Disrupting the infectious disease ecosystem in the digital precision health era innovations and converging emerging technologies

ABSTRACT

This commentary explores the convergence of precision health and evolving technologies, including the critical role of artificial intelligence (AI) and emerging technologies in infectious diseases (ID) and microbiology. We discuss their disruptive impact on the ID ecosystem and examine the transformative potential of frontier technologies in precision health, public health, and global health when deployed with robust ethical and data governance guardrails in place.

GLOBAL INFECTIOUS DISEASES LANDSCAPE

Before reviewing recent technological advancements, it is imperative to underscore that the ongoing major infectious diseases (ID) continue to present substantial challenges to public health and are being addressed by global organizations, which have published global action plans (1, 2). These global action plans underscore the pressing need to combat infectious diseases, non-communicable diseases, and antimicrobial resistance to safeguard global public health and mitigate the impact of future pandemics.

The landscape of infectious diseases has been revolutionized by emerging technologies, exerting influence across various dimensions of the ecosystem, encompassing prevention, surveillance, diagnostics, chronic disease management, and targeted therapeutics.

These advancements have fundamentally transformed our approach to infectious diseases by offering novel tech-enabled approaches to ID by early identification of high-risk populations, prevention, surveillance, compliance, targeted interventions, and efficient allocation of resources. These new ID solutions have also empowered individuals to make informed decisions about hygiene practices, vaccination adherence, and lifestyle modifications that minimize the risk of infection. The field of diagnostics has also witnessed remarkable progress with the advent of emerging technologies. Techniques such as polymerase chain reaction, next-generation sequencing, and point-of-care testing have revolutionized diagnostic capabilities. These technologies can enable early identification of infectious agents or antimicrobial resistance, facilitating timely antimicrobial stewardship and the implementation of infection control measures (3).

Chronic disease management has the potential to undergo a revolution by applying emerging technologies. Remote monitoring devices, telemedicine, and digital health platforms enable personalized care for individuals grappling with chronic diseases (4). These technologies facilitate continuous monitoring of disease progression, medication adherence, and patient engagement, resulting in improved outcomes and enhanced quality of life. The advent of genomics (5) and multi-omics has paved the way for targeted therapeutics in infectious diseases. Precision medicine (6) and health approaches, leveraging individual genetic profiles, will allow for the development of personalized treatment strategies. These technologies can identify specific molecular targets for therapies, leading to enhanced efficacy and reduced side effects. As we introduce novel concepts, please refer to Table 1 for a glossary of terms with references and examples.

TABLE 1.

Glossary of AI, emerging and frontier technologies terminology

Term	Definition	Reference/links
Machine Learning	Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy	https://www.ibm.com/topics/machine-learning
Edge AI	The deployment of AI algorithms and models directly on edge devices, enabling real-time processing and analysis of data at the network's edge.	https://blogs.nvidia.com/blog/2022/02/17/what-is-edge-ai/
Generative AI	AI systems that create new content, such as images, text, or music, resembling human-generated content, through advanced algorithms and models.	https://www.techtarget.com/searchenterpriseai/definition/generative-AI
Foundational AI Model	Pretrained deep learning models that serve as the basis for various AI applications, providing a broad understanding of language, images, or data.	https://hai.stanford.edu/news/what-foundation-model-explainer-non-experts
Deep Learning	A subfield of machine learning that utilizes neural networks to learn hierarchical representations of data, enabling complex pattern recognition.	https://www.ibm.com/topics/deep-learning
Cognitive AI	AI systems that simulate human-like cognitive abilities, including perception, reasoning, and learning, to perform complex tasks and decision-making Cognitive AI systems differ from generative AI as they synthesize data from various information sources while weighing context and conflicting evidence to suggest suitable answers.	https://www.edureka.co/blog/cognitive-ai/
Federated Learning	A distributed learning approach where models are trained on decentralized data sources, preserving privacy, and enabling collaborative model improvement.	https://ai.googleblog.com/2017/04/federated-learning-collaborative.html
Digital Twin	The creation of virtual replicas or simulations of physical objects, processes, or systems, enabling real-time monitoring, analysis, and optimization.	https://www.ibm.com/topics/what-is-a-digital-twin
Computer Vision	The field of AI and computer science that enables machines to interpret and understand visual information from images or videos, mimicking human vision.	https://www.ibm.com/topics/computer-vision
Robotics	The interdisciplinary field involving the design, development, and application of robots to perform tasks autonomously or in collaboration with humans.	https://www.automate.org/robotics
Exascale Computing	High-performance computing systems capable of performing at least one quintillion calculations per second, significantly exceeding the capabilities of traditional computers.	https://www.scientificamerican.com/article/new-exascale-supercomputer-can-do-a-quintillion-calculations-a-second/
NeuromorphicComputing	Computing systems that mimic the structure and functionality of the human brain, often implemented on electronic circuits, enabling energy-efficient and highly parallel processing.	https://www.humanbrainproject.eu/en/science-development/focus-areas/neuromorphic-computing/
Blockchain	Distributed digital ledgers of cryptographically signed transactions grouped into blocks. Each block is linked to the previous one, creating a tamper-evident and tamper-resistant record. Blockchain ensures transparency, security, and immutability in various applications, including cryptocurrency, supply chain management, and smart contracts.	https://nvlpubs.nist.gov/nistpubs/ir/2018/NIST.IR.8202.pdf
Quantum Computing	A field of computer science that leverages principles of quantum theory to develop computing systems capable of performing complex calculations and solving problems beyond the reach of classical computers. Quantum computers use qubits, which can exist in multiple states simultaneously, enabling parallel processing and potentially solving problems more efficiently than classical computers.	https://standards.ieee.org/news/ieee_p7130/
DNA Computing.	DNA computing (or biomolecular computing) represents a novel approach to solving complex computational problems using DNA molecules as information carriers.	https://link.springer.com/referenceworkentry/10.1007/978-0-387-30440-3_131
Multi-purpose High-performance Multiomics Computation Platforms:	Technology platforms combining multiple types of technologies and multiomics data sets (multiple omics).	https://frontlinegenomics.com/an-overview-of-omics-technologies-in-multi-omics/
Multi-Omics	A biological analysis approach that combines genomic data with data from other modalities such as transcriptomics, epigenetics, and proteomics, to measure gene expression, gene activation, and protein levels.	https://www.genome.gov/research-funding/Funded-Programs-Projects/Multi-Omics-for-Health-and-Disease
Computational Psychopharmacology	Computational psychopharmacology is a quantitative branch of neuroscience that relates behavioral effects of drugs. It is powered by computational modelling and that can be used to help translate behavioral findings.	https://link.springer.com/article/10.1007/s00213-019-05302-3
Web 3.0	Next iteration of the world wide web, is powered by multiple emerging technologies and characterized by decentralized networks offering increased user control	https://arxiv.org/pdf/2304.06032.pdf
Nanotechnology	Nanotechnology is a process that combines the basic attributes of biological, physical, and chemical sciences on atomic and near-atomic scales.	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9865684/
Bio-Implants	Engineered tissue graft processed from cortical, cortical/cancellous, cancellous bone or variations thereof, including without limitation ramps, dowels, wedges, blocks, and/or other forms variations thereof.	https://www.lawinsider.com/dictionary/bioimplants
Biometrics	Biometrics are unique physical characteristics, such as fingerprints, that can be used for automated recognition	https://www.dhs.gov/biometrics
internet of Things	The internet of Things (IoT) can be defined as a world of interconnected things that are capable of sensing, actuating, and communicating among themselves and with the environment (i.e., smart things or smart objects). In addition, IoT provides the ability to share information and autonomously respond to real/physical world events by triggering processes and creating services with or without direct human intervention.	https://ieeexplore.ieee.org/document/8390728
Wireless Networks (5G standard)	Fifth-generation (5G) networks offer high-speed data transmission with low latency, increased base station volume, improved quality of service (QoS), and massive multiple-input–multiple-output (M-MIMO) channels compared to 4G long-term evolution (LTE) networks	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10255561/
Smart Health	A healthcare system that enables patients and doctors to communicate with each other and remotely exchange information monitored, collected, and analyzed from patients’ daily activities via the IoT	https://www.igi-global.com/dictionary/smart-healthcare/76574
Human Computer Interfaces	Human computer interfaces refer to all modalities through which people interact with computational technologies. These interfaces have generated a new field of study “ Human–Computer Interaction (HCI) “, which is focusing on the way in which computer technology influences human work and activities.	https://www.sciencedirect.com/topics/computer-science/human-computer-interfaces
Trust	The confidence, reliance, and belief in the reliability, integrity, and security of AI systems and their outputs. Trust is a crucial factor in the widespread adoption and acceptance of AI technologies.	https://www.frontiersin.org/articles/10.3389/fdgth.2022.815573/full
Precision Medicine	An approach to healthcare that tailors medical treatments and interventions to the unique characteristics of each individual patient for improved outcomes.	https://www.nih.gov/about-nih/what-we-do/nih-turning-discovery-into-health/promise-precision-medicine
Precision Health	A broader concept encompassing precision medicine, focusing on personalized healthcare strategies that consider genetic, environmental, and lifestyle factors.	https://www.uclahealth.org/precision-health/about-us/what-precision-health #:~: text = Precision %20health%20takes%20into%20account,complex%20the%20human%20body%20is.

LEVERAGING AI IN RECONFIGURING THE PRECISION INFECTIOUS DISEASES ECOSYSTEM

Precision ID is a novel concept that aims to apply personalized and targeted approaches to the prevention, diagnosis, and treatment of infectious diseases. To date, to our knowledge there is no clear definition available in the published English literature. Nonetheless, we propose extrapolating from the National Institutes of Health, and Centers for Disease Control and Prevention definitions of “precision medicine” and “infectious diseases” integrating clinical, multi-omics, immune response, environmental data, and microbial profile to develop tailored strategies to prevent and treat infectious diseases. Additionally, genomic sequencing or molecular profiling could be adapted to accurately identify the causative agent of an infection and determine its susceptibility to different treatment options (7).

The deployment of a vast portfolio of AI tools has already transformed numerous aspects of infectious disease research, education, and clinical management. AI-powered diagnostic tools (i) can analyze vast amounts of clinical, epidemiological, and genomic data to identify patterns and predict disease outcomes; (ii) can aid in the rapid and accurate identification of pathogens; and (iii) can assist in forecasting disease outbreaks and optimizing resource allocation. Furthermore, AI-driven drug discovery platforms have the potential to accelerate the development of new antimicrobial agents and antimicrobial stewardship, while minimizing the risk of adverse effects and the development of antimicrobial resistance (8). The integration of AI techniques is also transforming the field of microbiology by enhancing the diagnosis, treatment, research, and understanding the microbial realm (9).

UNDERSTANDING THE TYPES OF AI

There are several tools and methods in the AI portfolio; however, a few have been impactful in infectious diseases, such as machine learning (ML), generative AI, foundational models, cognitive AI, adaptive AI, edge AI, natural language processing, neural networks, computer vision, and robotics (Fig. 1).

Fig 1.

Artificial Intelligence (AI) portfolio impact on infectious diseases.

Machine learning

Machine learning is a branch ofArtificial Intelligence (AI) and computer science, which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy. Examples include ML algorithms that can identify patterns, predict antibiotic resistance from genome sequencing (10), and expedite microorganism classification in images; natural language processing that extracts relevant data from the scientific literature or robotics platforms that automate laboratory procedures, freeing scientists from complex tasks like screening compounds for antimicrobial activity.

AI foundation models

Also known as AI base models or AI core models (11), AI foundation models are pre-trained deep learning models that serve as the building blocks for various artificial intelligence applications. These models are trained on large data sets and possess a broad understanding of language, images, or other types of data. They capture the fundamental knowledge and patterns required for tasks such as natural language processing, image recognition, or speech synthesis.

Generative AI

One of the critical areas where generative AI has been successfully applied is the development of new drugs and treatments for ID. Leveraging generative AI can lead to enhanced insights from large data sets to aid novel drug discovery and design, or to develop targeted therapeutics for specific viral or bacterial proteins. Another high impact domain is the ability to train AI models to improve the accuracy of automated diagnosis systems. By accelerating the ID diagnostic process, healthcare professionals can prevent the spread of the disease (e.g., pulmonary tuberculosis), avoid unnecessary testing, and be more precise in their treatment recommendations.

Generative AI can also uncover novel mutations, predict antimicrobial resistance, and identify infectious clusters and outbreaks within healthcare systems or the community. Furthermore, key antigens can be targeted in novel diseases, potentially speeding up the development of new vaccines and improving their efficacy.

Cognitive AI

Cognitive AI can augment personalized infectious disease treatments and prove vital in the prevention or monitoring of infectious disease outbreaks by predicting disease spread and enabling a more rapid, efficient, and effective response. By leveraging chatbots and virtual assistants powered by cognitive AI, we could have an optimized informed consent process, improved patient adherence to treatment, side effects prevention, and ultimately better outcomes. Cognitive AI could also provide real-time clinical decision support, thus reducing the risk of errors and improving patient safety.

Adaptive AI

Adaptive AI could be extremely helpful in augmenting current infectious diseases and public health by employing a sequence-based data analysis instead of conducting its gathering and processing simultaneously. Doing so allows learning from new experiences, while still working on previous predictions—all at a much faster pace due to the ability to receive large amounts of real-time data-based feedback. However, the potential impact is exponentially increased when deployed with cognitive AI or generative AI as part of state-of-the-art digital health platforms.

Edge AI

By deploying AI algorithms and models directly on edge devices, edge AI facilitates real-time infectious disease data analysis and decision-making. Edge AI enables early detection of novel pathogens, prompts intervention, and empowers public health professionals to act faster. It integrates diverse healthcare data sources for valuable clinical and administrative insights, while ensuring robust privacy and data security by processing data locally. During pandemics or other public health crisis, edge AI enables real-time symptom monitoring, contact tracing, and risk assessment on personal devices, alleviating the burden on healthcare systems. It functions reliably even during network disruptions. Edge AI enhances contact tracing efforts, identifies transmission chains, and supports predictive modeling for disease spread, aiding public health decision-making (12).

Computer vision and robotics

Robotics can automate routine tasks, improve infection control measures, and assist in patient care, reducing the risk of disease transmission. The world’s lack of preparedness for the COVID-19 pandemic has led to a need for a long and arduous recovery process. To address future outbreaks, Gao et al. suggest the potential use of robots (13), the fundamental requirements for robotics in infectious disease management, and highlight how robotic technologies can be employed in various scenarios, including disease prevention, clinical care, laboratory automation, logistics, and socioeconomic activities. It also addresses challenges in developing advanced robots that are reliable, safe, and quickly deployable. Ethical considerations are raised, emphasizing the need for sustained global efforts to ensure robots are ready for future pandemics.

CONVERGING TECHNOLOGY TRENDS RESHAPING THE ID ECOSYSTEM

Smart health enables increased data exchanges via internet of things (IoT), allowing for real-time strategic intelligence that can enhance the response to ID crisis. Digital health technologies, such as telehealth, mobile health applications, and wearable devices, have revolutionized healthcare delivery and have specific applications in infectious diseases. Telehealth enables virtual sessions at home or in specialized healthcare facilities, such as hospitals or nursing homes, remote monitoring, improving access to ID care, particularly in underserved areas. Mobile health applications can facilitate ID symptom monitoring, contact tracing, and adherence to treatment regimens. Wearable devices equipped with biosensors can provide real-time data on vital signs, enabling early detection and timely intervention for ID symptoms (14).

Numerous converging emerging technologies are deployed in combination with the full AI portfolio (Fig. 2) and are reshaping the infectious diseases ecosystem, such as next-generation sequencing (NGS), digital twins, federated learning (15), DNA computing, multi-purpose high-performance multi-omics computation platforms, computational biology (16), and pharmacology for infectious diseases.

Fig 2.

Converging, emerging and frontier technologies and potential impact on the infectious diseases ecosystem.

The symbiotic application of AI with NGS, digital twins, federated learning, DNA computing, multi-purpose high-performance multi-omics computation platforms, and computational biology and pharmacology has ushered in a new era of infectious disease and public health research. These technologies, in unison with AI’s capabilities, provide precise and personalized approaches in diagnosis, treatment, and prevention, fostering improved global health outcomes.

NGS

NGS has revolutionized DNA and RNA sequencing, empowering researchers to swiftly and cost-effectively study genomics, genetic variation, and gene expression. By pairing NGS with AI, infectious agents can be identified, and their mutations monitored, enabling timely responses and targeted therapies or vaccines.

Digital twins

Digital twins, as virtual representations of physical systems, simulate the spread of pathogens and predict outbreaks, allowing for the evaluation of various interventions. With AI, digital twins assimilate diverse data, including NGS information, and enhance prediction accuracy, optimizing resource allocation and public health decisions.

Federated learning

It facilitates collaborative machine learning without compromising data privacy. In the context of infectious diseases, it enables global-scale analysis of pooled data from healthcare institutions and research centers. By combining federated learning with NGS data, robust models identify disease transmission patterns and predict outcomes, fostering international cooperation against infectious diseases.

DNA computing

DNA computing harnesses DNA molecules for computations, expediting pathogen identification, drug discovery, and vaccine design. AI streamlines DNA-based algorithms and NGS data analysis, accelerating drug development and personalized medicine.

Multi-purpose high-performance multi-omics computation platforms

They integrate diverse omics data to probe the molecular mechanisms of infectious diseases and host interactions. AI-driven analysis of these platforms identifies biomarkers, drug targets, and personalized treatment options.

Computational biology and pharmacology

Computational biology and pharmacology employ computer-based modeling to understand pathogen behavior, host responses, and drug interactions. AI’s integration enhances these approaches, predicting drug efficacy and interactions, and revolutionizing drug discovery and treatment optimization for infectious diseases.

A few recent articles highlight the potential of these emerging technologies in addressing critical challenges in infectious diseases. Alrashed et al. discuss the potential of digital twins in managing the COVID-19 outbreak (17). They explore its applications in healthcare, resource optimization, and decision-making processes. Rasubala and Hendra (18) emphasize its roles in public healthcare, improving patient care, disease monitoring, and resource allocation. Sun et al. provided an overview of recent healthcare updates and challenges related to digital twins (19), highlighting applications in personalized medicine, disease prediction, and treatment optimization. The authors highlight the need for data privacy and security in digital twin systems. Chrisman et al. (20) and a subsequent editorial by Simner et al. (21) shed light on the human “contaminome” concept and its role in understanding computational analysis, genome sequencing, and infectious diseases. The authors describe how every genome sequencing project captures different DNA from different life forms (microbes and human hosts); in some cases, false associations between infectious diseases and, for example, gender could be made if we are not aware of the possible computational contamination. Other authors have examined the scope of computational biology in combating antimicrobial resistance (22), emphasizing its applications in understanding resistance mechanisms and designing treatment strategies, computational approaches in COVID-19 vaccine development (23), and discussing ML and bioinformatics tools in antigen selection and immunogenicity prediction. Some authors (15) propose a federated learning-based approach using blockchain for respiratory disease classification, ensuring collaborative research while maintaining data privacy. Dayan et al. present a federated learning framework for detecting COVID-19 infection from multi-site tomography scans (24). Their approach demonstrates accurate and robust segmentation results.

Using multi-omics, Kovarik et al. characterize the immune signature associated with long COVID-19 syndrome (25). They identify immune-related biomarkers that could aid in diagnosis and treatment. A recent publication (26) highlights the development of an automated DNA computing-based platform for the rapid and accurate diagnosis of acute respiratory illnesses etiology; the authors express that traditional methods need shorter turnaround times and have high costs, the new platform uses mRNA expression patterns in peripheral blood to discriminate between bacterial and viral causes. It achieves a diagnostic accuracy of 87% within 4 h without requiring computer or laboratory technicians. The integrated platform holds promise for accurate, rapid, low-cost, and automated diagnosis of disease etiology in emergency departments or point-of-care clinics.

IMPACT OF EMERGING TECHNOLOGIES ON ID ECOSYSTEM

The combined deployment of various technologies (Fig. 2), such as 5G high-speed mobile networks, IoT, blockchain, nanotechnology, bio-implants, and biometrics (27), holds immense potential in the fight against infectious diseases and reshaping precision public health. Nanotechnology-powered platforms can deliver targeted therapies (28), enhance diagnostics, and provide innovative tools for pathogen detection (29). IoT devices can monitor environmental conditions, predict disease outbreaks, and enable real-time surveillance. Blockchain technology can secure medical records, enhance supply chain management for vaccines and medications, and facilitate data sharing for research collaborations (15, 30).

LATEST RESEARCH

A few recently published articles illustrate the complexity of academic research efforts highlighting deep-tech-enabled ID solutions such as the impact of global change on infectious diseases and highlight the need for comprehensive strategies to combat emerging threats (31), the utilization of COVID-19 pandemic technologies for future infectious disease testing (32), and highlight their potential to drive innovations in diagnostics (33). AI technology in the fight against COVID-19 has seen applications in diagnostics, forecasting, and treatment. Multi-omics strategies for personalized and predictive medicine have potential in infectious diseases management (34).

Mbunge et al. systematically reviewed virtual healthcare services and digital health technologies deployed during the COVID-19 pandemic in South Africa, discussing their benefits and challenges (35). Other authors have published an implementation roadmap for establishing remote infectious disease specialist support and antibiotic stewardship in resource-limited settings (36), and provide an overview of telehealth use during the COVID-19 pandemic, discussing its advantages, challenges, and opportunities (37). Investigators have conducted a rapid review of machine-learning approaches for telemedicine in the context of COVID-19, highlighting their potential in remote diagnostics and patient monitoring (38) or discuss using AI techniques to predict infectious diseases, highlighting challenges and research opportunities in this field. Sood et al. (39) used CiteSpace to analyze information and communication technology in emerging infectious diseases, highlighting key trends and research gaps, while Liu et al. (40) explore the potential of big data analytics in urban epidemiology control, emphasizing the need for comprehensive strategies to enhance resilience against infectious diseases.

FUTURE DIRECTIONS: PRECISION INFECTIOUS DISEASES AND PRECISION PUBLIC HEALTH

The future of precision infectious diseases lies in integrating frontier technologies that, to date, have not yet made the transition from the innovation and research domain to clinical implementation; for example, Web 3.0, neuromorphic, exascale, or quantum computing, multi-omics-enabled human-computer interfaces and precision health platforms that leverage a combination of multiple frontier technologies. Web 3.0, characterized by decentralized networks and increased user control, can facilitate secure and privacy-preserving sharing of precision infectious disease data sets, enabling collaborations and optimizing global health outcomes. Inspired by the human brain’s architecture, neuromorphic computing could enable faster and energy-efficient processing of complex infectious disease data, while human-computer interfaces would provide real-time intelligence (Human Brain Project). Exascale and quantum computing’s superior capabilities would likely revolutionize precision infectious disease predictive modeling, exponentially increase precision drug discovery, and optimize precision treatment regimens by performing complex computations exponentially faster than classical computers. Multi-omics-enabled human-computer interfaces are another field that could enhance precision ID, for example, to identify patients at increased risk of infection enabling clinical risk stratification and individualized therapy (41). These technologies combined with ML will “propel the field of precision ID”.

CHALLENGES

The convergence of emerging technologies in the ID ecosystem offers promising solutions for preventing, managing, and identifying novel treatments to cure infectious diseases. However, we wish to emphasize that several challenges must be addressed to deploy these technologies effectively.

One of the key concerns is upholding ethical principles during deployment of emerging technologies. Using personal health data, genetic information, and sensitive patient information raises concerns about privacy, informed consent, and potential discrimination. Striking a balance between utilizing data for research and public health purposes while safeguarding individual rights and autonomy is essential.

Cybersecurity poses another significant challenge. With increased connectivity and data sharing, the risk of data breaches, hacking, and unauthorized access to health information is a pressing concern. Robust cybersecurity measures must be implemented to protect patient data and ensure the integrity of infectious disease management efforts.

Data ownership and patient self-sovereignty are central to the deployment of emerging technologies, emphasizing individuals’ rights to access, manage, and share their health data according to their preferences, with appropriate safeguards in place.

Other global challenges include high cost of computational equipment being a barrier to entry; “potential bias” due to algorithms being trained on biased data (intentionally or unintentionally); lack of trust; organizational and individual resistance to change; ongoing digital divide, as well as lacking health literacy and fluency. Clinical environments are not equivalent to predictable mechanical systems (42), and a different approach is needed where users’ perception is critical. Guckert et al. provide actionable recommendations and emphasize the critical need for a trust framework as part of the design methodology for developing and deploying AI in healthcare (43).

LIMITATIONS

While this invited commentary provides insights into the disruptive potential of emerging and frontier technologies in the infectious diseases’ ecosystem, it is vital to acknowledge our limitations. We wish to outline several notable limitations, such as covering only a few emerging technologies referenced in the published literature, as well as only a limited set of tools from the complex AI portfolio that can disrupt the ID ecosystem. While our manuscript primarily emphasizes the profound impact of artificial intelligence (AI) and other converging emerging technologies on epidemiology, public health, and global health, it is essential to note the influence of converging emerging and frontier technologies to various other areas within the infectious disease discipline. From virology to bacteriology, parasitology to mycology, and immunology to antimicrobial resistance, technology is revolutionizing on how we approach research, diagnosis, and treatment in each domain. Furthermore, the implementation of emerging technologies in the ID ecosystem is influenced by various contextual factors, including healthcare infrastructure, human behaviors, culture change and adoption of innovations, resource availability, and social determinants of health and regulatory frameworks which we have not covered in this manuscript.

CONCLUSION

This manuscript summarizes the present and future directions in this exciting field, as well as the amplified impact of leveraging deep-tech-enabled ID solutions on clinical outcomes. We also caution that the widespread implementation of ID-centric digital health and precision health solutions requires high-quality data sources, robust data governance, as well as the need for multi-disciplinary collaborations to develop customized interoperability standards. If implemented with the appropriate ethical and data governance guardrails in place, we have the potential to create a novel tech-enabled and human-led infectious diseases ecosystem that can reduce global morbidity and mortality.