ArticleJournal of nuclear medicine : official publication, Society of Nuclear Medicine2022
Nuclear Medicine and Artificial Intelligence: Best Practices for Evaluation (the RELAINCE Guidelines).
Article in Journal of nuclear medicine : official publication, Society of Nuclear Medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 63 papers, 3 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
63 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- AI for IMPACTS Framework for Evaluating the Long-Term Real-World Impacts of AI-Powered Clinician Tools: Systematic Review and Narrative Synthesis.Journal of medical Internet research · 2025Pooled it
- Artificial intelligence in the risk prediction models of cardiovascular disease and development of an independent validation screening tool: a systematic review.BMC medicine · 2024Pooled it
- Joint EANM/SNMMI guideline on radiomics in nuclear medicine : Jointly supported by the EANM Physics Committee and the SNMMI Physics, Instrumentation and Data Sciences Council.European journal of nuclear medicine and molecular imaging · 2023Guideline
- Evaluation of Baseline and Interim-Therapy PET Features for Prognostication in High-Risk Pediatric Hodgkin Lymphoma: A Retrospective Analysis of the AHOD1331 Trial.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2026Article
- Revisiting Perfusion in the Forgotten Right Ventricle: Artificial Intelligence-Enhanced Quantification on PET/CT.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2026Article
- Applications of artificial intelligence in nuclear medicine.Zeitschrift fur medizinische Physik · 2026Review
- Review
- Clinical application of AI for lung lobe segmentation and functional quantification with SPECT/CT: open-source versus custom models.Scientific reports · 2026Article
- Validation of a Deep-Learning Coregistration Framework for Long-Axial-Field-of-View PET/CT Using Low-Radiation-Exposure Protocols Across Various Tracers.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2026Article
- Artificial intelligence for TNM staging in NSCLC: a critical appraisal of segmentation utility in [European journal of nuclear medicine and molecular imaging · 2026Article
- Comparison of multi-organ CT image segmentation tools for whole-body [European journal of nuclear medicine and molecular imaging · 2026Article
- AutoPET Challenge on Fully Automated Lesion Segmentation in Oncologic PET/CT Imaging, Part 2: Domain Generalization.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2026Article
- Non-inferiority of automated deep learning-based [EJNMMI research · 2026Article
- Context-specific image quality assessment for virtual histologic staining: checklist and guideline.Journal of medical imaging (Bellingham, Wash.) · 2026Article
- Artificial intelligence across oncologic theranostics: evidence for patient stratification, dosimetry, and adaptive radiopharmaceutical therapy.Frontiers in nuclear medicine · 2026Review
- Critical Appraisal Tools for Evaluating Artificial Intelligence in Clinical Studies: Scoping Review.Journal of medical Internet research · 2025Article
- Artificial Intelligence-Powered Quantification of Flortaucipir PET for Detecting Tau Pathology.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2025Article
- Development and in silico imaging trial evaluation of a deep-learning-based transmission-less attenuation compensation method for DaT SPECT.Medical physics · 2025Article
- Summary Report of the SNMMI AI Task Force Radiomics Challenge 2024.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2025Article
- Emerging Diagnostic Approaches for Musculoskeletal Disorders: Advances in Imaging, Biomarkers, and Clinical Assessment.Diagnostics (Basel, Switzerland) · 2025Review
3 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors.
Funding
Abstract
An important need exists for strategies to perform rigorous objective clinical-task-based evaluation of artificial intelligence (AI) algorithms for nuclear medicine. To address this need, we propose a 4-class framework to evaluate AI algorithms for promise, technical task-specific efficacy, clinical decision making, and postdeployment efficacy. We provide best practices to evaluate AI algorithms for each of these classes. Each class of evaluation yields a claim that provides a descriptive performance of the AI algorithm. Key best practices are tabulated as the RELAINCE (Recommendations for EvaLuation of AI for NuClear medicinE) guidelines. The report was prepared by the Society of Nuclear Medicine and Molecular Imaging AI Task Force Evaluation team, which consisted of nuclear-medicine physicians, physicists, computational imaging scientists, and representatives from industry and regulatory agencies.
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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.