Confronting the Disruption of the Infectious Diseases Workforce by Artificial Intelligence : What This Means for Us and What We Can Do About It
© The Author(s) 2024. Published by Oxford University Press on behalf of Infectious Diseases Society of America..
With the rapid advancement of artificial intelligence (AI), the field of infectious diseases (ID) faces both innovation and disruption. AI and its subfields including machine learning, deep learning, and large language models can support ID clinicians' decision making and streamline their workflow. AI models may help ensure earlier detection of disease, more personalized empiric treatment recommendations, and allocation of human resources to support higher-yield antimicrobial stewardship and infection prevention strategies. AI is unlikely to replace the role of ID experts, but could instead augment it. However, its limitations will need to be carefully addressed and mitigated to ensure safe and effective implementation. ID experts can be engaged in AI implementation by participating in training and education, identifying use cases for AI to help improve patient care, designing, validating and evaluating algorithms, and continuing to advocate for their vital role in patient care.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:11 |
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| Enthalten in: |
Open forum infectious diseases - 11(2024), 3 vom: 15. März, Seite ofae053 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Langford, Bradley J [VerfasserIn] |
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| Links: |
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| Themen: |
Antimicrobial stewardship |
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| Anmerkungen: |
Date Revised 05.03.2024 published: Electronic-eCollection Citation Status PubMed-not-MEDLINE |
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| doi: |
10.1093/ofid/ofae053 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
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| 520 | |a With the rapid advancement of artificial intelligence (AI), the field of infectious diseases (ID) faces both innovation and disruption. AI and its subfields including machine learning, deep learning, and large language models can support ID clinicians' decision making and streamline their workflow. AI models may help ensure earlier detection of disease, more personalized empiric treatment recommendations, and allocation of human resources to support higher-yield antimicrobial stewardship and infection prevention strategies. AI is unlikely to replace the role of ID experts, but could instead augment it. However, its limitations will need to be carefully addressed and mitigated to ensure safe and effective implementation. ID experts can be engaged in AI implementation by participating in training and education, identifying use cases for AI to help improve patient care, designing, validating and evaluating algorithms, and continuing to advocate for their vital role in patient care | ||
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