AI in preventive healthcare: The next generation of diagnostic intelligence

• AI in preventive healthcare is facilitating a transformational shift from a reactive "sick care" model to a proactive care model focused on predicting, detecting, and preventing disease progression

• Clinical AI can reduce the burden on healthcare staff by automating tasks and supporting real-time decision-making

• Digital solutions are decentralizing care and improving health equity by moving advanced diagnostics into primary care and home settings

For much of modern medical history, healthcare has been organized around diagnosing and treating disease after symptoms emerge—a reactive model sometimes referred to as "sick care".¹ Our systems are designed to react: we wait for the cough, the lump, or the chest pain before the machinery of modern medicine goes into gear. But this reactive posture has created systemic challenges for building a sustainable workforce and managing healthcare costs.² We are currently facing what has been described as a "ticking time bomb" of staff shortages, with the World Health Organization estimating a global deficit of 15 million healthcare workers by 2030.³

The human cost of this delay is even more profound. Catching colorectal cancer at stage one or two, rather than after symptoms manifest, often means the difference between life and death.⁴ To help the system, and ourselves, we require a fundamental paradigm shift: moving from a care model that merely treats disease to one that provides prediction, early detection, and prevention. This transition is already happening. It is being powered by a new architecture of AI in preventive healthcare, fueled by digital diagnostic solutions that not only bring groundbreaking technology to the early detection and prevention of disease, but also pave the way for testing to move from the specialist’s office to the primary care clinic and to the home. In this way, the rise of AI in preventive healthcare also brings us one step closer to health equity, ensuring that a patient’s postal code doesn’t have to determine their life expectancy.⁴

The bridge between reactive and proactive care is being built with specialized, clinically validated medical algorithms. These task-specific tools are designed to predict high-stakes clinical events and disease progression, such as sepsis onset, heart-failure decompensation, kidney-function deterioration, and cancer progression. Because these models have clear input and output parameters, they provide the transparency and reproducibility that clinicians require to build trust^.5,6

At Geisinger Health System, machine learning models have been used to identify high-risk patients due for colonoscopy, increasing screening completion rates by 30 percent and enabling earlier detection of colorectal cancer at more treatable stages.⁷

We are also entering the next era of clinical AI, one that leverages the power of generative foundation models. While specialized algorithms are "narrow" and optimized for one specific task, new foundational models are learning the "grammar of health" by analyzing millions of patient records. These research models can simulate "future health timelines," predicting the onset of over a thousand different diseases up to 20 years in advance.⁸

When it comes to implementing AI in preventive healthcare, one of the most striking benefits is its potential to relieve an overburdened workforce. Staff shortages lead to a vicious cycle: remaining workers face heavier workloads, often leading to burnout and low job satisfaction, which causes more staff to quit.⁹ While digitalization in healthcare has often been associated with the increased administrative burden that comes with compliance requirements for electronic health records (EHRs), digitalization is now entering a new phase. The healthcare technology landscape includes medical AI algorithms, generative AI technologies (e.g. ambient clinical documentation), conversational medical assistants, and agentic AI solutions that can automate routine tasks and orchestrate workflows across care teams. Rather than simply digitalizing documentation, these tools can truly reduce cognitive load, streamline operations, and support clinical decision-making in real-time. In 2024, 71% of US hospitals reported using predictive AI integrated with EHRs, up from 66% in 2023.¹⁰ With the wider adoption of clinical algorithms driving prevention and earlier diagnosis, fewer hospitalizations and shorter hospital stays will ease the clinical workload and free up time for higher-value patient care.¹¹ Moreover, under value-based contracts, hospitals benefit when avoided admissions and shorter stays translate into a lower total cost of care and improved quality metrics, rather than simply more volume.¹²

Consider the management of sepsis with AI, an area that has evolved tremendously over the past decade. Sepsis claims more lives annually than all cancers combined, yet often presents with vague, non-specific symptoms that can evade clinical recognition until it is too late.¹³ Here, the “collaborative intelligence” of AI offers a vital lifeline to the clinician, continuously integrating vitals, laboratory trends, clinical notes, medications, and patient history into a real-time risk picture that no human could produce alone. By identifying high-risk patterns hours before standard blood cultures return results, these algorithms allow teams to administer antibiotics within the "golden hour," preventing the catastrophic organ failure that necessitates prolonged, labor-intensive ICU stays.^14,15 Such technologies do not only save lives, they fundamentally alter clinical workflows, transforming a potentially frantic, emergency situation into a streamlined, predictive intervention that leverages the aggregated experience of thousands of cases to support a single, confident decision.¹⁶

As healthcare shifts toward earlier detection and intervention, pathology is another area where AI-enabled clinical decision support is becoming essential to address modern healthcare challenges. With a global workforce of a mere 100,000 pathologists tasked with analyzing 20 million new cancer cases every year, the traditional model of manually reviewing tissue samples has become unsustainable.^17,18 Breakthroughs in digital pathology are stepping in not to replace these specialists, but to augment them as high-speed collaborators capable of reducing the time required to detect metastatic deposits by up to 90 percent and boosting sensitivity to nearly 100 percent.¹⁹ Recent innovations in this field even include companion diagnostics that leverage AI to analyze tissue samples and provide personalized recommendations of the correct targeted therapies.²⁰

AI-enabled technologies are also accelerating a fundamental change in healthcare delivery toward decentralized settings. Traditionally, advanced diagnostics and precision medicine have been the province of large, urban medical centers. This creates a geographic barrier to care that disproportionately affects rural and underserved populations.⁴ The shift toward "health care" requires moving advanced diagnostics into primary care and community settings, or even into the home.

Multi-cancer early detection (MCED) testing provides a concrete example of how AI is enabling this decentralization. By applying machine-learning models to blood-based signals of circulating tumor DNA and other biomarkers, MCED tests can identify multiple cancers at earlier stages, often before clinical symptoms emerge, within primary care and community settings.²¹ This shifts advanced oncological insight out of academic medical and comprehensive cancer centers and into routine care, enabling earlier referral and intervention while reducing reliance on late-stage, resource-intensive treatment.

But, perhaps nowhere is the urgency of this transition more visible than in the management of diabetes, a condition currently facing a demographic explosion. The International Diabetes Federation projects that the number of people living with diabetes globally will surge from 590 million today to 853 million by 2050—a 46 percent increase that threatens the economic sustainability of healthcare systems.²² The impending crisis of cost stems from the catastrophic price of failure; in the United Kingdom, for instance, nearly 60 percent of diabetes spending is consumed by treating late-stage diabetic complications like heart failure and strokes.²³ Innovations in at-home testing, like AI-enabled Continuous Glucose Monitoring (CGM), can shift the focus from "rescue" to "anticipation."²⁴ Rather than simply alerting a patient that their blood sugar has already dropped, new algorithms can forecast glucose dynamics hours in advance, reducing the time patients spend in dangerous nocturnal hypoglycemia by 37 percent, and preventing trips to the emergency room.^25,26

The most profound impact for AI in preventive healthcare may be in advancing health equity. Today’s care models often disadvantage patients with limited resources who struggle to navigate fragmented specialty networks and face long delays before receiving care. AI-enabled digital solutions can democratize clinical expertise, extending specialist-level diagnostic accuracy to underserved and rural settings. By using predictive analytics and risk stratification to identify at-risk patient populations in these communities, providers can allocate resources where they are most needed, reducing traditional health disparities.⁴

Oak Street Health, a primary care provider for Medicare patients, demonstrated this by using a machine learning algorithm to predict hospital admissions and mortality. Their model was more accurate than clinicians alone in identifying high-risk patients, allowing the organization to allocate resources to those most in need.⁴ Further downstream, generative AI agents are emerging, so-called “virtual nurses” that can engage patients to support prevention and early detection. By increasing the frequency and continuity of patient touchpoints, virtual nurses can monitor symptoms, reinforce screening and follow-up, and escalate concerns early, particularly in communities with limited access to clinical staff.²⁷

We are at a critical juncture. The technology to move from reactive to proactive care—from "sick care" to "health care"—is no longer a distant prospect; it is here. By increasing AI literacy in the provider and primary care space, investing in validated algorithms, interoperable data systems, decentralized diagnostic technologies, and AI-enabling enterprise infrastructure, we can build a system that is not only more efficient and economically sustainable, but fundamentally better for people.

The call to action for healthcare leaders is clear: make digital solutions a priority, find the right partners, and ensure your organization is "AI-ready". Digital health is no longer optional; it is the foundation for proactive, equitable, and better care for all.