TY - JOUR
T1 - Medical Artificial Intelligence
T2 - Opportunities and Challenges In Infectious Disease Management
AU - Sahoo, Ankit
AU - Singh, Janhvi
AU - Alam, Kainat
AU - Alruwaili, Nabil K.
AU - Aodah, Alhussain
AU - Almalki, Waleed H.
AU - Almujri, Salem Salman
AU - Alrobaian, Majed
AU - Barkat, Md Abul
AU - Singh, Tanuja
AU - Lal, Jonathan A.
AU - Rahman, Mahfoozur
N1 - Publisher Copyright:
© 2025 Bentham Science Publishers
PY - 2025
Y1 - 2025
N2 - Globally, millions of individuals suffer from infectious diseases, which are major public health concerns caused by bacteria, fungi, viruses, or parasites. These diseases can be transmitted directly or indirectly from person to person, potentially leading to a pandemic or epidemic. Several advancements have been made in molecular genetics for infectious disease management, which include pharmaceutical chemistry, medicine, and infection tracking; however, these advancements still lack control over human infections. Multidisciplinary cooperation is needed to address and control human infections. Advancements in scientific tools have empowered scientists to enhance epidemic prediction, gain insights into pathogen specificity, and pinpoint potential targets for drug development. Artificial intelligence (AI)-based methodologies demonstrate significant potential for integrating large-scale quantitative and omics data, enabling effective handling of biological complexity. Machine Learning (ML) plays a crucial role in AI by leveraging data to train predictive models. AI can enhance diagnostic accuracy through objective pattern recognition, standardize infection diagnoses with implications for Infection Prevention and Control (IPC), and aid in generalizing IPC knowledge. Additionally, AI-powered hand hygiene applications have the potential to drive behavioral change, although further evaluation in diverse clinical contexts is necessary. This review article highlights AI's potential in improving the healthcare system in different aspects of infectious diseases management, such as monitoring disease growth, using a real-time chatbot for patient assistance, using image processing for diagnosis, and developing new treatment algorithms. The study also discusses future directions for novel vaccine and drug development, as well as other aspects, such as the need for physicians and healthcare professionals to receive AI system training for their correct use and the ability of doctors to identify and resolve any problems that may arise with AI.
AB - Globally, millions of individuals suffer from infectious diseases, which are major public health concerns caused by bacteria, fungi, viruses, or parasites. These diseases can be transmitted directly or indirectly from person to person, potentially leading to a pandemic or epidemic. Several advancements have been made in molecular genetics for infectious disease management, which include pharmaceutical chemistry, medicine, and infection tracking; however, these advancements still lack control over human infections. Multidisciplinary cooperation is needed to address and control human infections. Advancements in scientific tools have empowered scientists to enhance epidemic prediction, gain insights into pathogen specificity, and pinpoint potential targets for drug development. Artificial intelligence (AI)-based methodologies demonstrate significant potential for integrating large-scale quantitative and omics data, enabling effective handling of biological complexity. Machine Learning (ML) plays a crucial role in AI by leveraging data to train predictive models. AI can enhance diagnostic accuracy through objective pattern recognition, standardize infection diagnoses with implications for Infection Prevention and Control (IPC), and aid in generalizing IPC knowledge. Additionally, AI-powered hand hygiene applications have the potential to drive behavioral change, although further evaluation in diverse clinical contexts is necessary. This review article highlights AI's potential in improving the healthcare system in different aspects of infectious diseases management, such as monitoring disease growth, using a real-time chatbot for patient assistance, using image processing for diagnosis, and developing new treatment algorithms. The study also discusses future directions for novel vaccine and drug development, as well as other aspects, such as the need for physicians and healthcare professionals to receive AI system training for their correct use and the ability of doctors to identify and resolve any problems that may arise with AI.
KW - AI
KW - epidemiology
KW - infection diagnosis
KW - infection prevention
KW - ML
KW - prediction
KW - wearable devices
UR - https://www.scopus.com/pages/publications/105017480696
U2 - 10.2174/0109298673390868250728103906
DO - 10.2174/0109298673390868250728103906
M3 - Review article
C2 - 40917030
AN - SCOPUS:105017480696
SN - 0929-8673
JO - Current Medicinal Chemistry
JF - Current Medicinal Chemistry
ER -