Medical Artificial Intelligence: Opportunities and Challenges In Infectious Disease Management
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, AIpowered 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.
Authors
- Jonathan A. Lal (ORCID: https://orcid.org/0000-0002-1929-1843)
- Alhussain H. Aodah (ORCID: https://orcid.org/0000-0002-5344-0777)
- Jagdish Singh (ORCID: https://orcid.org/0000-0003-3198-2839)
- Mahfoozur Rahman (ORCID: https://orcid.org/0000-0002-5616-4026)
- Salem Salman Almujri (ORCID: https://orcid.org/0000-0003-0263-5053)
- Ankit Sahoo (ORCID: https://orcid.org/0000-0002-7100-2447)
- Kainat Alam
- Nabil K. Alruwaili (ORCID: https://orcid.org/0000-0003-2358-4259)
- Majed Alrobaian (ORCID: https://orcid.org/0000-0002-8047-3515)
- Md. Abul Barkat (ORCID: https://orcid.org/0000-0001-6009-5222)
- Tanuja Singh
- Waleed Hassan Almalki (ORCID: https://orcid.org/0000-0003-2584-8510)
Institutions
- Prince Sattam Bin Abdulaziz University (SA)
- Taif University (SA)
- Umm al-Qura University (SA)
- Jouf University (SA)
- Sam Higginbottom Institute of Agriculture (IN)
- KES College (CY)
- Central University of Tibetan Studies (IN)
- University of Hafr Al-Batin (SA)
- Block Center (US)
- Jamia Millia Islamia (IN)
- King Khalid University (SA)
Publication Details
- Journal
- Current Medicinal Chemistry
- Published
- 2025-08-22
- DOI
- https://doi.org/10.2174/0109298673390868250728103906
- Primary Topic
- COVID-19 diagnosis using AI
- Type
- article
- Field-Weighted Citation Impact
- 0.00