2025 Healthcare and Life Sciences Predictions

The health tech sector demonstrated remarkable resilience in 2024. Even in a challenging macroeconomic environment, healthcare AI companies showed particular strength, with successful implementations across clinical, operational, and research domains fundamentally reshaping care delivery and management. This evolution has moved well beyond the initial excitement phase, with organizations now focused on scalable, enterprise-wide deployments that deliver measurable value. 

Looking ahead to 2025, we anticipate several forces converging to make this year an especially promising one for healthcare innovation. Advances in AI capabilities are coinciding with mounting pressure to address fundamental challenges in healthcare delivery and access. Meanwhile, therapeutic breakthroughs and novel care delivery models are creating opportunities for transformative change. The companies best positioned to succeed will be those that can harness these technological capabilities while building sustainable businesses that deliver clear value to patients, providers, and the healthcare system as a whole.

In this year’s healthcare and life sciences predictions, we share what we’re hearing and anticipating to shape the sector in the coming months. 

1. Trump’s support for ICHRAs may strengthen the Obamacare exchanges he previously tried to eliminate

Individual Coverage Health Reimbursement Arrangements (ICHRAs) were created under the first Trump administration, allowing employers to provide tax-free allowances for employees to purchase individual health insurance. Republicans, including Trump, view ICHRAs favorably because they are market-based and give employees more choice. 

The irony of bolstering ICHRAs is that such a policy will likely strengthen the Affordable Care Act (ACA) exchanges in ways that may surprise their original architects. As more employers offer ICHRAs, more employees will purchase individual market plans through ACA exchanges, increasing overall enrollment. Industry analysts project this shift could significantly expand the ACA marketplace, with some carriers predicting that up to 45% of the employer group market could be disrupted through ICHRA growth. 

Moreover, the influx of employees from group coverage to individual plans is expected to reduce the average age of ACA market participants, helping to stabilize the market via improved risk pool health and potentially lower premium costs.

2. Medicaid is likely to face scrutiny, but opportunity may lie ahead for more value-based care 

There is plenty of speculation of how a Republican-led government will change Medicaid, from spending cuts to work requirements. Surely many of these policies would weaken the Medicaid program, potentially leading to a decrease in the number of Americans who can access healthcare. Despite our concerns, we see a potential silver lining: acceleration of value-based care (VBC) adoption.  

Several factors support this view: 

• Republicans are likely to propose more Medicaid block grants to states, allowing greater flexibility in program design and spending.
• Limited resources will push states toward models that maximize efficiency and outcomes. Unlike Medicaid fee-for-service, which is state-funded, VBC ultimately pushes more risk onto provider groups and risk-based entities off each state’s balance sheet.  
• VBC focuses on patient outcomes rather than service volume, potentially leading to better health results with limited resources.
• VBC is a hot sector for private investment and innovation, which Republican administrations generally encourage. Private investment capital could supplement limited public funds.

3. New drug modalities will treat the masses–literally and figuratively 

The next decade will witness a paradigm shift in the focus of novel drug modalities—such as gene therapies, RNA-based treatments, cell therapies, and protein degraders—from rare diseases to large-scale chronic conditions to treat the masses (yes, including obesity), either as a pivot or a complementary expansion. This transition will be driven by advancements in technology, market dynamics, and the continued evolution of drug development.

Today, small molecules and antibodies dominate drug modalities. However, antibodies once were a “new modality”, too. The first monoclonal antibody was approved in 1986, after initial generation in 1975. This drug, Orthoclone OKT3, was approved to prevent kidney transplant rejection, limited to rare, acute cases. Today, antibody therapeutics represent a >$230B market, and growing. We see a similar analogy in “novel modalities”: just as monoclonal antibody technology sparked excitement in 1975, the development of CRISPR gene editing in 2012 along with other new drug approaches ignited a similar wave of enthusiasm. We predict we’re entering a decade-long shift towards genetic medicine and other novel modalities expanding scope to large, chronic conditions as a mainstay of drug development and personalized medicine. 

There are three key drivers behind this shift:

1. Technological maturation: Like antibody drugs were initially constrained by efficacy, immunogenicity and other safety issues, genetic medicines and other novel modalities have faced challenges with specificity and efficiency of delivery, off-target safety concerns, and reimbursement challenges. Similar to how antibody technology progressed from murine to chimeric to humanized antibodies, novel modalities are undergoing similar refinements. This opens the door for the biotech ecosystem to leverage these newer modalities to address the multifactorial complexities of chronic conditions like diabetes, cardiovascular diseases, and neurodegenerative disorders.
2. Economic imperatives: The success of GLP-1s (which some analysts predict will be a $100 billion market) have further increased the entire industry’s focus on large, chronic diseases given their immense market potential. As manufacturing techniques mature for these newer modalities, sufficient cost of goods sold (COGS) reductions will be critical for commercial viability. Expanding the application of these modalities to chronic conditions unlocks larger markets, aligning pharmaceutical innovation with sustained financial viability. Additionally, given several provisions in the Inflation Reduction Act (IRA) that may impede incentives to develop drugs for orphan indications, drug developers may feel the need to explore larger chronic conditions with these newer modalities, though the future of this legislation remains uncertain in the upcoming administration.
3. Integration with precision medicine: Advances in genomics and biomarker discovery enable stratification of patients with chronic diseases into subgroups that closely resemble the genetic clarity of rare diseases. This precision reduces the risks associated with targeting large, heterogeneous patient populations.

4. Multiple de novo protein therapeutics will be tested in humans in 2025

In 2025, multiple de novo protein therapeutics designed entirely by AI will likely enter human clinical trials. While there have already been over 70 drugs leveraging AI that have entered clinical trials, we’d argue that the majority of these have used AI to modify or optimize existing drug structures or sequences, rather than generating a completely de novo therapeutic with an AI approach to design drugs from the ground up without a starting point.  

Generative models like ProGen, RFdiffusion, SCUBA-diffusion, Chroma and others are turning the traditional therapeutic development approach on its head, aiming to design completely synthetic proteins with drug-like properties to perform a desired function in treating disease.  While it is still early days for de novo drug design, we are optimistic about this approach’s longer-term potential because we believe that value will accrue most significantly to the AI-native biotechs that leverage these tools to create blockbuster therapeutics.

Plus, the rapid iteration in this field we are seeing gives us hope that generative AI could rapidly advance drug programs through preclinical and regulatory hurdles. As a result, a zero-shot design cycle offers the potential to witness completely generative therapeutics obtain investigational new drug (IND) clearance, and even enter human trials. This milestone will mark a convergence of advances in AI, structural biology, and translational medicine, with the initial focus on a narrowly defined, high-impact therapeutic area.

5. At risk of unions’ disapproval, the AI conversation will shift in focus toward happier jobs and workforce multiplier capabilities

Healthcare's AI conversation needs a fundamental shift in 2025 to deliver true transformation. Two forces are driving this imperative: growing influence from unions and worker advocacy groups concerned about job automation, and an increasingly strained workforce facing critical supply shortages.

Rather than focusing on automating jobs away, successful AI adoption will focus on empowering healthcare workers—both frontline and back-office staff—with "superpowers" that have new skill requirements and enhance their capabilities and efficiency. Having humans in the loop is about more than just productivity gains—it’s the new way forward for life-saving healthcare. Human oversight is required to fulfill the promise of high-stakes, AI-enabled pathology interpretations and surgical decisions. 

By reframing AI's role as a tool for scaling the limited supply of healthcare practitioners, organizations can foster new behaviors and workflows that allow everyone to operate at the top of their license. This includes not only doctors and nurses but also administrative staff who play critical roles in patient care delivery.

The key to this transformation lies in collaboration and trust that prioritizes worker empowerment, fair training, and equitable access to technology. As a result, AI tools will increasingly create happier, more fulfilled healthcare jobs. This shift will redefine the narrative around AI, transforming it from a perceived threat to a critical enabler of a more effective and sustainable healthcare system.

6. Healthcare systems will scale new infrastructure to end the obesity epidemic and sustain mass adoption of GLP-1s 

More than two in five (42.4%) U.S. adults have obesity—it’s so prevalent that it needs to be acknowledged as more than an individual willpower problem. Factors like genetics, food access, poor sleep quality, and chronic stress all contribute to the obesity epidemic, calling for new infrastructure to help end it. In 2024, the mainstream adoption of GLP-1 receptor agonists marked a turning point, with Medicare and other major payers beginning to cover these transformative therapies and their impact capturing public attention. For the first time in decades, U.S. obesity rates showed measurable improvement, bending the curve and sparking optimism for long-term public health.

However, mass GLP-1 adoption also introduces secondary and tertiary effects that the industry must anticipate and address. To sustain this momentum, we predict healthcare systems will invest in new infrastructure across four key areas: 

• Precision medicine and companion diagnostics will help match patients to optimal therapies and dosing regimens as more GLP-1 agonist drugs gain approval and market competition grows. 
• Comprehensive wraparound services and side effect management will be essential for managing side effects like gastrointestinal (GI) issues and muscle loss, emphasizing “food-as-medicine” nutrition counseling, exercise, and holistic care plans.
• Scaled manufacturing and supply chain capabilities must expand to meet growing demand while maintaining affordability, likely via step therapy protocols and patient assistance programs.
• New payment models are required as GLP-1s demonstrate impact beyond weight loss alone. As these drugs reduce expensive comorbidities across addiction, cardio-metabolic, and renal conditions, the industry will need innovative value-based arrangements that align incentives and share savings across all stakeholders who traditionally bear these costs—from payers to providers and pharma companies.

7. U.S. healthcare will develop a net new workforce model to address the silver tsunami 

The U.S. healthcare caregiver system will face an unprecedented strain as the boomer generation continues to age into Medicare. This silver tsunami will far outpace the available workforce, with the ratio of workers to Medicare beneficiaries reaching a critical breaking point. Existing caregivers, already overburdened and undercompensated, will feel the additional weight of caregiving demands, compounded by potential disruptions from immigration policy shifts and mass deportations from the new administration. 

To sustain care for the aging population, we expect four key innovations to emerge in 2025:

• AI aids and companions: Artificial intelligence and IOT devices that straddle the physician and digital world will support the existing supply of caregivers both in facilities and homes.
• "Business-in-a-box" models will enable clinician-entrepreneurs to create and operate their own caregiving businesses with streamlined operations. 
• Family caregiver payment programs: Programs that enable aging at home and take care of members in a scaled way such as group therapy. Reimbursement reforms, including family caregiver payment programs and better compensation for professional caregivers, will attract more individuals to the field. 
• Credentialing and upskilling talent will help equip low-skilled workers to provide higher-quality care and take on more advanced roles.

Together, these efforts will not only bolster the caregiving workforce but also transform it into a sustainable, innovative sector capable of meeting the needs of an aging nation.

8. Healthcare organizations will shift from piloting new tech to meeting real demands for ROI in AI 

Following an intense year of AI enthusiasm in 2024, healthcare organizations in 2025 will reach a critical juncture in their battle with "pilotitis": the persistent cycle of piloting new technologies without achieving widespread adoption. As organizations face mounting pressure to demonstrate returns on their AI investments, vendor procurement will become dramatically more selective.

Rather than continuing with isolated proof-of-concept projects, organizations will prioritize scalable, enterprise-wide platform deployments backed by a maturing ecosystem of benchmarks and assurance frameworks. New AI vendors will face heightened scrutiny, with organizations demanding robust evidence of potential ROI before signing contracts. Evaluation frameworks will evolve beyond simple accuracy metrics to encompass workflow integration costs, staff training requirements, and measurable improvements in patient outcomes.

While healthcare enterprises will continue prioritizing solutions that generate first-year revenue and immediate cost savings, we anticipate growing demand for clinical AI applications, where there is significant work to be done across regulatory, financial, and safety domains.

9. Foundation model providers will launch healthcare and life sciences-specific products 

Foundation model companies will launch specialized models in healthcare and life sciences applications this year. We're already seeing strong indicators: OpenAI is actively building its health AI team, Anthropic is gaining traction with healthcare firms, and Google released open weight models called Health AI Developer Foundation models (HAI-DEF), which help developers build healthcare AI applications starting with chest X-rays, dermatology, and pathology at the end of 2024. 

This expansion raises intriguing strategic questions. Will these companies leverage their formidable consumer brands to venture into direct healthcare services like symptom checkers and AI medical consultations? Or will they focus on deepening their relationships with existing enterprise customers in the healthcare sector? The life sciences landscape presents its own challenges, particularly in the protein language model space. With several well-funded competitors already commanding significant leads, new entrants will need to carve out distinctive applications or innovative business models to generate meaningful value.

Yet perhaps the most fascinating aspect is watching these foundation model companies bet on specialized biomedical models, even as debates continue about their advantages over general-purpose alternatives. Who better to hedge the bet?

10. Multimodal clinical AI will revolutionize measurement in medicine, but payment models lag behind 

2025 is the year multimodal AI in healthcare faces its commercial reality check. The technology —integrating imaging, clinical notes, video, audio, and -omics data—has matured. But healthcare's traditional payment structures haven't caught up. The gap between technical capability and commercial viability will continue to widen.

Multimodal AI is now tackling healthcare's most complex challenges: interpreting haploinsufficient and polygenic conditions, assessing disease progression, simulating patient encounters, and guiding robotic surgeries, to name a few. But clinical deployment requires more than technical excellence. It demands careful integration into physician workflows, clear protocols for clinical decision support, actionability, safety monitoring infrastructure, and sustainable business models.

The initial wins for multimodal healthcare AI will come from operational applications, such as virtual nursing, supply chain optimization, and surgical robotics, where ROI calculations and workflow integration are more straightforward. The transformative potential of diagnostic AI, however, remains constrained by reimbursement complexities. Even as companies secure CPT codes, implementation barriers create a maze of restrictions that limit scalability.

The vision of the learning health system—where every patient interaction advances medical knowledge—is within reach technically with multimodal AI. While operational use cases will lead adoption, clinical AI represents our greatest opportunity to fundamentally reimagine healthcare. Getting the business models right is essential to unlock this potential.