Artificial Intelligence-Assisted Diagnostic Cytology and Genomic Testing for Hematologic Disorders.

Artificial intelligence (AI) is a rapidly evolving field of computer science that involves the development of computational programs that can mimic human intelligence. In particular, machine learning and deep learning models have enabled the identification and grouping of patterns within data, leading to the development of AI systems that have been applied in various areas of hematology, including digital pathology, alpha thalassemia patient screening, cytogenetics, immunophenotyping, and sequencing. These AI-assisted methods have shown promise in improving diagnostic accuracy and efficiency, identifying novel biomarkers, and predicting treatment outcomes. However, limitations such as limited databases, lack of validation and standardization, systematic errors, and bias prevent AI from completely replacing manual diagnosis in hematology. In addition, the processing of large amounts of patient data and personal information by AI poses potential data privacy issues, necessitating the development of regulations to evaluate AI systems and address ethical concerns in clinical AI systems. Nonetheless, with continued research and development, AI has the potential to revolutionize the field of hematology and improve patient outcomes. To fully realize this potential, however, the challenges facing AI in hematology must be addressed and overcome.

The applications of metabolomics in the molecular diagnostics of cancer.

Introduction: Metabolomics, the study of metabolites, is a promising research field for cancers. The metabolic pathway in a tumor cell is different from a normal tissue cell. There are two approaches to study the metabolism, targeted and untargeted. The general approach is that metabolomic data are interpreted by bioinformatics tools correlating with metabolomic databases to obtain significant findings. With the use of specific analysis tools, such as nuclear magnetic resonance (NMR) and mass spectrometer (MS) combined with chromatography, metabolic profile or metabolic fingerprint of various biological specimens could be obtained. The applications of metabolomics are used to discover potential cancer biomarkers and monitor the metastatic state, therapeutic and drug response for better patient management. Areas covered: In this review, the author introduce metabolomics and discuss the use of metabolomics approaches in different cancers, including the study of colorectal cancer, prostate cancer, liver cancer, pancreatic cancer and breast cancer using NMR and MS. Expert opinion: Knowledge on the molecular basis of cancer metabolism and its potential clinical applications has been improving recently. However, there are still many challenges for the technological development and integration of metabolomics with other omics spaces such as genomics. In the near future, it is expected that metabolomics will play an important role in cancer molecular diagnostics.