Artificial intelligence applied to clinical trials: opportunities and challenges

S Askin, D Burkhalter, G Calado, S El Dakrouni - Health and technology, 2023 - Springer
Abstract Background Clinical Trials (CTs) remain the foundation of safe and effective drug
development. Given the evolving data-driven and personalized medicine approach in …

[HTML][HTML] Artificial intelligence in pancreatic cancer

B Huang, H Huang, S Zhang, D Zhang, Q Shi, J Liu… - Theranostics, 2022 - ncbi.nlm.nih.gov
Pancreatic cancer is the deadliest disease, with a five-year overall survival rate of just 11%.
The pancreatic cancer patients diagnosed with early screening have a median overall …

Are deep learning structural models sufficiently accurate for virtual screening? application of docking algorithms to AlphaFold2 predicted structures

AM Díaz-Rovira, H Martín, T Beuming… - Journal of Chemical …, 2023 - ACS Publications
Machine learning-based protein structure prediction algorithms, such as RosettaFold and
AlphaFold2, have greatly impacted the structural biology field, arousing a fair amount of …

[HTML][HTML] The Role of Artificial Intelligence on Tumor Boards: Perspectives from Surgeons, Medical Oncologists and Radiation Oncologists

V Nardone, F Marmorino, MM Germani… - Current …, 2024 - mdpi.com
The integration of multidisciplinary tumor boards (MTBs) is fundamental in delivering state-of-
the-art cancer treatment, facilitating collaborative diagnosis and management by a diverse …

[HTML][HTML] Modeling the efficacy of different anti-angiogenic drugs on treatment of solid tumors using 3D computational modeling and machine learning

M Mousavi, MD Manshadi, M Soltani… - Computers in biology …, 2022 - Elsevier
Accurate simulation of tumor growth during chemotherapy has significant potential to
alleviate the risk of unknown side effects and optimize clinical trials. In this study, a 3D …

A systematic literature review for the prediction of anticancer drug response using various machine‐learning and deep‐learning techniques

DP Singh, B Kaushik - Chemical Biology & Drug Design, 2023 - Wiley Online Library
Computational methods have gained prominence in healthcare research. The accessibility
of healthcare data has greatly incited academicians and researchers to develop executions …

TDC-2: Multimodal Foundation for Therapeutic Science

A Velez-Arce, K Huang, MM Li, X Lin, W Gao, T Fu… - bioRxiv, 2024 - biorxiv.org
Abstract Therapeutics Data Commons (tdcommons. ai) is an open science initiative with
unified datasets, AI models, and benchmarks to support research across therapeutic …

Machine learning algorithms and biomarkers identification for pancreatic cancer diagnosis using multi-omics data integration

AK Rouzbahani, G Khalili-Tanha, Y Rajabloo… - … -Research and Practice, 2024 - Elsevier
Purpose Pancreatic cancer is a lethal type of cancer with most of the cases being diagnosed
in an advanced stage and poor prognosis. Developing new diagnostic and prognostic …

Predicting Phase 1 Lymphoma Clinical Trial Durations Using Machine Learning: An In-Depth Analysis and Broad Application Insights

B Long, SW Lai, J Wu, S Bellur - Clinics and Practice, 2023 - mdpi.com
Lymphoma diagnoses in the US are substantial, with an estimated 89,380 new cases in
2023, necessitating innovative treatment approaches. Phase 1 clinical trials play a pivotal …

Correlation Between Early Trends of a Prognostic Biomarker and Overall Survival in Non–Small-Cell Lung Cancer Clinical Trials

H Loureiro, TM Kolben, A Kiermaier… - JCO Clinical Cancer …, 2023 - ascopubs.org
PURPOSE Overall survival (OS) is the primary end point in phase III oncology trials. Given
low success rates, surrogate end points, such as progression-free survival or objective …