AIMC Topic: Artificial Intelligence

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Automated quantification of measurable residual disease in chronic lymphocytic leukemia using an artificial intelligence-assisted workflow.

Cytometry. Part B, Clinical cytometry
Detection of measurable residual disease (MRD) in chronic lymphocytic leukemia (CLL) is an important prognostic marker. The most common CLL MRD method in current use is multiparameter flow cytometry, but availability is limited by the need for expert...

Protein complexes in cells by AI-assisted structural proteomics.

Molecular systems biology
Accurately modeling the structures of proteins and their complexes using artificial intelligence is revolutionizing molecular biology. Experimental data enable a candidate-based approach to systematically model novel protein assemblies. Here, we use ...

Artificial Intelligence in Radiology: A Private Practice Perspective From a Large Health System in Latin America.

Seminars in roentgenology
In the field of radiology, the use of artificial intelligence (AI) is increasing. Even though healthcare facilities are interested in using this technology, having success with an AI project can be challenging. There is a myriad of AI solutions today...

Cognitive computing technological trends and future research directions in healthcare - A systematic literature review.

Artificial intelligence in medicine
BACKGROUND AND AIM: Cognitive Computing systems are the intelligent systems that thinks, understands and augments the capabilities of human brain by blending the technologies of Artificial Intelligence, Machine Learning and Natural Language Processin...

Strategy toward Kinase-Selective Drug Discovery.

Journal of chemical theory and computation
Kinase drug selectivity is the ground challenge in cancer research. Due to the structurally similar kinase drug pockets, off-target inhibitor toxicity has been a major cause for clinical trial failures. The pockets are similar but not identical. Here...

The devil is in the details: a small-lesion sensitive weakly supervised learning framework for prostate cancer detection and grading.

Virchows Archiv : an international journal of pathology
Prostate cancer (PCa) is a significant health concern in aging males, and the diagnosis depends primarily on histopathological assessments to determine tumor size and Gleason score. This process is highly time-consuming, subjective, and relies on the...

Determinants of implementing artificial intelligence-based clinical decision support tools in healthcare: a scoping review protocol.

BMJ open
INTRODUCTION: Artificial intelligence (AI), the simulation of human intelligence processes by machines, is being increasingly leveraged to facilitate clinical decision-making. AI-based clinical decision support (CDS) tools can improve the quality of ...

Deep learning for the diagnosis of mesial temporal lobe epilepsy.

PloS one
OBJECTIVE: This study aimed to enable the automatic detection of the hippocampus and diagnose mesial temporal lobe epilepsy (MTLE) with the hippocampus as the epileptogenic area using artificial intelligence (AI). We compared the diagnostic accuracie...

Technical Adequacy of Fully Automated Artificial Intelligence Body Composition Tools: Assessment in a Heterogeneous Sample of External CT Examinations.

AJR. American journal of roentgenology
Clinically usable artificial intelligence (AI) tools analyzing imaging studies should be robust to expected variations in study parameters. The purposes of this study were to assess the technical adequacy of a set of automated AI abdominal CT body ...