ReviewCurrent medicinal chemistry2026
Medical Artificial Intelligence: Opportunities and Challenges In Infectious Disease Management.
Review in Current medicinal chemistry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
Indexed as
Identifiers
40917030What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.