USING ARTIFICIAL INTELLIGENCE FOR BIOMARKER ANALYSIS IN CLINICAL DIAGNOSTICS DOI
П. В. Селиверстов, V. Kutsenko,

V. G. Gorelova

et al.

Molekulyarnaya Meditsina (Molecular medicine), Journal Year: 2024, Volume and Issue: unknown, P. 31 - 40

Published: Nov. 6, 2024

Introduction. Artificial intelligence (AI) technologies are becoming crucial in clinical diagnostics due to their ability process and interpret large volumes of data. The implementation AI for biomarker analysis opens new opportunities personalized medicine, offering more accurate individualized approaches disease diagnosis treatment. relevance this review stems from the need systematize recent advances application analysis, which is critical early prediction chronic non-communicable diseases (NCDs). Material methods. peer-reviewed scientific publications reports leading research centers over past five years was conducted. Studies on algorithms analyzing genomic, proteomic, metabolomic biomarkers were reviewed, including machine learning methods deep neural networks. Special attention paid integration multi-marker panels improving accuracy cardiovascular, digestive, respiratory, endocrine system diseases, as well oncological neurodegenerative pathologies. Results. has significantly increased sensitivity specificity diagnostics, especially complex cases requiring multiple parameters. effectiveness been demonstrated lung, breast, colorectal cancer, cardiovascular complications NCDs progression, diabetes mellitus Alzheimer’s disease. AI’s significant contribution discovery biomarkers, optimization treatment, improvement therapeutic strategies noted. Conclusion. use become a breakthrough medical particularly oncology, cardiology, diseases. technology allows data about various contributes creating models prediction. Further development associated with advancement overcoming ethical regulatory barriers, will expand capabilities practice.

Language: Английский

The Application of Artificial Intelligence in Lung Cancer Research DOI Creative Commons
Fang Lei

Cancer Control, Journal Year: 2024, Volume and Issue: 31

Published: Jan. 1, 2024

The advent of artificial intelligence in healthcare is transforming medical research and clinical practice, with significant advancements the areas oncology. This commentary explores pivotal role plays lung cancer research, offering insights into its current applications future potential.

Language: Английский

Citations

1
USING ARTIFICIAL INTELLIGENCE FOR BIOMARKER ANALYSIS IN CLINICAL DIAGNOSTICS DOI
П. В. Селиверстов, V. Kutsenko,

V. G. Gorelova

et al.

Molekulyarnaya Meditsina (Molecular medicine), Journal Year: 2024, Volume and Issue: unknown, P. 31 - 40

Published: Nov. 6, 2024

Introduction. Artificial intelligence (AI) technologies are becoming crucial in clinical diagnostics due to their ability process and interpret large volumes of data. The implementation AI for biomarker analysis opens new opportunities personalized medicine, offering more accurate individualized approaches disease diagnosis treatment. relevance this review stems from the need systematize recent advances application analysis, which is critical early prediction chronic non-communicable diseases (NCDs). Material methods. peer-reviewed scientific publications reports leading research centers over past five years was conducted. Studies on algorithms analyzing genomic, proteomic, metabolomic biomarkers were reviewed, including machine learning methods deep neural networks. Special attention paid integration multi-marker panels improving accuracy cardiovascular, digestive, respiratory, endocrine system diseases, as well oncological neurodegenerative pathologies. Results. has significantly increased sensitivity specificity diagnostics, especially complex cases requiring multiple parameters. effectiveness been demonstrated lung, breast, colorectal cancer, cardiovascular complications NCDs progression, diabetes mellitus Alzheimer’s disease. AI’s significant contribution discovery biomarkers, optimization treatment, improvement therapeutic strategies noted. Conclusion. use become a breakthrough medical particularly oncology, cardiology, diseases. technology allows data about various contributes creating models prediction. Further development associated with advancement overcoming ethical regulatory barriers, will expand capabilities practice.

Language: Английский

Citations

0