In 2019, COVID-19 spread across the globe before governments could evaluate the scale of the threat, in turn costing the global economy trillions of dollars, resulting in millions of lives lost, and exposing critical weaknesses in traditional disease surveillance systems. Today, a new generation of artificial intelligence-based startups is attempting to solve this problem. Venture capital firms have responded by investing into companies that promise more efficient pathogen detection and outbreak mapping — can AI generate returns while still improving global public health concerns?
Nationwide, the infectious disease startup ecosystem saw a massive surge after 2020. While funding peaked dramatically at $2.9 billion in Q1 of 2020 due to the COVID-19 pandemic, it has since normalized back to historic baselines as investor focus shifted toward oncology and AI-driven platforms.
Historically, the infectious disease startup ecosystem in the Midcontinent region (Oklahoma, Kansas, Missouri, Texas, Colorado, New Mexico) has been sparse, with the ecosystem being more popular on both coasts. The Midcontinent has less specialized local medical resources as compared to coastal hubs like Boston, San Francisco and San Diego; furthermore, the Midcontinent receives only 3% of funding, while having 20% of the population. Infectious disease and biotech companies require a substantial amount of funding to generate a product, as well as requiring enough capital to get past the hurdle of the clinical trial stages, causing many of these companies to fail. However, the presence of the ecosystem in the Midcontinent is growing as novel diseases emerge, reflecting the growing need for infectious disease research nationwide.
Over the past few years, the agentic AI market wave has been steadily rising in popularity — from December 2025 to May 2026, general AI usage in businesses hovered between 17% and 20%, with these numbers expected to increase by around 2–3% in the next six months. With new platforms such as Claude Science, we expect AI use to increase rapidly in the coming months.
In the scientific field in particular, AI usage is much higher than the overall average, hovering around 40%. Agentic AI’s use case in infectious disease is evolving around three primary fronts: as an “early warning” system for pandemics, as a pathogen detector, and as a method to develop novel antibiotics.
First Front: AI as a Pathogen Detector
Traditionally, infectious disease diagnosis is slow, with the process being arduous; tests require cultures and lab visits, both of which are extremely time consuming. While lab turnaround times are at least 48 hours, the amount of time increases greatly as the data is being processed, lasting up to even weeks. In urgent cases, the amount of time is still between 12–24 hours. Cultures take several days for bacteria to develop prior to analysis. For a critically ill patient, every hour matters — especially if they have a fast-moving disease. Startups, with the assistance of AI, combine large biological databases with machine learning to help narrow down the possibility of pathogens and speed up the traditional processes.
Case Studies:
Karius — A late Series C/D startup based in California with a Mayo Clinic partnership
Karius utilizes blood samples to identify pathogens from a blood sample; through detecting microbial cell-free DNA, the test provides an accurate insight into pathogen detections via matching the DNA to a large biological database that only gets larger as more data gets added to the system.
Pattern Bioscience — A Series D startup based in Texas
Microbiology workflows often require physicians to wait several days to obtain culture results, before determining a pathogenic cause and a viable treatment outcome. During this waiting period, patients are treated with broad-spectrum antibiotics, resulting in a growing antimicrobial resistance. Pattern Bioscience seeks to remove this waiting period via automating the culture process through looking at DNA; instead of waiting for a growth pattern, the technology analyses biological signals, allowing physicians to obtain information about a pathogen sooner and determine a viable treatment plan.
Second Front: AI as an “Early Warning” System
Hospital admissions, flight records, and news reports are all used to determine when and how a disease may spread across people. This vast array of data can be easily combed through by AI (namely through certain machine learning models) to indicate an emerging pandemic, giving governments and people ample time to prepare a response for a sickness.
Case Studies:
Kinsa Health — Merged company headquartered in California
Kinsa is a predictive AI-based company; the AI predicts where an infectious disease is starting, and where the disease will continue to spread. Through this, it forecasts the impact that a disease may have on hospitals — getting an early warning allows hospitals to prepare their resources ahead of time.
BlueDot — Series A company based in Canada
BlueDot rose to prominence by identifying unusual pneumonia reports prior to COVID-19. They utilize AI to scan through data points and provide outbreak intelligence.
For investors: Early warning systems have the potential for monumental societal value; even a small detection could save governments billions of dollars in resources. However, customers are often governments, which greatly limits their market; the value of AI-driven prevention tools is much harder to quantify.
Third Front: AI-designed therapeutics
New antibiotics are the least prevalent in the infectious disease pipeline. Antimicrobial resistance causes millions of deaths annually worldwide and will eventually become a leading cause of death worldwide, but large pharmaceutical companies continue to turn away from antibiotic R&D because of poor economics. AI is beginning to change discovery in this field.
Case Study:
Phare Bio — Boston, MA social venture
From MIT’s Collins Lab, Phare Bio’s generative-AI platform designs new antibiotics, customized for drug-like properties and reduced toxicity. Phare Bio is now part of Google’s Generative AI Accelerator.
For investors: The scientific promise in this field is unprecedented, but antibiotic therapeutic companies face extremely hard business models: long timelines, and high trial costs. As a result, investors are typically wary to invest in this field, being that it has a great amount of risk. So, to counteract this, non-traditional forms of investment (e.g. non-dilutive capital, social ventures) typically absorb a lot of the early danger within this field.
Conclusion
These three fronts combined could significantly reduce the time between the emergence of a pathogen and the creation of a societal response. However, while AI does have inherent benefits, significant regulatory and business challenges may hurt investor returns:
AI models often struggle outside of laboratory experiments: in the lab, models rely on previous historical medical data, and produce results based on that. However, the hospital environment is rapidly changing, and AI models rapidly lose accuracy when deployed in a real-world situation.
The “Black Box”: AI works in a system of input and outputs. Many machine learning systems generate a prediction without explaining their reasoning — since doctors can’t determine their reasoning, it makes it difficult for physicians to fully rely on AI’s determinations.
Racial Bias: since AI is rooted in previous medical data, it has the possibility to include biases against marginalized groups — for example, dermatology AI models trained on white models frequently miss diagnoses on darker-skinned groups — which, in turn, raises concern about the efficacy of AI diagnoses.
Compliance Barriers: when AI makes a mistake, does it lie with the developer, healthcare professional, or the hospital? This question makes it difficult to fully utilize AI, being that it’s difficult to determine responsibility for medical errors.
Whether these AI technologies ultimately can fulfill their promise remains uncertain. However, the cost of delayed detection is extremely high. For both founders and investors alike, the race to identify the next outbreak may be an extremely important challenge.
About Cortado Ventures
Cortado Ventures is an early-stage venture capital firm that invests in ambitious, growth-driven companies, backing a new generation of economic prosperity in the Midcontinent region. Cortado’s focus is on tech companies bringing innovative solutions to critical industries across space, energy, logistics, healthcare, and the future of work. For more information, visit cortado.ventures.