University, Stanford, Calif (D.L.R.).ORCID 0000-0003-2127-8645
Krishna Nand Keshava MurthyDepartment of Radiology, Memorial Sloan-Kettering Cancer Center, 1275 York Ave, Box 29, New York, NY 10065 (K.J., H.H.S., K.N.K.M., P.E., A.E.R., J.F.); Department of Radiology, Duke University Medical Center, Durham, NC (C.R.); NVIDIA, Santa Clara, Calif (B.G.); Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, Md (E.S.); and Department of Radiology, Stanford University, Stanford, Calif (D.L.R.).ORCID 0000-0003-2408-7414
Pierre ElnajjarDepartment of Radiology, Memorial Sloan-Kettering Cancer Center, 1275 York Ave, Box 29, New York, NY 10065 (K.J., H.H.S., K.N.K.M., P.E., A.E.R., J.F.); Department of Radiology, Duke University Medical Center, Durham, NC (C.R.); NVIDIA, Santa Clara, Calif (B.G.); Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, Md (E.S.); and Department of Radiology, Stanford University, Stanford, Calif (D.L.R.).
Amin El-RowmeimDepartment of Radiology, Memorial Sloan-Kettering Cancer Center, 1275 York Ave, Box 29, New York, NY 10065 (K.J., H.H.S., K.N.K.M., P.E., A.E.R., J.F.); Department of Radiology, Duke University Medical Center, Durham, NC (C.R.); NVIDIA, Santa Clara, Calif (B.G.); Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, Md (E.S.); and Department of Radiology, Stanford University, Stanford, Calif (D.L.R.).ORCID https://orcid.org/0000-0003-2966-2959
Christopher RothDepartment of Radiology, Memorial Sloan-Kettering Cancer Center, 1275 York Ave, Box 29, New York, NY 10065 (K.J., H.H.S., K.N.K.M., P.E., A.E.R., J.F.); Department of Radiology, Duke University Medical Center, Durham, NC (C.R.); NVIDIA, Santa Clara, Calif (B.G.); Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, Md (E.S.); and Department of Radiology, Stanford University, Stanford, Calif (D.L.R.).ORCID 0000-0002-9634-7126
Brad GenereauxDepartment of Radiology, Memorial Sloan-Kettering Cancer Center, 1275 York Ave, Box 29, New York, NY 10065 (K.J., H.H.S., K.N.K.M., P.E., A.E.R., J.F.); Department of Radiology, Duke University Medical Center, Durham, NC (C.R.); NVIDIA, Santa Clara, Calif (B.G.); Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, Md (E.S.); and Department of Radiology, Stanford University, Stanford, Calif (D.L.R.).ORCID https://orcid.org/0000-0003-2923-2887
Josef FoxDepartment of Radiology, Memorial Sloan-Kettering Cancer Center, 1275 York Ave, Box 29, New York, NY 10065 (K.J., H.H.S., K.N.K.M., P.E., A.E.R., J.F.); Department of Radiology, Duke University Medical Center, Durham, NC (C.R.); NVIDIA, Santa Clara, Calif (B.G.); Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, Md (E.S.); and Department of Radiology, Stanford University, Stanford, Calif (D.L.R.).
Eliot SiegelDepartment of Radiology, Memorial Sloan-Kettering Cancer Center, 1275 York Ave, Box 29, New York, NY 10065 (K.J., H.H.S., K.N.K.M., P.E., A.E.R., J.F.); Department of Radiology, Duke University Medical Center, Durham, NC (C.R.); NVIDIA, Santa Clara, Calif (B.G.); Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, Md (E.S.); and Department of Radiology, Stanford University, Stanford, Calif (D.L.R.).ORCID https://orcid.org/0000-0002-7458-6281
Daniel L RubinDepartment of Radiology, Memorial Sloan-Kettering Cancer Center, 1275 York Ave, Box 29, New York, NY 10065 (K.J., H.H.S., K.N.K.M., P.E., A.E.R., J.F.); Department of Radiology, Duke University Medical Center, Durham, NC (C.R.); NVIDIA, Santa Clara, Calif (B.G.); Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, Md (E.S.); and Department of Radiology, Stanford University, Stanford, Calif (D.L.R.).ORCID https://orcid.org/0000-0001-5057-4369
University of Maryland, Baltimore · US
Funding
X-RAY CRYSTALLOGRAPHYP30CA008748 · NCI · SLOAN-KETTERING INSTITUTE FOR CANCER RES · PI Hongbiao Carl Lekaye · 1985 to 2026
$347.4MNCI NIH HHS P30 CA008748
Abstract
Integration of artificial intelligence (AI) applications within clinical workflows is an important step for leveraging developed AI algorithms. In this report, generalizable components for deploying AI systems into clinical practice are described that were implemented in a clinical pilot study using lymphoscintigraphy examinations as a prospective use case (July 1, 2019-October 31, 2020). Deployment of the AI algorithm consisted of seven software components, as follows:
Indexed as
Computer Applications-General (Informatics)DiagnosisPACS
Identifiers
What OpenQuestion holds
Textmetadata
Read underepoch 390
Registered trials
None linked
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.