AIMC Topic: Artificial Intelligence

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Myths and facts about artificial intelligence: why machine- and deep-learning will not replace interventional radiologists.

Medical oncology (Northwood, London, England)
Artificial intelligence (AI) is revolutionizing healthcare and transforming the clinical practice of physicians across the world. Radiology has a strong affinity for machine learning and is at the forefront of the paradigm shift, as machines compete ...

PRIMAGE project: predictive in silico multiscale analytics to support childhood cancer personalised evaluation empowered by imaging biomarkers.

European radiology experimental
PRIMAGE is one of the largest and more ambitious research projects dealing with medical imaging, artificial intelligence and cancer treatment in children. It is a 4-year European Commission-financed project that has 16 European partners in the consor...

The International Conference on Intelligent Biology and Medicine 2019 (ICIBM 2019): computational methods and applications in medical genomics.

BMC medical genomics
In this editorial, we briefly summarized the International Conference on Intelligent Biology and Medicine 2019 (ICIBM 2019) that was held on June 9-11, 2019 at Columbus, Ohio, USA. We further introduced the 19 research articles included in this suppl...

An artificial intelligence-based EEG algorithm for detection of epileptiform EEG discharges: Validation against the diagnostic gold standard.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
OBJECTIVE: To validate an artificial intelligence-based computer algorithm for detection of epileptiform EEG discharges (EDs) and subsequent identification of patients with epilepsy.

Artificial intelligence to detect tympanic membrane perforations.

The Journal of laryngology and otology
OBJECTIVE: To explore the feasibility of constructing a proof-of-concept artificial intelligence algorithm to detect tympanic membrane perforations, for future application in under-resourced rural settings.

Artificial intelligence-based detection of pharyngeal cancer using convolutional neural networks.

Digestive endoscopy : official journal of the Japan Gastroenterological Endoscopy Society
OBJECTIVES: The prognosis for pharyngeal cancer is relatively poor. It is usually diagnosed in an advanced stage. Although the recent development of narrow-band imaging (NBI) and increased awareness among endoscopists have enabled detection of superf...

Artificial intelligence and machine learning in nephropathology.

Kidney international
Artificial intelligence (AI) for the purpose of this review is an umbrella term for technologies emulating a nephropathologist's ability to extract information on diagnosis, prognosis, and therapy responsiveness from native or transplant kidney biops...

An artificial intelligence algorithm that identifies middle turbinate pneumatisation (concha bullosa) on sinus computed tomography scans.

The Journal of laryngology and otology
OBJECTIVE: Convolutional neural networks are a subclass of deep learning or artificial intelligence that are predominantly used for image analysis and classification. This proof-of-concept study attempts to train a convolutional neural network algori...