Bessemer Venture Partners recently released its State of Health AI 2026 report, analyzing the development trends of AI in healthcare this year.
Bessemer Venture Partners (BVP) is one of the world's most prestigious and longest-standing venture capital firms, originating from the family office of the co-founders of Carnegie Steel in 1911.
The report explores the unique prospects of "Health Tech 2.0", presents seven key predictions, and offers actionable recommendations for entrepreneurs.
Prediction 1: As healthcare providers gain a competitive edge in AI applications, payers will face mounting pressure, triggering a nationwide wave of administrative systems embracing AI.
Over the past 18 to 24 months, healthcare providers (such as hospitals and clinics) have actively integrated AI into their administrative workflows, particularly in the field of Revenue Cycle Management.
They leverage AI to enhance revenue recovery efficiency through more precise coding and medical record documentation, standardized claims submission, and streamlined appeal processes.
This trend presents new challenges for payers (e.g., health insurance companies and commercial insurers): the rise in healthcare spending is not driven by fraud or overuse of services, but rather by providers becoming increasingly adept at securing reasonable reimbursements that should rightfully be theirs.
When AI can identify missed diagnoses, suggest improvements in medical records, and optimize appeal strategies, the compensation amounts received by hospitals increase accordingly—initially denied claims decrease, while successfully overturned denial cases significantly increase.
What are the results? Payments are under pressure from many sides:
Rising administrative costs: While providers submit higher-quality claims, the process has become more complex and requires more meticulous review, resulting in a dual increase in both claim processing volume and customer service inquiries.
The medical claims ratio keeps rising: As providers enhance their revenue capture capacity, payers' compensation expenditures naturally increase.
Fierce competition intensifies: As some early adopters of AI-powered payment solutions have achieved profit margin improvements, this has compelled other industry players to accelerate their adoption.
2026 will mark a pivotal turning point. Payment providers will accelerate the comprehensive deployment of AI in administrative operations and increasingly turn to external partnerships to catch up with the efficiency gains achieved by service providers over the past year.
Meanwhile, they must also address another challenge: clearly demonstrating the actual return on investment (ROI) of AI investments while ensuring patient access to healthcare and meeting increasingly stringent regulatory requirements.
For healthcare entrepreneurs developing solutions for payers, the following three key areas should be prioritized:
1. Payment Integrity: This includes compliance verification of claims documentation, automated review, appeal management, fraud detection, waste identification, and reasonable review of medical service utilization.
2. Prior Authorization & Concurrent Utilization Review: The focus is on accelerating the diagnostic and therapeutic approval process based on clinically validated guidelines. Coupled with the new regulatory incentives introduced by the U.S. CMS and clear ROI, this scenario has become a high-priority use case.
3. Member Engagement: Includes insurance plan and healthcare service navigation, nursing gap analysis, and precise risk identification (e.g., social determinants of health (SDOH), comorbidities, etc.).
For entrepreneurs, this means capitalizing on the payment processors 'strong interest in partnering with' AI-native' companies, and targeting a niche market segment that offers both high ROI and technical complexity.
Prediction 2: Clinical AI applications will accelerate their emergence, primarily focusing on triage and risk assessment, with physicians remaining the core decision-makers.
While administrative AI (Admin AI) has achieved widespread adoption, clinical AI (Clinical AI) that directly engages in patient diagnosis and treatment has made relatively slow progress.
The underlying reasons include regulatory complexity (e.g., AI-assisted diagnosis requires FDA approval), liability risks, and the absence of a clear payment mechanism for clinical AI value under the traditional 'fee-for-service' payment model—the current system tends to incentivize physicians' time spent with patients rather than their decision-making efficiency or accuracy.
These barriers are real and will not disappear overnight. However, early breakthroughs in clinical AI have been observed: solutions that strictly adhere to existing regulatory and payment frameworks, while consistently placing physicians at the core of decision-making, are gradually being implemented.
We predict that clinical AI will achieve large-scale application in triage and risk assessment in the future—not by replacing physicians for autonomous diagnosis, but by integrating messy and fragmented multi-source data to construct complete and quantifiable patient profiles for physicians, thereby reshaping clinical workflows.
This will result in: fewer misdiagnoses, faster identification of subtle patterns in multimodal data, and clearer determination of which patients urgently require intervention and which treatment options are truly effective.
Specific application scenarios include:
Pre-visit risk stratification: AI automatically analyzes patients' electronic health records (EHR), claims data, test results, and social determinants of health (SDOH) before consultation, flags high-risk signals, recommends relevant screening questions, and identifies care gaps. Physicians only need to review the comprehensive summary generated by AI in a very short time, without manually sifting through lengthy medical records.
Hospitalized Patient Deterioration Alert: AI continuously monitors all hospitalized patients to predict the risk of disease progression, complications, or other adverse events. Compared to relying on nurses to manually detect subtle changes, AI enables round-the-clock dynamic monitoring and proactively issues alerts to the medical team.
Triage Optimization: AI assists physicians in prioritizing the most urgent cases based on patients' chief complaints, vital signs, and medical history. While the final decision remains with the physician, AI can identify subtle patterns that may be overlooked by humans during high-intensity, high-load work.
Specialty Referral Matching and Consultation Support: AI analyzes patient conditions, integrating case complexity, specialist expertise, and individual patient characteristics to intelligently recommend the most suitable specialty resources. Additionally, AI assists specialists in managing high-risk consultations and provides evidence-based clinical guidelines and automated support for low-complexity consultations.
For entrepreneurs, this means:
For clinical AI to achieve autonomous diagnosis and treatment decision-making, the greatest obstacle is not technology, but a sustainable business model.
It is recommended to adopt a "small steps, fast run, gradual progress" strategy—starting with low-risk scenarios and gradually advancing to regulatory approvals, while simultaneously exploring feasible payment pathways at the earliest opportunity.
From day one, deeply integrate clinicians' feedback into the product design, embedding it rather than circumventing existing clinical workflows, and allocate substantial resources to algorithmic evaluation and bias testing.
An ideal starting point is to begin with front-end administrative AI scenarios (such as appointment scheduling and pre-consultation), gradually extending to core clinical processes including diagnostic assistance, triage support, clinical decision-making, risk assessment, and care coordination.
Prediction 3: CMS will attempt to establish a dedicated medical insurance payment code for clinical AI.
While regulatory progress is underway and AI may eventually be permitted to fully handle certain clinical tasks (such as prescribing), the primary barrier to large-scale clinical AI adoption lies not in the technology itself, but in the payment model.
Currently, healthcare institutions primarily derive revenue from clinical procedures, outpatient visits, and face-to-face patient interactions—whereas AI-generated diagnoses, continuous monitoring, or preventive interventions are not covered by reimbursement.
This creates a paradox: AI can identify high-risk patients requiring preventive care, but if such preventive services are not covered by health insurance or commercial insurance, healthcare institutions have no financial incentive to implement AI recommendations.
For instance, when AI automatically interacts with patients in the background to provide preliminary diagnoses or symptom monitoring, physicians do not take the time to participate, thereby failing to meet current billing standards.
The results-based value care (RBCV) model can address this issue by paying for health outcomes rather than the volume of services provided. However, currently only 30%–40% of the U.S. healthcare system operates under value-based care contracts.
We anticipate that by 2026, the Centers for Medicare & Medicaid Services (CMS) will launch a series of pilot programs for new CPT codes and payment models specifically designed for 'AI-Prioritized Care'.
Why CMS and its Innovation Center? There are three reasons:
Scale effect: CMS covers over 140 million Americans through Medicare, Medicaid, and CHIP. Once CMS introduces new payment codes or models, commercial insurance companies typically follow within 12–24 months.
Regulatory authority: CMS can rapidly test new payment models through its Innovation Center (CMMI) without congressional approval, demonstrating strong policy experimentation capabilities.
Underlying drivers: CMS faces dual pressures to control cost growth and improve healthcare quality, where AI offers a solution that addresses both objectives. Enhanced prevention can reduce costly acute treatments, earlier interventions lower hospitalization rates, and more efficient care coordination improves outcomes while cutting costs.
We do not anticipate that CMS will establish a unified 'AI reimbursement code,' but will instead roll out a series of targeted pilot programs and demonstration initiatives.
Why is this important? What can it bring?
If CMS can successfully demonstrate that AI-powered healthcare not only improves health outcomes and reduces costs, but also establishes a payment mechanism that enables medical institutions to benefit from AI insights, commercial insurers will swiftly adopt similar CPT codes.
The inspiration for entrepreneurs:
The evolving regulatory framework, pilot programs, and existing payment structures should be regarded as strategic assets. In the healthcare sector, establishing a business model that enables clinical AI to generate sustainable profits is crucial—not only for product commercialization but also as an effective hedge against the persistent trend of rising healthcare costs.
Prediction 4: Self-paying users will accelerate the adoption of clinical AI far faster than the implementation of any medical insurance reimbursement codes.
While CMS is piloting AI-powered payment coding and payers remain locked in debates over reimbursement frameworks, consumers are no longer willing to wait—they're starting to pay out of pocket, forcing the entire healthcare system to adapt.
Over the past few years, consumer health has experienced a resurgence, driven by the convergence of three key forces:
The widespread dissatisfaction with the complex procedures and accessibility barriers in traditional healthcare systems (e.g., Hims & Hers achieved explosive growth through asynchronous diagnosis and treatment);
The public's interest in preventive health and technology-driven health insights is growing (Function Health, for example, has achieved over $100 million in annual recurring revenue in less than two years);
AI has become deeply embedded in daily life. OpenAI data reveals that before the launch of ChatGPT Health in January 2026, over 40 million people were already using ChatGPT daily, with one-fifth of users asking health-related questions at least once a week. People have long relied on AI for health advice and are now willing to pay for AI services that enhance their healthcare experience.
A study by RadNet involving 10 medical institutions and 747,604 women revealed that 36% of the participants were willing to pay an additional $40 out-of-pocket for AI-enhanced mammography screening. The results validated their intuition—groups utilizing AI-enhanced screening demonstrated a 43% higher overall cancer detection rate, with 21% of this improvement directly attributable to AI analysis.
Women participating in the program experienced a 21% increase in cancer detection rates and a 15% rise in positive predictive value (PPV), indicating that each recall test was more likely to confirm true cancer cases.
This willingness to self-pay is giving rise to a new market for 'AI-first' healthcare services, which is not constrained by traditional reimbursement limitations. When consumers pay directly, enterprises can deploy clinical AI solutions without waiting for the clarification of CPT codes, payer contracts, or reimbursement policies.
Typical scenarios in practice include:
Primary and Emergency Care: The AI Priority platform provides diagnostic and treatment plans for common conditions such as urinary tract infections, dermatological disorders, and respiratory infections, with oversight and review by human physicians. Consumers pay per consultation to gain faster access to medical consultations and comprehensive analysis of symptoms, medical history, and risk factors by AI.
Second Opinion in Specialized Diagnosis: The most direct example is AI-powered imaging interpretation services for consumers—patients can upload complex imaging such as CT and MRI to obtain AI-enhanced second opinions in oncology, cardiology, orthopedics, or ophthalmology. For major treatment decisions, patients are willing to pay to have their images compared with millions of similar cases by AI to identify subtle patterns that might be missed. RadNet research shows that AI-assisted diagnosis improves radiologists' accuracy from 84%–89% to approximately 93%.
Health AI Coach: Users upload diagnostic reports, synchronize wearable device data, and track health metrics, enabling AI to monitor symptoms, recommend preventive measures, and issue risk alerts. The subscription-based AI health coach has become comparable to human services at a lower cost. With industry giants like ChatGPT Health and Anthropic's HealthEX entering the market with massive user bases, new players may focus their differentiated advantages on niche areas such as chronic disease management and speech therapy. More importantly, the key will be who can provide exceptional customer service and user engagement, deeply integrate with medical infrastructure, and establish unique distribution channels.
Why is this important? What will it unlock?
The self-paid healthcare market is laying the foundation for the feasibility of "AI doctors" in the next decade. We have not yet reached the endpoint—regulatory frameworks, liability determination mechanisms, and trust barriers remain substantial. However, consumers' proactive adoption is addressing the most fundamental challenge: demonstrating the economic value of AI-driven care and ensuring patient willingness to pay.
The inspiration for entrepreneurs:
Unlike waiting for the healthcare system to establish payment models, the consumer-grade health AI market provides a faster pathway to achieve revenue and product market fit (PMF).
Focus on scenarios where AI can significantly improve outcomes—higher diagnostic accuracy, earlier disease detection, and faster diagnosis and treatment—while consumers can intuitively perceive its value.
Companies that pioneer the consumer-centric AI-first care model will secure the most competitive edge when payers and healthcare providers are ready.
Prediction 5: The burgeoning healthcare AI data infrastructure sector is gaining momentum, but can it truly unlock value and achieve sustainable growth in the medical industry?
For decades, venture capitalists have faced repeated setbacks in healthcare infrastructure, learning a hard lesson: value tends to flow to the application layer rather than the infrastructure layer.
Why? Because healthcare infrastructure companies face structural challenges:
The buyer base is extremely limited: there are only a few thousand healthcare institutions, hundreds of payers (insurance companies), and hundreds of pharmaceutical companies across the United States. Among these, those with in-house development teams capable of effectively leveraging data and infrastructure tools are exceedingly rare. In contrast, general-purpose SaaS solutions (such as CRM and HR systems) serve millions of enterprise clients.
The contract amounts are generally small: healthcare institutions already face tight IT budgets. Even large-scale medical systems struggle to approve contracts exceeding $500,000 for a single set of infrastructure software—after all, even basic application software is in short supply.
Under pressure from general-purpose infrastructure giants: General-purpose data platforms such as Snowflake, AWS, and Databricks have already demonstrated strong capabilities in serving healthcare clients. Given that these platforms offer enhanced functionality, lower costs, and more rigorous validation, why would clients opt for 'medical-specific' infrastructure?
The results are clear: healthcare infrastructure companies have historically struggled to reach the scale expected by venture capitalists. While there are a few successful cases, the majority of these enterprises have annual recurring revenue (ARR) ranging from $20 million to $50 million—far from meeting the return requirements of venture capital funds.
But now the tide is turning: The AI era has sparked a new demand for specialized medical data and infrastructure, with the buyers being entirely different—AI model labs and AI application companies.
We anticipate that 2026 will witness significant investment growth in the field of medical AI infrastructure, driven by these two emerging demand-side factors. However, whether such infrastructure can truly support venture capital-level commercial returns remains uncertain at present.
The inspiration for entrepreneurs:
If you are building a medical AI infrastructure, focus on the following three key points:
Establish clear differentiation from general-purpose platforms: Why can't customers directly use Snowflake, Databricks, or AWS? What capabilities in your solution are truly' medical-specific'? You must clearly demonstrate why an infrastructure independent of general-purpose platforms is necessary.
Build a repeatable and sustainable revenue model: Avoid one-time data authorization transactions. Combine a usage-based billing mechanism with enterprise annual licenses to ensure stable and scalable recurring revenue.
Serve multiple stakeholders and expand potential markets: Your platform should deliver value to various roles within the healthcare ecosystem, avoiding being confined to a narrow buyer pool. Approach from intersecting scenarios—such as serving both healthcare institutions and biopharmaceutical companies in clinical trials.
The roadmap to the application layer: The most valuable infrastructure companies are those that can further capture application-layer benefits. Therefore, from day one, we should consider how to leverage infrastructure as a springboard to enter high-value application scenarios in the future, achieving a dual-wheel drive of 'infrastructure + application'.
Prediction 6: Value-Based Care (VBC) makes a strong comeback with AI.
Over the past decade, value-based care (VBC) has been the 'white whale' pursued by the healthcare industry—a vision that is highly appealing: shifting from payment by volume to payment for outcomes that 'improve patient health'; aligning incentives to reduce wasteful expenditures and enhance the quality of care.
Yet the reality is disappointing: most VBC models consistently fail to achieve sustainable economic models. What is the reason?
Highly dependent on human input: VBC requires extensive patient interactions—including care coordinator calls, chronic disease monitoring, medication adherence tracking, and social determinants of health (SDOH) support. Complete reliance on nurses and care coordinators for manual completion would incur exorbitant costs.
Meager profits under risk-bearing: VBC enterprises typically assume financial risks through capitation or shared savings models. However, given the high volatility and unpredictability of healthcare expenditures, the narrow profit margins mean that a severe influenza season or a high-cost patient could potentially erode annual profits.
The return cycle is lengthy: VBC typically takes 12–24 months to deliver results, requiring substantial upfront capital and testing investors' patience.
The challenge of attribution: When multiple physicians or institutions jointly participate in the care of a patient, who should be credited for the final health outcome? Attribution determination is complex and often controversial.
These challenges triggered a brutal shakeout in the VBC sector from 2022 to 2024: numerous companies collapsed, survivors drastically scaled back operations, and investors lost confidence in the field.
AI is fundamentally transforming the economic logic of VBC by enabling scalable patient interactions at minimal marginal costs.
In the past, a nurse or care coordinator managed a maximum of 50–75 patients; whereas in the AI-enabled VBC model, the same healthcare professional can cover 200–300 patients, with routine monitoring and interactions handled by AI, allowing human resources to focus on high-value, high-complexity interventions.
We anticipate that 2026 will witness the emergence of a new cohort of enterprises that inherently integrate AI and VBC concepts. Unlike traditional VBC companies that merely patchlessly overlay AI, these pioneers have been architecturally designed from day one to focus on 'AI-driven patient engagement.'
A wave of AI-first startups will emerge to explore this model.
This AI-powered revival of value-based healthcare may finally bring the vision of 'paying for health outcomes' into reality.
Prediction 7: The next-generation digital CRO will resolve the 'impossible triangle' in pharmaceutical R&D—cost, speed, and competitiveness.
Amid the rapid advancement of medical AI, drug development remains a 'slow lane.' Even when administrative processes are reduced from weeks to minutes, the time to market for a new drug still spans 10–15 years, with an average cost of up to $1 billion to $2 billion.
Traditional contract research organizations (CROs) remain highly labor-intensive operational models: thousands of scientists conduct physical experiments on animals and cells in laboratories, systematically advancing rigid, linear testing phases. To reduce costs, a significant portion of such work has been outsourced to China over the past two decades—now, more than 70% of Western drug development projects entrust preclinical research to China's CROs.
However, this paradigm is being disrupted. In April 2025, the U.S. FDA released a strategic roadmap to immediately reduce reliance on animal testing, initially targeting monoclonal antibody drugs, with plans to gradually expand over the next 3–5 years, ultimately achieving "animal testing as the exception, not the norm." The roadmap explicitly supports AI-driven computational models, organ-on-chip systems, and in silico toxicity prediction as alternative approaches.
The logic behind it is compelling:
More than 90% of the candidate drugs that are shown to be safe in animal studies are ultimately not approved because of lack of efficacy in humans or unexpected safety issues;
Animal experiments are not only time-consuming (often extending the R&D cycle by several years) but also costly—e.g., a non-human primate can cost up to $50,000, and a typical monoclonal antibody project typically requires over 100 animals.
In 2026, we will witness the rise of a cohort of 'AI-native' digital CROs (Digital CROs). By replacing physical experiments with virtual ones, they are expected to shorten the drug discovery cycle by several months or even years, while creating opportunities for the United States to reshape its domestic drug development capabilities and enhance global competitiveness.
What does a digital CRO look like in practice?
Digital CRO utilizes AI to simulate biological experiments that traditionally relied on physical laboratories, animals, and years of testing. Instead of synthesizing thousands of compounds for sequential screening, computational models are employed to predict interactions between millions of candidate molecules and target proteins, as well as their potential toxicity, before any actual molecular contact occurs.
Emerging patterns include:
AI-driven macromolecular drug design platform: Utilizing protein language models to predict protein structures and optimize therapeutic properties (e.g., binding affinity, immunogenicity risk, manufacturability) in a "dry lab," screening optimal candidates from millions of molecules before synthesis. This can reduce the time required for biotech companies to identify preclinical drug candidates by months or even years. Leading enterprises are accelerating the design cycle and improving the success rate of their clients' R&D pipelines by deploying cutting-edge AI models and platform capabilities.
Virtual laboratory platform: By computationally simulating cellular behavior, molecular interactions, and biological pathways, it validates hypotheses, identifies drug targets, and confirms mechanisms of action under thousands of experimental conditions—eliminating 18–24 months of trial-and-error without conducting a single wet lab experiment.
AI-driven robotic automation experimental platform: AI-scheduled high-throughput physical experiments achieve 24/7 unmanned operation, performing hundreds of experiments in parallel with superhuman precision. Serial experiments originally requiring months are compressed to weeks, while eliminating human errors and operational variability.
AI-assisted clinical trial design platform: Through computational modeling, it accurately identifies patient populations most likely to respond to therapies, enabling pharmaceutical companies to design smaller-scale, faster, and more successful clinical trials. Given the high failure rate of Phase II trials (up to 90%), precise patient stratification can significantly improve outcomes and reduce costs.
Real-World Data (RWD) Platform: Integrating clinical trial data with real-world evidence to predict the efficacy and safety of drugs in specific patient subgroups, enabling pharmaceutical companies to prioritize the most promising indications before committing substantial investments.
The inspiration for entrepreneurs:
The digital CRO sector offers two distinct pathways:
Full-stack AI biotechnology company: self-built computing platform and internal drug pipeline development. The advantages include rapid speed and strong control, but it requires substantial capital and still faces a lengthy R&D cycle.
Industry-level platform infrastructure company: Building general-purpose AI models and tools to serve the entire pharmaceutical ecosystem. The advantage lies in faster revenue generation and lower capital requirements, but it requires rigorous validation to demonstrate model reliability and overcome enterprise-level sales challenges.
The $70+ billion CRO market will ultimately be defined by companies that consistently deliver accurate forecasts and master regulatory compliance.
Disclaimer: This article is intended solely for knowledge exchange, sharing, and popular science purposes, and does not constitute commercial promotion, nor should it be regarded as medical guidance or medication advice. For copyright infringement, please contact us for removal.
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