Microbiology in the era of artificial intelligence: transforming medical and pharmaceutical microbiology

Abstract

In this mini-review, we delve into the transformative impact of artificial intelligence (AI) and machine learning (ML) in the field of microbiology. The paper provides a brief overview of various domains where AI is reshaping practices, including clinical diagnostics, drug and vaccine discovery, and public health management. Our discussion spotlights the implementation of convolutional neural networks for enhanced pathogen identification, the advancements in point-of-care diagnostics, and the emergence of new antimicrobials to tackle resistant strains. The application of AI in epidemiology, microbial ecology and forensic microbiology is also outlined, underscoring its proficiency in deciphering complex microbial interactions and forecasting disease outbreaks. We critically examine the challenges in AI application, such as ensuring data quality and overcoming algorithmic constraints, and stress the necessity for interpretable AI models that align with medical and ethical standards. We address the intricacies of digitalization in microbiology diagnostics, emphasizing the need for efficient data management in laboratory and clinical environments. Looking forward, we identify key directions for AI in microbiology, particularly focusing on developing adaptable, self-updating AI models and their integration into clinical settings. We conclude by highlighting AI's potential to revolutionize microbiological diagnostics and infection control, significantly influencing patient care and public health. This review serves as an invitation to explore AI's integration into microbiology, showcasing its role in evolving current methodologies and propelling future innovations.

Keywords:

• AI in microbiology
• machine learning diagnostics
• computational microbiology
• AI drug discovery
• ML antimicrobial resistance
• AI infectious diseases

Introduction

In the dynamic landscape of microbiology, the advent of artificial intelligence (AI) marks a revolutionary stride, reshaping the fabric of research and applications in this field. As we delve into this era, the unprecedented influx of biological data, derived from high-throughput technologies, demands a paradigm shift toward computational techniques for meaningful data interpretation. AI, particularly machine learning (ML), emerges as a pivotal tool in addressing complex microbiological challenges, ranging from predicting drug targets and diagnosing infectious diseases to deciphering microbial interactions and resistance mechanisms [1].

The integration of AI in microbiology is not limited to a single domain but spans virology, parasitology, mycology and bacteriology. The synergy of AI with advanced data collection devices, enhanced computing power and global networks—epitomized by Big Data, Moore’s Law, and the Internet—has catalyzed the effective application of AI in microbiology [2,3]. This fusion is particularly evident in the field of drug design, where AI-driven approaches are pioneering the development of novel antibiotics to combat multidrug-resistant organisms, a looming threat to global health [4].

AI's role in clinical microbiology is equally transformative, optimizing data processing and enhancing diagnostic accuracy. Techniques like matrix-assisted laser desorption/ionization time-of-flight mass spectrometry—(MALDI-TOF MS), coupled with AI, are revolutionizing microbial identification and antimicrobial resistance profiling, offering rapid, reliable and cost-effective solutions [5,6]. Moreover, AI’s capability to handle ‘big data’—such as genomic data and digital images from advanced diagnostic technologies—is instrumental in refining laboratory reports and accelerating diagnostic processes [7,8].

The potential of AI in microbiology extends to addressing public health challenges, such as the management of infectious diseases and sepsis, where AI aids in diagnosis, prognosis and personalized treatment strategies. Furthermore, AI's application in infection prevention and control is notable, with its ability to analyze large health datasets, aiding in outbreak detection and infection control strategies [1,9].

In this paper, we aim to explore the multifaceted role of AI in medical microbiology, emphasizing its impact on research and clinical applications. We will discuss how AI is not only reshaping current methodologies but also paving the way for novel discoveries and innovations in the field of microbiology, thereby significantly contributing to the advancement of healthcare and public health. This exploration into the convergence of microbiology and AI will provide insights into the current state of the art, challenges, and prospects of this interdisciplinary alliance.

Fundamentals of AI in microbiology

Machine learning (ML)

Machine learning, a vital component of artificial intelligence (AI), is reshaping microbiology. This technology offers robust methods for data analysis with applications ranging from microbial identification to predicting antibiotic resistance. The support vector machine (SVM) algorithm has been used to identify known and novel antimicrobial resistance genes in Mycobacterium tuberculosis using a dataset of over 1500 genomes [10]. A general overview of machine learning applications in microbiology is schematically presented in Figure 1.

Figure 1. General workflow and example for machine learning applications in microbiology.

Basics of machine learning

Machine learning enables computers to learn from data, making decisions autonomously. This is particularly beneficial in microbiology, allowing for new perspectives in microbial analysis, and significantly enhancing our understanding of microbial behavior and disease outcomes [10].

Supervised learning

Supervised learning involves training algorithms with labeled datasets. In microbiology, this method is effective for tasks like predicting microbial behavior or disease outcomes. Adaptive boosting classifiers are developed for identifying antimicrobial resistance in various bacterial species [11,12].

Unsupervised learning

Unsupervised learning involves analyzing unlabeled data to uncover hidden patterns in complex microbial datasets. This method is invaluable for enhancing our understanding of diverse microbial communities and their relationships [13–17].

The specimens are represented on a pink background, symbolizing the initial dataset. From each specimen, a diverse array of molecular attributes such as DNA, RNA and proteins, along with phenotypic traits like cellular shape, motility and acidity levels, are recorded. These attributes comprise a comprehensive set of features, denoted by F1 through Fn. Concurrent with feature collection, specific target outcomes are identified, which the machine learning model will endeavor to predict. These targets are derived from additional data correlated with the specimens and are visually distinguished by a blue background, linked to the features via bidirectional black arrows. The next stage is the training phase, which is underpinned by a violet hue and involves processing the input data, which includes metrics like the relative abundance of microbial populations, quantification of metabolic products, and patterns of gene activity. This phase is critical and consists of a series of methodical steps: selecting an appropriate machine learning model, fine-tuning its parameters to enhance accuracy and establishing the final model while also pinpointing the most influential features. Post-training, the model is poised for the prediction phase, where it encounters new, previously unseen biological samples that are unlabeled, designated by a yellow background. These samples are processed to extract the same type of features used in the training phase. Finally, the model applies its learned patterns to these features to predict the unknown target outcomes, which remain as question marks. This predictive capability enables the extrapolation of significant insights from novel biological data, furthering the understanding of microbial behaviors and interactions.

The role of deep learning

Deep learning, an advanced subset of machine learning, uses neural networks to process extensive datasets. In microbiology, deep learning models like deep antibiotic resistance genes (ARG) [18] are used for predicting antibiotic resistance genes, aiding in environmental monitoring [19].

ML in microbial data analysis and predictive modeling

Machine learning is adept at processing and analyzing large volumes of microbial data. Its use in predictive modeling, particularly in forecasting trends in microbial behavior or disease spread, plays a crucial role in public health and microbiological research [18,19].

Challenges in ML for microbiology

Applying ML in microbiology presents challenges, particularly in terms of data quality and quantity. Effective ML models require large, diverse datasets for accurate training and predictions [11].

Applications of AI in microbiology and current advances

Advancements in clinical diagnostics and disease identification

Artificial intelligence (AI) and machine learning (ML) are revolutionizing medical microbiology, offering rapid and precise diagnostics and treatments for infectious diseases. In malaria diagnosis, for example, ML techniques such as convolutional neural networks (CNNs) applied in computer-aided diagnosis (CADx) software significantly enhance the accuracy of identifying malaria-infected cells [20–22]. Similar methodologies have advanced parasite detection in fecal samples, surpassing traditional human slide examination in sensitivity [23].

Automated systems are now capable of analyzing laboratory samples represented as images, transforming the recognition of bacterial genera and species. These systems identify bacteria based on shape, color, and colony patterns, crucial in various sectors including medical, veterinary and food industries [24–28].

Epidemiology and disease prognosis

ML models like logistic regression (LR) and artificial neural networks (ANN) are used in epidemiology to predict patient outcomes for diseases such as Ebola virus disease (EVD). Tools like the Ebola Care app provide healthcare personnel with critical insights for treatment decisions by analyzing clinical symptoms and laboratory data [29].

Point-of-care (POC) diagnostics

The impact of ML on point-of-care (POC) diagnostics is transformative. In diagnosing sexually transmitted infections like trichomoniasis, ML algorithms distinguish between positive and negative cases using routine test results, such as urinalysis [30]. This approach is particularly beneficial in resource-limited settings. Advances in smartphone technology have also enabled the development of mobile microbiological laboratories, making diagnostics more accessible [31–33].

Drug and vaccine discovery

ML is invaluable in identifying new compounds and vaccine candidates. It has been used to analyze data from public databases like opportunistic infection and tuberculosis therapeutics database (ChemDB) for potential antiviral compounds against human immunodeficiency virus (HIV), demonstrating its potential to accelerate drug discovery [34,35]. In silico vaccine candidate selection using ML classifiers has proven effective for Apicomplexan pathogens [36–38].

Antimicrobial resistance and outbreak prediction

ML plays a crucial role in understanding and combating antimicrobial resistance (AMR). It has been instrumental in identifying drug resistance in pathogens like tuberculosis (TB) and HIV, opening new avenues for resistance prediction and management [39–45]. Additionally, ML has been used to predict disease outbreaks using data from non-traditional sources like social media and search engines, indicating its potential in real-time infectious disease tracking [46–49].

Microbial ecology and forensics

In microbial ecology, ML aids in understanding the interactions and dynamics of microbial communities. algorithms like random forest (RF) have been used to predict interactions within ecosystems [50,51] Forensic microbiology benefits from ML in analyzing post-mortem microbiomes, significantly contributing to forensic science and public health diagnostics [51].

Dimensionality reduction and microbiome analysis

ML is crucial in microbiome analysis, especially in dimensionality reduction methods like the generalized linear model-based ordination method for microbiome samples (GOMMS), Figure 2. These techniques address challenges in microbiome dataset analysis, enhancing our understanding of microbial communities [52,53].

Figure 2. A schematic diagram illustrating the process of analyzing microscopic images of microorganisms using deep learning techniques, focusing on the assessment of their geometric properties and macroscopic resemblance.

Enhancing productivity and precision in medical microbiology

AI's role in enhancing productivity and precision in medical microbiology is significant. Automated susceptibility tests and pathogen identification streamline laboratory processes, addressing challenges posed by manual analysis of complex data like images, spectra and DNA–RNA sequences, especially in the face of staff shortages and intricate analyses [54,55].

AI applications in microbiology are diverse:

• Automated interpretation of blood culture Gram stains using convolutional neural networks (CNNs) [56].

• Automation in culture plate image analysis, improving sensitivity and time efficiency [57–59].

• Advancements in MALDI-TOF MS for direct sample identification and AMR detection [60–64].

• Using WGS and ML to predict AMR by analyzing DNA sequences [65,66].

AI in Healthcare and microbial diagnosis

AI's multifaceted role in healthcare extends to microbial diagnosis through ML, deep learning, and natural language processing (NLP). It is particularly effective in structured data analysis from hospital labs and onco-radiology [67]. NLP facilitates the creation and maintenance of EMRs, enabling the diagnosis of various diseases through speech and text analysis [68].

AI applications in culture interpretation involve developing complex algorithms for culture identification and enhancing efficiency in microbial culture analysis [69,70]. Automated systems like automated plate assessment system (APAS) Independence and PhenoMatrix have advanced culture analysis in urine samples and Methicillin-resistant Staphylococcus aureus MRSA detection [71,72]. AI predicts antimicrobial susceptibility patterns, aiding in the early detection and treatment of drug-resistant pathogens [73]. Total laboratory automation systems such as Kiestra Total Laboratory Automation (TLA) and WASPLab are examples of AI's integration into large-scale laboratory processes [74].

Sepsis management

AI's pivotal role in early warning systems for sepsis is evident through models that predict the onset of sepsis in advance, surpassing traditional scoring methods [75]. These models have been expanded to include routine clinical variables, enhancing sepsis prediction feasibility in various settings [76–79]. AI has also improved sepsis diagnosis by screening tools based on big data and machine learning, incorporating unstructured textual data for increased accuracy [80,81].

Clinical studies confirm AI's effectiveness in sepsis management, linking early warning systems to reduced mortality and shorter hospital stays [82]. AI also aids in sepsis subtyping, classifying distinct phenotypes with varying clinical profiles [83]. In pathogen identification and antimicrobial susceptibility testing, AI models rapidly identify common bacteria and fungi, optimizing empirical antibiotic therapy [84]. AI models guide fluid resuscitation and management in sepsis treatment, with predictive models for post-resuscitation urine output and fluid responsiveness [85,86]. Causal inference frameworks estimate treatment effects, facilitating personalized medical care [87].

AI models have significantly improved in-hospital mortality rates and reduced hospital stays in sepsis prognostication [88,89]. However, further research is needed to validate these models and integrate them into clinical practice [90].

Advancing infection surveillance and control

AI is revolutionizing the surveillance of healthcare-associated infections (HAIs) and enhancing infection prevention and control (IPC). Its application in interpreting complex datasets from electronic healthcare records (EHRs) is crucial for monitoring infection trends and evaluating intervention strategies [91]. AI's role in diagnosing infections with IPC implications is exemplified by its use in tuberculosis detection via deep learning applied to chest radiography [92]. AI-enhanced microscopy and ML algorithms are employed in laboratories for rapid diagnosis and targeted antimicrobial management [93–94].

Despite AI’s transformative potential, challenges in acquiring high-quality datasets for model development persist [95]. Nevertheless, AI promises improved efficiency in infection surveillance and a significant impact on public health management and patient care strategies.

AI in antibiotic discovery against MRSA

The integration of AI and ML in microbiology has been pivotal in discovering new antibiotics against MRSA [96]. AI's ability to process and analyze extensive datasets has accelerated the identification of potential antibiotic candidates (Figure 3). The research methodology involves training, validating and assessing ML models using features computed by RDKit [97,98], with performance evaluated using AUPRC [98].

Figure 3. Computer-aided drug design, expansive databases are harnessed to sieve through and identify key pharmacological attributes that determine the functionality and efficacy of compounds. Regardless of the methodology employed, the resultant data form the cornerstone for devising a suite of innovative compounds. These novel entities undergo rigorous testing, with the ensuing results providing fresh insights into their pharmacological profiles. This iterative cycle continues, refining the compounds through successive stages until a robust scoring algorithm yields prospective antibiotic agents. These optimized candidates are then subjected to critical evaluation, examining their in vivo efficacy and safety profiles. The frontrunners that emerge from this meticulous process stand as strong contenders for advancement into clinical trial phases.

AI's role in analyzing and visualizing the chemical space of compounds aids in differentiating between effective and non-effective antibiotics [99,100]. Deep learning models and Monte Carlo tree searches identify specific structural classes predictive of antibiotic activity [101,102]. AI-driven predictions undergo rigorous experimental validation, underscoring the synergy between computational predictions and traditional experimental methods. AI-guided studies provide insights into the compounds’ pharmacodynamics, resistance pathways and overall efficacy [103–108].

In summary, the integration of AI and ML in microbiology represents a paradigm shift in the field. From enhancing clinical diagnostics to accelerating drug discovery, predicting disease outbreaks and analyzing microbial communities, AI's role is multifaceted and continuously evolving. The integration of computational methods with biological data is forging a path toward more nuanced and effective disease diagnosis, treatment and ecological understanding, reflecting AI and ML’s profound impact on current microbiological research.

Challenges, future trends, and directions in AI and ML applications in microbiology

The integration of AI and ML in microbiology heralds a transformative era in diagnostic capabilities and infection prevention and control (IPC). However, this integration is not without its challenges and limitations, which must be addressed to harness their full potential.

Data quality and algorithm challenges

The effectiveness of ML algorithms in microbiology is contingent upon the quality of training data. Obtaining high-quality data is a persistent challenge, as data sources are often imperfect and may introduce noise. Advanced techniques in data cleaning and normalization are expected to mitigate issues related to noisy and unbalanced data. Future trends will likely focus on developing methods to improve the quality of training data and enhance the generalizability of algorithms. Additionally, efforts in explainable AI (XAI) aim to create more interpretable and transparent models, building trust among users [108].

AI in IPC: challenges and prospective evaluation

AI offers significant advantages in IPC, such as handling large datasets and providing consistent outputs. However, AI's dependency on data quality and the absence of robust reference standards in IPC present challenges. Future directions include conducting more prospective studies in clinical settings and building robust collaboration frameworks between AI developers and IPC experts to ensure clinical relevance and sensitivity to healthcare data nuances [109–111].

Universal applicability and clinical integration of AI models

The limited acceptance of AI by medical professionals, due to its complex logic and misalignment with conventional medical reasoning, poses a challenge. Future trends involve developing AI models capable of self-updating and adapting to different healthcare environments, enhancing their relevance and effectiveness. Efforts will be made to enhance the acceptance of AI among healthcare professionals through education and alignment with medical reasoning [112–118].

Digitalization in diagnostic processes

The digitalization of microbiology diagnostic processes brings challenges in data management, including collection, quality control, storage and security. The trend toward digitalization will continue, with an increasing focus on managing the explosion of data through advanced analytics tools and sophisticated data visualization techniques. Training laboratory personnel in digital skills to manage and analyze this data effectively is also a key trend [119–126].

Legal and ethical considerations

Data collection, analysis and exchange in microbiology must comply with legal and regulatory requirements, including the General Data Protection Regulation (GDPR) in Europe. The future will bring more rigorous discussions and potentially new regulations around data privacy, ethical considerations and patient consent in the context of AI and ML in microbiology. Harmonizing legal frameworks and developing universal standards for data sharing and usage, guided by principles like FAIR, are essential [127–130].

ML applications in microbiology laboratories

Machine learning can significantly enhance the diagnostic process in microbiology laboratories. Future trends in ML applications will focus on integrating algorithms throughout the diagnostic process, from pre-analytics to post-analytics. Emphasis will be on creating algorithms that can analyze complex interactions within bacterial networks and provide nuanced insights into microbial resistance and pathogenicity [131–137].

The future of AI and ML in microbiology is poised for significant advancements. Key trends include enhancing data quality, developing adaptable and interpretable AI models, integrating AI into clinical practice, managing the challenges of digitalization, navigating the complex legal and ethical landscape, and leveraging ML across the microbiological diagnostic process. These advancements, addressing current challenges, are expected to revolutionize microbiological diagnostics and IPC, leading to improved patient care and public health outcomes.

Conclusions

The integration of AI and ML in microbiology marks a transformative shift in biomedical research and clinical practice. This exploration has unveiled extensive applications of these technologies, from enhancing diagnostic accuracy in clinical microbiology to pioneering drug discovery and advancing public health management. AI's capability to process and analyze complex biological data has led to significant strides in identifying pathogens, predicting antimicrobial resistance and managing infectious diseases. Despite these advancements, challenges such as data quality, algorithmic limitations and ethical considerations remain significant. There is a pressing need for adaptable, interpretable AI models that can be seamlessly integrated into clinical settings, aligning computational power with medical expertise and ethical standards.

Looking ahead, envision a future where AI and ML are integral to microbiological research and practice. The development of self-updating, adaptable AI models, capable of functioning in diverse healthcare environments, is crucial. Furthermore, integrating AI into clinical practice will necessitate enhanced education and training in AI-based technologies for healthcare professionals.

In conclusion, AI and ML hold the promise of revolutionizing microbiology, offering unprecedented opportunities to enhance patient care and public health outcomes. Addressing current challenges and focusing on future developments are essential for AI and ML to significantly contribute to the advancement of healthcare, heralding a new era of precision medicine in microbiology.

Acknowledgments

In the creation of this research paper, no artificial intelligence tools were utilized for content generation or research purposes.

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability

The data supporting the findings of this study are available from the corresponding author upon reasonable request.

Additional information

Funding

The author(s) reported there is no funding associated with the work featured in this article.