ReviewJournal of endocrinological investigation2024
Artificial intelligence in endocrinology: a comprehensive review.
Review in Journal of endocrinological investigation, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis 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
18 citing papers in PubMed, 1 synthesis or guideline pooled it, 42 citations in OpenAlex.
- Pooled it
- Building the adult growth hormone deficiency data mart: a Real-World model of AI-driven clinical data extraction in a single Italian center.Journal of endocrinological investigation · 2026Article
- Slice Level Classification of Parathyroid Adenoma Using Arterial Phase CT Images with Hybrid Light Attention Mechanism Based Residual Framework.Bioengineering (Basel, Switzerland) · 2026Article
- Integrating Robotic Bilateral Axillo-Breast Approach Thyroidectomy with Molecular Diagnostics and Artificial Intelligence in Thyroid Cancer Care.Biomolecules & therapeutics · 2026Review
- Development of a prediction model by combining tumor diameter and clinical parameters of adrenal incidentaloma.Endocrine journal · 2025Article
- A novel approach for classifying patients with adrenal tumors based on decision support systems and artificial intelligence.Hormones (Athens, Greece) · 2025Article
- Assessment of the Ability of the ChatGPT-5 Model to Pass the Endocrinology Specialization Exam.Cureus · 2025Article
- Artificial intelligence in fracture detection on radiographs: a literature review.Japanese journal of radiology · 2025Review
- Advancing clinical biochemistry: addressing gaps and driving future innovations.Frontiers in medicine · 2025Review
- The association between computed tomography-based osteosarcopenia and osteoporotic vertebral fractures: a longitudinal study.Journal of endocrinological investigation · 2025Article
- Integrating deep learning and clinical characteristics for early prediction of endometrial cancer using multimodal ultrasound imaging: a multicenter study.Frontiers in oncology · 2025Article
- Artificial intelligence is going to transform the field of endocrinology: an overview.Frontiers in endocrinology · 2025Article
- Differential gene expression profiling and machine learning-based discovery of key genetic markers in VTE and CKD.Frontiers in immunology · 2025Article
- EndoCompass Project: Artificial Intelligence in Endocrinology.Hormone research in paediatrics · 2025Review
- Machine learning-based reproducible prediction of type 2 diabetes subtypes.Diabetologia · 2024Article
- Cardiovascular Disease Risk Stratification Using Hybrid Deep Learning Paradigm: First of Its Kind on Canadian Trial Data.Diagnostics (Basel, Switzerland) · 2024Article
- Artificial intelligence in assisting pathogenic microorganism diagnosis and treatment: a review of infectious skin diseases.Frontiers in microbiology · 2024Review
- A Glimpse into the Realm that Combines Artificial Intelligence and Endocrinology.Indian journal of endocrinology and metabolismReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
BACKGROUND AND
aimArtificial intelligence (AI) has emerged as a promising technology in the field of endocrinology, offering significant potential to revolutionize the diagnosis, treatment, and management of endocrine disorders. This comprehensive review aims to provide a concise overview of the current landscape of AI applications in endocrinology and metabolism, focusing on the fundamental concepts of AI, including machine learning algorithms and deep learning models.
methodsThe review explores various areas of endocrinology where AI has demonstrated its value, encompassing screening and diagnosis, risk prediction, translational research, and "pre-emptive medicine". Within each domain, relevant studies are discussed, offering insights into the methodology and main findings of AI in the treatment of different pathologies, such as diabetes mellitus and related disorders, thyroid disorders, adrenal tumors, and bone and mineral disorders.
resultsCollectively, these studies show the valuable contributions of AI in optimizing healthcare outcomes and unveiling new understandings of the intricate mechanisms underlying endocrine disorders. Furthermore, AI-driven approaches facilitate the development of precision medicine strategies, enabling tailored interventions for patients based on their individual characteristics and needs.
conclusionsBy embracing AI in endocrinology, a future can be envisioned where medical professionals and AI systems synergistically collaborate, ultimately enhancing the lives of individuals affected by endocrine disorders.
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.