The role of artificial intelligence in the diagnosis and treatment of infectious diseases: A narrative review

Zahra Nahardani, Seyedeh; Rahimian, Neda; Beheshtifar, Maryam

doi:10.22034/27.3.111

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Volume 27, Issue 3 (10-2025)

EBNESINA 2025, 27(3): 111-126

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The role of artificial intelligence in the diagnosis and treatment of infectious diseases: A narrative review

Seyedeh Zahra Nahardani

, Neda Rahimian ^*

, Maryam Beheshtifar

Department of Internal Medicine, School of Medicine, Firoozgar General Hospital. Iran University of ‎Medical Sciences, Tehran, Iran , rahimian.n@iums.ac.ir

Keywords: artificial intelligence, infectious diseases, therapeutics, diagnosis

Full-Text [PDF 1592 kb] (91 Downloads) | Abstract (HTML) (199 Views)

Type of Study: Review |
Received: 2025/03/8 | Revised: 2025/07/27 | Accepted: 2025/08/9 | Published: 2025/09/23

Extended Abstract: (81 Views)

Introduction

Infectious diseases are caused by pathogenic microorganisms that multiply after entering the body and can disrupt an individual's health [1]. The transmission routes of these diseases are highly diverse, including direct contact with an infected person or animal, consumption of contaminated food or water, and transmission by vectors such as insects [2]. These diseases remain a major challenge for healthcare systems worldwide, and according to the World Health Organization, infectious diseases impose additional costs on healthcare systems and the global economy [3]. In recent years, significant advances have been made in vaccine development and the eradication of infectious diseases. Digital technologies such as artificial intelligence (AI) and machine learning (ML) have been able to impact all aspects of human life and medical science [4]. These tools provide the ability to directly and dynamically monitor pathogen load during the course of a disease and can play an effective role in assessing disease progression and measuring treatment efficacy [5]. In addition to clinical data, sensor technology and medical devices enable the collection of additional data streams and real-time monitoring of patients, which can lead to faster and more accurate responses in clinical management [6]. Given the importance of this issue, the aim of the present review was to explain the role of artificial intelligence in the diagnosis and treatment of infectious diseases [7].

Methods

The present study is a narrative review article that comprehensively examines domestic and international studies published between 2018 and 2024. The search was conducted in English (PubMed and ScienceDirect) and Persian (SID, IranDoc, and Civilica) databases, as well as Google Scholar. The search strategy included a combination of terms related to infectious diseases, artificial intelligence, and their clinical and epidemiological applications. Inclusion criteria consisted of articles published in Persian or English from January 2018 to December 2024. Articles lacking full text, conference abstracts, case reports, letters, or those irrelevant to the research objective were excluded from the review. Ultimately, out of 195 identified articles, 43 articles were included in the present review after screening and quality assessment.

Results

Artificial intelligence represents the most advanced analytical tools capable of processing and analyzing vast volumes of heterogeneous and complex data, including clinical, laboratory, microbiological, genomic, and epidemiological information [8]. Through ML methods, it is possible to extract hidden patterns related to infectious diseases, which improves prediction accuracy [9].
ML strategies are broadly divided into two main categories: "supervised learning" and "unsupervised learning" [10]. In the field of infectious diseases, these approaches have found widespread application in infection diagnosis, disease severity prediction, and treatment response evaluation [11].
Feature selection is a fundamental step in developing machine learning models; since using all variables can lead to increased computational complexity and noise, employing feature selection methods to identify key factors affecting the occurrence and outcomes of infectious diseases is essential [12].
Evidence from health data-based studies shows that optimal feature selection can significantly improve the performance of ML algorithms in early diagnosis and disease severity prediction [13]. Furthermore, combining feature selection techniques with ML algorithms has been able to identify hidden patterns in demographic and environmental data, providing more accurate predictions in disease monitoring [14]. Hospital data-based studies have shown that using feature selection methods in predicting healthcare-associated infections increases model stability and improves performance metrics [15]. A systematic review of AI applications in infectious diseases indicates that feature engineering and selection is a determining factor in increasing the accuracy of prediction models, particularly in early diagnosis and patient risk assessment [16].
In the context of pandemic control, analyzing big data from various sources can help predict and identify infectious disease trends [17].
In recent years, AI has played an effective role in controlling the COVID-19 pandemic and predicting influenza outbreaks in different geographical regions [18]. Additionally, by analyzing social media and the potential spread of diseases, preventive and counteractive measures can be presented to authorities [19].
In the epidemiology of infectious diseases, spatiotemporal data analysis enables the identification of disease transmission trends [20]. ML and deep learning algorithms, alongside classical epidemiological methods, have increased prediction accuracy and improved the performance of surveillance systems [21].
To predict infectious disease trends, models such as ARIMA, as well as its combination with ML (such as random forest and artificial neural networks), have been able to improve prediction accuracy in areas with high population density and extensive mobility [22].
Recent studies in the field of zoonoses have shown that using ML algorithms can be highly effective in identifying and modeling environmental and demographic risk factors [23].
Moreover, analyzing qualitative and quantitative data based on individual and socio-economic characteristics is necessary to provide precise solutions [24].
In the analysis of infectious diseases, AI helps health specialists better understand pathogen characteristics and identify sensitive points in the disease transmission chain [26].
Regarding antimicrobial resistance, research has shown that ML algorithms can predict complex resistance patterns and play an effective role in managing this phenomenon [28]. Genomic surveillance of molecular resistance mechanisms helps in designing effective therapeutic interventions [27]. Additionally, various models have been designed to predict treatment response and determine the optimal antimicrobial combination [29].
Computer simulation studies also have applications in identifying effective factors and drug compounds to improve medical treatments [31]. In the management of sepsis and infections, advanced models like FAST (Fuzzy Adaptive System Technique) can rapidly predict pathogens and play a role in the treatment decision-making process [33].
Antibodies are one of the key mechanisms of the immune system in combating viral infections and form the basis of many vaccines [34]. In HIV management, ML algorithms have been able to assist in identifying high-risk populations and predicting prevalence, thereby helping to design targeted prevention strategies [35].
Evaluating patient adherence to treatment through pharmacy records, as well as using automated recording devices and the Internet of Things (IoT) to monitor viral load decay, has yielded positive results in data trend analysis and viral load classification [38]. Furthermore, hybrid AI-based methods have been able to improve the classification accuracy of antiretroviral treatment response and CD4⁺ T cell counts [39].
Despite the high potential of AI in medicine, challenges such as model complexity, difficulty in interpretation (black box), and the lack of comprehensive and standardized data exist, which can lead to non-generalizable or biased results [40]. Moreover, clinical adoption of AI requires staff training and adaptation to clinical structures, and these tools must remain under human supervision [41].
In the healthcare sector, IoT and AI models have been able to perform better than traditional statistical methods in the automatic detection of lung infections via ultrasound and in predicting the risk of Clostridium difficile infection leakage post-colectomy [44]. Additionally, integrating spatial and geographic data with patients' medical history is effective in reducing infection transmission in healthcare settings [43]. The use of humanoid robots and IoT-based online monitoring systems also enables continuous evaluation of infection status and the identification of at-risk patients [45].

Discussion and Conclusion

The findings of this narrative review indicate that artificial intelligence represents the most powerful analytical tools, playing a very important role in improving the diagnosis and treatment of infectious diseases. Unlike traditional statistical methods that require strict and limiting assumptions, AI is capable of analyzing complex and diverse clinical and epidemiological data without needing these assumptions, significantly increasing prediction and classification accuracy. AI plays a key role in predicting the outbreak of infectious diseases, controlling pandemics, and aiding strategic public health decision-making, especially in critical conditions such as COVID19. Furthermore, in improving treatments, predicting microbial resistance, and designing vaccines, it can lead to the optimization of therapeutic processes and cost reduction. However, remaining challenges, including input data quality and the complexity of interactions, require the combination of human expertise with AI models. Ultimately, the integration of novel technologies such as the Internet of Things and surveillance systems provides a bright outlook for the control and management of infectious diseases, making increased interdisciplinary collaboration and the promotion of a culture of utilizing these technologies essential to fully exploit their capacities in the diagnosis and treatment of infectious diseases.

Ethical Considerations

As this study is a narrative review based exclusively on previously published literature, it did not involve human or animal participants. Therefore, formal ethical approval was not required.

Funding

There is no funding support.

Authors’ Contribution

In this study, the first and third authors were responsible for data collection and initial manuscript drafting, while the second author led the conceptualization, data analysis, and manuscript editing. All authors contributed to the development and writing of the article. Each author reviewed and approved the final version of the manuscript.

Conflict of Interest

Authors declared no conflict of interest.

Acknowledgments

The authors would like to thank their colleagues for their constructive comments and scholarly support throughout the preparation of this narrative review.

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Zahra Nahardani S, Rahimian N, Beheshtifar M. The role of artificial intelligence in the diagnosis and treatment of infectious diseases: A narrative review. EBNESINA 2025; 27 (3) :111-126
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