Challenges and applications of artificial intelligence in infectious diseases and antimicrobial resistance
Abstract Artificial intelligence (AI) has transformed infectious disease control, enhancing rapid diagnosis and antibiotic discovery. While conventional tests delay diagnosis, AI-driven methods like machine learning and deep learning assist in pathogen detection, resistance prediction, and drug disc...
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Format: | Article |
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Nature Portfolio
2025-01-01
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Series: | npj Antimicrobials and Resistance |
Online Access: | https://doi.org/10.1038/s44259-024-00068-x |
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author | Angela Cesaro Samuel C. Hoffman Payel Das Cesar de la Fuente-Nunez |
author_facet | Angela Cesaro Samuel C. Hoffman Payel Das Cesar de la Fuente-Nunez |
author_sort | Angela Cesaro |
collection | DOAJ |
description | Abstract Artificial intelligence (AI) has transformed infectious disease control, enhancing rapid diagnosis and antibiotic discovery. While conventional tests delay diagnosis, AI-driven methods like machine learning and deep learning assist in pathogen detection, resistance prediction, and drug discovery. These tools improve antibiotic stewardship and identify effective compounds such as antimicrobial peptides and small molecules. This review explores AI applications in diagnostics, therapy, and drug discovery, emphasizing both strengths and areas needing improvement. |
format | Article |
id | doaj-art-f9888435895e4822aaf8d337d1df6f7d |
institution | Kabale University |
issn | 2731-8745 |
language | English |
publishDate | 2025-01-01 |
publisher | Nature Portfolio |
record_format | Article |
series | npj Antimicrobials and Resistance |
spelling | doaj-art-f9888435895e4822aaf8d337d1df6f7d2025-01-12T12:41:24ZengNature Portfolionpj Antimicrobials and Resistance2731-87452025-01-013111010.1038/s44259-024-00068-xChallenges and applications of artificial intelligence in infectious diseases and antimicrobial resistanceAngela Cesaro0Samuel C. Hoffman1Payel Das2Cesar de la Fuente-Nunez3Machine Biology Group, Department of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of PennsylvaniaIBM Research, Thomas J. Watson Research Center, Yorktown HeightsIBM Research, Thomas J. Watson Research Center, Yorktown HeightsMachine Biology Group, Department of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of PennsylvaniaAbstract Artificial intelligence (AI) has transformed infectious disease control, enhancing rapid diagnosis and antibiotic discovery. While conventional tests delay diagnosis, AI-driven methods like machine learning and deep learning assist in pathogen detection, resistance prediction, and drug discovery. These tools improve antibiotic stewardship and identify effective compounds such as antimicrobial peptides and small molecules. This review explores AI applications in diagnostics, therapy, and drug discovery, emphasizing both strengths and areas needing improvement.https://doi.org/10.1038/s44259-024-00068-x |
spellingShingle | Angela Cesaro Samuel C. Hoffman Payel Das Cesar de la Fuente-Nunez Challenges and applications of artificial intelligence in infectious diseases and antimicrobial resistance npj Antimicrobials and Resistance |
title | Challenges and applications of artificial intelligence in infectious diseases and antimicrobial resistance |
title_full | Challenges and applications of artificial intelligence in infectious diseases and antimicrobial resistance |
title_fullStr | Challenges and applications of artificial intelligence in infectious diseases and antimicrobial resistance |
title_full_unstemmed | Challenges and applications of artificial intelligence in infectious diseases and antimicrobial resistance |
title_short | Challenges and applications of artificial intelligence in infectious diseases and antimicrobial resistance |
title_sort | challenges and applications of artificial intelligence in infectious diseases and antimicrobial resistance |
url | https://doi.org/10.1038/s44259-024-00068-x |
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