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Artificial Intelligence in Retrosynthesis Prediction and its Applications in Medicinal Chemistry.
J Med Chem
January 2025
Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.
Retrosynthesis is a strategy to analyze the synthetic routes for target molecules in medicinal chemistry. However, traditional retrosynthesis predictions performed by chemists and rule-based expert systems struggle to adapt to the vast chemical space of real-world scenarios. Artificial intelligence (AI) has revolutionized retrosynthesis prediction in recent decades, significantly increasing the accuracy and diversity of predictions for target compounds.
View Article and Find Full Text PDFArtificial intelligence for segmentation and classification in lumbar spinal stenosis: an overview of current methods.
Eur Spine J
January 2025
Department of Neurosurgery, Leiden University Medical Center, Albinusdreef 2, 2333ZA, Leiden, The Netherlands.
Purpose: Lumbar spinal stenosis (LSS) is a frequently occurring condition defined by narrowing of the spinal or nerve root canal due to degenerative changes. Physicians use MRI scans to determine the severity of stenosis, occasionally complementing it with X-ray or CT scans during the diagnostic work-up. However, manual grading of stenosis is time-consuming and induces inter-reader variability as a standardized grading system is lacking.
View Article and Find Full Text PDFThe impact of updated imaging software on the performance of machine learning models for breast cancer diagnosis: a multi-center, retrospective study.
Arch Gynecol Obstet
January 2025
Department of Obstetrics and Gynecology, Breast Cancer Center, Heidelberg University Hospital, Im Neuenheimer Feld 440, 69120, Heidelberg, Germany.
Purpose: Artificial Intelligence models based on medical (imaging) data are increasingly developed. However, the imaging software on which the original data is generated is frequently updated. The impact of updated imaging software on the performance of AI models is unclear.
View Article and Find Full Text PDFValidation of a Pretransplant Risk Prediction Model for Early Allograft Dysfunction After Living-donor Liver Transplantation.
Transplantation
January 2025
University of Zurich, Wyss Translational Center, Zurich, Switzerland.
Background: Early allograft dysfunction (EAD) affects outcomes in liver transplantation (LT). Existing risk models developed for deceased-donor LT depend on posttransplant factors and fall short in living-donor LT (LDLT), where pretransplant evaluations are crucial for preventing EAD and justifying the donor's risks.
Methods: This retrospective study analyzed data from 2944 adult patients who underwent LDLT at 17 centers between 2016 and 2020.
Ejtm3 experiences after ChatGPT and other AI approaches: values, risks, countermeasures.
Eur J Transl Myol
January 2025
A&C M-C Foundation for Translational Myology, Padua, Italy; Institute of Biomedical and Neural Engineering, Reykjavik University, Reykjavik, Iceland; Department of Digital Transformation, Landspitali University Hospital, Reykjavík.
We invariably hear that Artificial Intelligence (AI), a rapidly evolving technology, does not just creatively assemble known knowledge. We are told that AI learns, processes and creates, starting from fixed points to arrive at innovative solutions. In the case of scientific work, AI can generate data without ever having entered a laboratory, (i.
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