ReviewNPJ digital medicine2023
Challenges of implementing computer-aided diagnostic models for neuroimages in a clinical setting.
Review in NPJ digital medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 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
26 citing papers in PubMed, 42 citations in OpenAlex.
- Artificial Intelligence and Digital Biomarkers for Early Detection and Monitoring of Neurological Disorders: A Narrative Review.Diagnostics (Basel, Switzerland) · 2026Review
- Artificial intelligence, equity, and pediatric neurodevelopmental disorders: A scoping review of clinical practice applications.Pediatric investigation · 2026Review
- A novel diagnosis method based on multimodal-aware bi-objective competitive mechanism for Alzheimer's disease.Scientific reports · 2026Article
- Unsupervised Anomaly Detection in Medical Imaging: A Survey of Methods, Challenges, and Future Directions.Bioengineering (Basel, Switzerland) · 2026Article
- Mild cognitive impairment cases affect the predictive power of Alzheimer's disease diagnostic models using routine clinical variables.npj aging · 2026Article
- Review
- Artificial intelligence-based biomarkers for the diagnosis and treatment of neurological conditions: a narrative review.Molecular brain · 2026Review
- Artificial intelligence in radiology: 173 commercially available products and their scientific evidence.European radiology · 2026Review
- Autism spectrum disorder detection using diffusion tensor imaging and machine learning.PLOS digital health · 2025Article
- Differential dementia detection from multimodal brain images in a real-world dataset.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- Predicting Immunotherapy Response in Unresectable Hepatocellular Carcinoma: A Comparative Study of Large Language Models and Human Experts.Journal of medical systems · 2025Article
- Developing multifactorial dementia prediction models using clinical variables from cohorts in the US and Australia.Translational psychiatry · 2025Article
- It is Not Time to Kick Out Radiologists.Asian bioethics review · 2025Article
- Towards collaborative data science in mental health research: The ECNP neuroimaging network accessible data repository.Neuroscience applied · 2025Article
- Faculty perspectives on artificial intelligence's adoption in the health sciences education: a multicentre survey.Frontiers in medicine · 2025Article
- Enhancing cancer risk awareness and screening management through artificial intelligence: a narrative review.Frontiers in oncology · 2025Review
- Precision neurodiversity: personalized brain network architecture as a window into cognitive variability.Frontiers in human neuroscience · 2025Review
- An artificial intelligence-derived metabolic network predicts psychosis in Alzheimer's disease.Brain communications · 2025Article
- Review
- Artificial General Intelligence for the Detection of Neurodegenerative Disorders.Sensors (Basel, Switzerland) · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 5 institutions in 4 countries.
Funding
Abstract
Advances in artificial intelligence have cultivated a strong interest in developing and validating the clinical utilities of computer-aided diagnostic models. Machine learning for diagnostic neuroimaging has often been applied to detect psychological and neurological disorders, typically on small-scale datasets or data collected in a research setting. With the collection and collation of an ever-growing number of public datasets that researchers can freely access, much work has been done in adapting machine learning models to classify these neuroimages by diseases such as Alzheimer's, ADHD, autism, bipolar disorder, and so on. These studies often come with the promise of being implemented clinically, but despite intense interest in this topic in the laboratory, limited progress has been made in clinical implementation. In this review, we analyze challenges specific to the clinical implementation of diagnostic AI models for neuroimaging data, looking at the differences between laboratory and clinical settings, the inherent limitations of diagnostic AI, and the different incentives and skill sets between research institutions, technology companies, and hospitals. These complexities need to be recognized in the translation of diagnostic AI for neuroimaging from the laboratory to the clinic.
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.