AIMC Topic: Artificial Intelligence

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Study on detection rate of polyps and adenomas in artificial-intelligence-aided colonoscopy.

Saudi journal of gastroenterology : official journal of the Saudi Gastroenterology Association
BACKGROUND/AIM: To study the impact of computer-aided detection (CADe) system on the detection rate of polyps and adenomas in colonoscopy.

[Use of artificial intelligence for image reconstruction].

Der Radiologe
CLINICAL/METHODOLOGICAL PROBLEM: In the reconstruction of three-dimensional image data, artifacts that interfere with the appraisal often occur as a result of trying to minimize the dose or due to missing data. Used iterative reconstruction methods a...

The Ethics of Medical AI and the Physician-Patient Relationship.

Cambridge quarterly of healthcare ethics : CQ : the international journal of healthcare ethics committees
This article considers recent ethical topics relating to medical AI. After a general discussion of recent medical AI innovations, and a more analytic look at related ethical issues such as data privacy, physician dependency on poorly understood AI he...

The coming 15 years in gynaecological pathology: digitisation, artificial intelligence, and new technologies.

Histopathology
Surgical pathology forms the cornerstone of modern oncological medicine, owing to the wealth of clinically relevant information that can be obtained from tissue morphology. Although several ancillary testing modalities have been added to surgical pat...

[Artificial Intelligence in radiology : What can be expected in the next few years?].

Der Radiologe
CLINICAL/METHODOLOGICAL ISSUE: Artificial intelligence (AI) is being increasingly used in the field of radiology. The aim of this review is to illustrate the developments expected in the next 5 to 10 years as well as possible advantages and risks.

Augmented Radiologist Workflow Improves Report Value and Saves Time: A Potential Model for Implementation of Artificial Intelligence.

Academic radiology
RATIONALE AND OBJECTIVES: Our primary aim was to improve radiology reports by increasing concordance of target lesion measurements with oncology records using radiology preprocessors (RP). Faster notification of incidental actionable findings to refe...