# Artificial Intelligence in Medicine

**Type:** Journals  
**Canonical URL:** https://scholariq.org/journals/artificial-intelligence-in-medicine/

## Facts

| Field | Value |
| --- | --- |
| APC (USD) | 3,230 |
| Citations | 122,057 |
| h-index | 148 |
| Homepage | https://www.sciencedirect.com/journal/artificial-intelligence-in-medicine |
| Open Access | false |
| ISSN-L | 0933-3657 |
| ISSNs | 0933-3657,1873-2860 |
| OpenAlex ID | https://openalex.org/S42468263 |
| Publisher | Elsevier BV |
| Works | 3,576 |

## Journal papers

Showing 12 of 14.

- [The coming of age of artificial intelligence in medicine](https://scholariq.org/papers/the-coming-of-age-of-artificial-intelligence-in-medicine/)
- [Machine learning in oral squamous cell carcinoma: Current status, clinical concerns and prospects for future—A systematic review](https://scholariq.org/papers/machine-learning-in-oral-squamous-cell-carcinoma-current-status-clinical/)
- [Deep learning to find colorectal polyps in colonoscopy: A systematic literature review](https://scholariq.org/papers/deep-learning-to-find-colorectal-polyps-in-colonoscopy-a-systematic-literature/)
- [A fuzzy clustering based segmentation system as support to diagnosis in medical imaging](https://scholariq.org/papers/a-fuzzy-clustering-based-segmentation-system-as-support-to-diagnosis-in-medical/)
- [An intelligent learning approach for improving ECG signal classification and arrhythmia analysis](https://scholariq.org/papers/an-intelligent-learning-approach-for-improving-ecg-signal-classification-and/)
- [Uncertainty-guided mutual consistency learning for semi-supervised medical image segmentation](https://scholariq.org/papers/uncertainty-guided-mutual-consistency-learning-for-semi-supervised-medical-image/)
- [Intelligent dental training simulator with objective skill assessment and feedback](https://scholariq.org/papers/intelligent-dental-training-simulator-with-objective-skill-assessment-and/)
- [A multicenter random forest model for effective prognosis prediction in collaborative clinical research network](https://scholariq.org/papers/a-multicenter-random-forest-model-for-effective-prognosis-prediction-in/)
- [A Bayesian approach to generating tutorial hints in a collaborative medical problem-based learning system](https://scholariq.org/papers/a-bayesian-approach-to-generating-tutorial-hints-in-a-collaborative-medical/)
- [Surgical motion analysis using discriminative interpretable patterns](https://scholariq.org/papers/surgical-motion-analysis-using-discriminative-interpretable-patterns/)
- [How knowledge drives understanding—matching medical ontologies with the needs of medical language processing](https://scholariq.org/papers/how-knowledge-drives-understanding-matching-medical-ontologies-with-the-needs-of/)
- [An integrated scheme for feature selection and parameter setting in the support vector machine modeling and its application to the prediction of pharmacokinetic properties of drugs](https://scholariq.org/papers/an-integrated-scheme-for-feature-selection-and-parameter-setting-in-the-support/)

## Journal top topics

Showing 8 of 25.

- [Machine Learning in Healthcare](https://scholariq.org/topics/machine-learning-in-healthcare/)
- [Biomedical Text Mining and Ontologies](https://scholariq.org/topics/biomedical-text-mining-and-ontologies/)
- [AI in cancer detection](https://scholariq.org/topics/ai-in-cancer-detection/)
- [Diverse Scientific and Economic Studies](https://scholariq.org/topics/diverse-scientific-and-economic-studies/)
- [Human auditory perception and evaluation](https://scholariq.org/topics/human-auditory-perception-and-evaluation/)
- [Artificial Intelligence in Healthcare and Education](https://scholariq.org/topics/artificial-intelligence-in-healthcare-and-education/)
- [Educational Robotics and Engineering](https://scholariq.org/topics/educational-robotics-and-engineering/)
- [Artificial Intelligence in Healthcare](https://scholariq.org/topics/artificial-intelligence-in-healthcare/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.