The artificial intelligence revolution in healthcare is not reaching everyone equally. A comprehensive analysis of nearly 3,800 AI health startups founded between 2010 and 2024 reveals a troubling pattern: venture capital is flooding into high-complexity AI systems for diagnostics, drug discovery, and clinical decision support, while entire categories of healthcare needs, including mental health and public health interventions, remain dramatically underfunded .

Why Are Investors Ignoring Mental Health and Public Health AI?

The data tells a striking story. According to researchers who analyzed the startup ecosystem using a five-tier framework of AI systems complexity, nearly two-thirds of all AI healthcare investment concentrates on just three domains: clinical decision support, drug discovery, and diagnostics . These areas attract capital because they rely on deep-learning systems that can process large datasets and demonstrate measurable, quantifiable results. But this focus comes at a cost.

Mental health, public health, and rehabilitation technologies attract significantly less venture capital, not because these areas lack clinical need, but because of practical barriers. These fields often struggle with data limitations and scalability challenges that make them less attractive to investors seeking rapid returns. The research suggests that the problem is not a lack of innovation potential, but rather structural obstacles in how AI systems are built and deployed in these domains .

How Are Geographic and Team Composition Gaps Shaping the AI Healthcare Landscape?

The startup ecosystem reveals additional disparities that could limit the reach of AI healthcare innovation. Consider these key structural imbalances:

• Geographic Concentration: AI health startups remain heavily concentrated in high-income countries, limiting access to AI-driven healthcare solutions in lower-income regions where healthcare infrastructure gaps are often greatest.
• Technical Dominance: Founding teams are predominantly composed of technical and business-oriented professionals, with limited clinical representation from doctors, nurses, and healthcare practitioners who understand real-world care delivery challenges.
• Gender Representation: The startup ecosystem shows limited gender diversity, which research suggests can narrow the range of health problems being addressed and the populations being served.

These patterns matter because they shape which health problems get solved first. When teams lack clinical input and diversity, they may miss critical insights about how AI tools will actually function in hospitals, clinics, and rural healthcare settings .

Meanwhile, leaders in healthcare and research are beginning to recognize AI's potential to address geographic healthcare gaps. At the University of Maine, Michael F. Chiang, director of the National Eye Institute at the National Institutes of Health, emphasized how emerging AI technologies could expand access to care beyond traditional clinical settings. He highlighted opportunities to deliver care through telehealth, remote monitoring, and home-based tools, particularly in rural areas where patients often travel long distances for specialist care .

"Clinical practice and research are being rapidly reshaped by breakthroughs in artificial intelligence and data science," said Chiang, noting that these advances are making medical care more data-driven, consistent, and accessible.

Michael F. Chiang, Director of the National Eye Institute at the National Institutes of Health

Chiang pointed to a concrete example of how AI can improve consistency in diagnosis. Retinopathy of prematurity, a condition that can cause blindness in infants, presents a challenge where even expert ophthalmologists reviewing the same retinal images often disagree on disease severity. AI systems can help standardize these assessments, making diagnoses more accurate and consistent across different clinicians and settings .

What Role Will Real-World Data Play in Accelerating Drug Development?

Beyond the startup ecosystem, major healthcare organizations are deploying AI in ways that could reshape how quickly new treatments reach patients. Labcorp, a global leader in laboratory services, recently announced an AI-powered real-world data platform developed with Amazon Web Services (AWS) and Datavant, specifically designed to accelerate Alzheimer's disease research .

The platform addresses a critical bottleneck in drug development: the time required to prepare and analyze healthcare data. Traditionally, researchers spend months extracting, cleaning, and organizing fragmented data from different sources before they can even begin analysis. Labcorp's platform compresses this process dramatically. Using agentic AI, a type of AI system that can autonomously plan and execute complex tasks, the platform can generate insights in minutes that previously required months of intensive data mining .

"By combining AI, advanced analytics and the scale of Labcorp's diagnostic data, we're redefining what's possible, compressing months of manual data preparation into minutes. That shift enables researchers to focus on discovery instead of data engineering, accelerating the insights needed to drive better treatment decisions," explained Bola Oyegunwa, Labcorp's executive vice president and chief information and technology officer.

Bola Oyegunwa, EVP and Chief Information and Technology Officer at Labcorp

The platform unifies multiple data sources, including Labcorp's extensive diagnostic and genomic datasets, medical claims data, and in future versions, electronic health records and social determinants of health information. This integration allows researchers to identify patient cohorts, track disease progression patterns, and measure treatment effectiveness across diverse populations in real time .

With more than 7.2 million Americans living with Alzheimer's disease and annual care costs exceeding $380 billion in the United States, accelerating drug development is critical. The platform's ability to shorten timelines could potentially save years in bringing new treatments to patients .

How Is AI Transforming Genomic and Protein Analysis?

Beyond diagnostics and drug discovery, AI is reshaping how researchers analyze biological data at the molecular level. The global market for AI in omics studies, which involves analyzing genomic, proteomic, and metabolomic data, was valued at $1.25 billion in 2025 and is projected to reach $6.11 billion by 2035, growing at a compound annual growth rate of 17.20 percent .

Omics refers to the comprehensive study of biological molecules, such as genes, proteins, and metabolites. Traditional methods struggle with the sheer volume and complexity of this data. AI algorithms, particularly machine learning and deep learning systems, can uncover patterns that would be impossible for humans to detect manually, turning raw genetic sequences into testable hypotheses that influence clinical decisions .

The market is dominated by genomics applications, which accounted for 40 percent of the AI in omics market in 2025, driven by large-scale DNA sequencing data and the increasing use of AI for variant detection and personalized medicine. Proteomics, the study of proteins, held the second-largest share at 20 percent and is growing rapidly as AI's ability to predict protein structure and analyze biological pathways becomes more precise .

Recent industry developments underscore this momentum. In October 2025, Illumina, a leader in DNA sequencing technology, launched a new business focused on informatics and artificial intelligence software for drug developers to analyze large amounts of DNA sequencing and multi-omics data. The company also acquired SomaLogic, a proteomics firm, for $425 million, signaling major investment in protein analysis capabilities .

The pharmaceutical and biotechnology sector accounts for the largest share of the AI in omics market at 50 percent, driven by high investments in AI-enhanced drug discovery and research aimed at improving drug pipelines and reducing development failure rates. Research institutes hold the second-largest share and are expected to grow significantly as they continue to innovate in genomics and multi-omics research .

North America dominates the global market with a 42 percent share, supported by strong capital investment, advanced computational infrastructure, and robust research networks. However, Asia-Pacific is expected to grow at the fastest rate during the forecast period, fueled by expanding genomics initiatives in China, India, Japan, and South Korea .

The convergence of these trends, from startup ecosystem imbalances to real-world data platforms to omics market growth, suggests that AI healthcare innovation is accelerating rapidly, but unevenly. The challenge ahead will be ensuring that these advances reach beyond high-income countries and high-complexity domains to address the full spectrum of healthcare needs, including mental health, public health, and care delivery in underserved communities.