Background: Artificial intelligence in medicine is a field that is rapidly evolving. Machine learning and deep learning are used to improve disease identification and diagnosis, personalize disease treatment, analyze medical images, evaluate clinical trials, and speed drug development.
Methods: First, relevant
[...] Read more.
Background: Artificial intelligence in medicine is a field that is rapidly evolving. Machine learning and deep learning are used to improve disease identification and diagnosis, personalize disease treatment, analyze medical images, evaluate clinical trials, and speed drug development.
Methods: First, relevant aspects of AI are revised in a comprehensive manner, including the classification of hematopoietic neoplasms, types of AI, applications in medicine and hematological neoplasia, generative pre-trained transformers (GPTs), and the architecture and interpretation of feedforward neural net-works (multilayer perceptron). Second, a series of 233 diffuse large B-cell lymphoma (DLBCL) patients treated with rituximab-CHOP from the Lymphoma/Leukemia Molecular Profiling Project (LLMPP) was analyzed.
Results: Using conventional statistics, the high expression of
MYC and
BCL2 was associated with poor survival, but high
BCL6 was associated with a favorable overall survival of the patients. Then, a neural network predicted
MYC,
BCL2, and
BCL6 with high accuracy using a pan-cancer panel of 758 genes of immuno-oncology and translational research that includes clinically relevant actionable genes and pathways. A comparable analysis was performed using gene set enrichment analysis (GSEA).
Conclusions: The mathematical way in which neural networks reach conclusions has been considered a black box, but a careful understanding and evaluation of the architectural design allows us to interpret the results logically. In diffuse large B-cell lymphoma, neural networks are a plausible data analysis approach.
Full article