AIMC Topic: Diagnostic Imaging

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One Algorithm May Not Fit All: How Selection Bias Affects Machine Learning Performance.

Radiographics : a review publication of the Radiological Society of North America, Inc
Machine learning (ML) algorithms have demonstrated high diagnostic accuracy in identifying and categorizing disease on radiologic images. Despite the results of initial research studies that report ML algorithm diagnostic accuracy similar to or excee...

Artificial Intelligence and Stroke Imaging: A West Coast Perspective.

Neuroimaging clinics of North America
Artificial intelligence (AI) advancements have significant implications for medical imaging. Stroke is the leading cause of disability and the fifth leading cause of death in the United States. AI applications for stroke imaging are a topic of intens...

Artificial intelligence in paediatric radiology: Future opportunities.

The British journal of radiology
Artificial intelligence (AI) has received widespread and growing interest in healthcare, as a method to save time, cost and improve efficiencies. The high-performance statistics and diagnostic accuracies reported by using AI algorithms (with respect ...

Diverse Applications of Artificial Intelligence in Neuroradiology.

Neuroimaging clinics of North America
Recent advances in artificial intelligence (AI) and deep learning (DL) hold promise to augment neuroimaging diagnosis for patients with brain tumors and stroke. Here, the authors review the diverse landscape of emerging neuroimaging applications of A...

An East Coast Perspective on Artificial Intelligence and Machine Learning: Part 1: Hemorrhagic Stroke Imaging and Triage.

Neuroimaging clinics of North America
Hemorrhagic stroke is a medical emergency. Artificial intelligence techniques and algorithms may be used to automatically detect and quantitate intracranial hemorrhage in a semiautomated fashion. This article reviews the use of deep learning convolut...

[Artificial intelligence for eye care].

Nederlands tijdschrift voor geneeskunde
Technological developments in ophthalmic imaging and artificial intelligence (AI) create new possibilities for diagnostics in eye care. AI has already been applied in ophthalmic diabetes care. AI-systems currently detect diabetic retinopathy in gener...

Developing an AI project.

Journal of medical imaging and radiation sciences
Artificial intelligence applications can very powerful in areas of speech recognition, image processing and identification, medical diagnosis and clustering to name a few. There is a perception that developing your own artificial intelligence (AI) ap...

Thyroid Incidentalomas: Practice Considerations for Radiologists in the Age of Incidental Findings.

Radiologic clinics of North America
Radiologists very frequently encounter incidental findings related to the thyroid gland. Given increases in imaging use over the past several decades, thyroid incidentalomas are increasingly encountered in clinical practice, and it is important for r...

Artificial Intelligence and Its Effect on Dermatologists' Accuracy in Dermoscopic Melanoma Image Classification: Web-Based Survey Study.

Journal of medical Internet research
BACKGROUND: Early detection of melanoma can be lifesaving but this remains a challenge. Recent diagnostic studies have revealed the superiority of artificial intelligence (AI) in classifying dermoscopic images of melanoma and nevi, concluding that th...