For much of the modern biopharma era, rare disease has occupied an awkward place in corporate strategy. On one hand, rare and ultra-rare conditions represent profound unmet need, strong scientific interest, and substantial human impact. On the other, they present small and fragmented markets, complex evidence requirements, and commercial models that do not always fit traditional playbooks.^[1][2]

Rare disease is acknowledged as important, but often treated as a strategic exception: a handful of high-profile programs, a few flagship partnerships, and a series of bespoke efforts that are often difficult to scale. Today, however, advances in artificial intelligence and adjacent technologies are starting to change the underlying economics and operating assumptions. What makes this shift particularly compelling is not just that AI can address rare disease challenges, but that AI must be applied differently in rare disease, requiring innovation in methodology that becomes a strategic asset across the entire portfolio.^[3][4]

For companies prepared to act, rare disease can move from an exception to a more valuable, systemized segment of the business and serve as a catalyst for enterprise-wide AI capabilities that differentiate performance in every therapeutic area.^[5][6]

How companies historically navigated the business of rare disease

The business of rare disease emerged long before AI became a board-level topic. Historically, pharma and biotech companies have relied on a relatively narrow set of levers to justify investment and manage risk.^[7][8]

Regulatory and incentive-driven strategies

The Orphan Drug Act and comparable frameworks in Europe and other regions created the original economic foundation for rare disease. Companies designed strategies around orphan designation and associated benefits such as market exclusivity, fee waivers, and tax credits. Expedited pathways and flexibility in trial design made addressing small populations feasible, while portfolio approaches used a small number of high-value rare disease assets to offset higher development costs and commercial uncertainty elsewhere.^[9][7]

These levers transformed rare disease from a purely mission-driven activity into a viable business segment, but they did not remove fundamental operational and data challenges.^[2]

Focused therapeutic and platform bets

Many organizations approached rare disease as an extension of existing scientific strengths. Gene and cell therapy platforms found natural fit with monogenic or ultra-rare conditions. Oncology and immunology franchises expanded into orphan subtypes or niche biomarker-defined populations. In-licensing or acquiring late-stage rare disease assets from smaller biotechs allowed larger organizations to de-risk science while leveraging internal infrastructure.^[10][2]

These approaches made strategic sense, but they reinforced a product-by-product mindset, with each rare disease program treated as a largely bespoke effort.^[2]

High-touch clinical and commercial models

Because patient populations are small and specialized, historical operating models in rare disease leaned heavily on centers of excellence and key opinion leader networks as hubs for clinical trials and treatment. Deep relationships with advocacy groups supported recruitment, education, and adherence, while high-touch patient and caregiver support programs delivered value through specialty hubs or bespoke service providers.^[11][12]

These models created value and trust, but they were also expensive, labor-intensive, and difficult to scale across many indications or geographies.^[13][14]

Traditional evidence generation under non-traditional constraints

Perhaps the most persistent challenge has been generating evidence that satisfies regulators, payers, clinicians, and investors when patient numbers are limited and geographically dispersed, natural history is poorly characterized, and endpoints are evolving or not yet standardized.^[15][16]

Historically, companies relied on creative trial designs, long-running registries, and post-marketing commitments. While many succeeded, every program felt like a unique negotiation with regulators and payers.^[17][18]

The pain points that still limit the value of rare disease

Despite scientific and policy advances, several pain points remain central to leadership discussions about the role of rare disease in the broader portfolio.

Diagnostic delay and invisibility of patients mean many patients spend years without a correct diagnosis, never entering the funnel for clinical trials or approved therapies. This limits both patient impact and commercial potential. Small, competing recruitment pools create an environment where sponsors compete for the same small patient populations, driving up timelines and cost per enrolled patient.^{[12][19][20][21][11]}

Fragmented, low-volume data constrains trial design, modeling, and value demonstration. Natural history data, real-world evidence, and outcomes information are often scattered across sites, registries, and unstructured records. Uncertain, case-by-case commercial models mean pricing, access, and contracting for high-cost rare disease therapies require intensive negotiations and ongoing data generation, adding complexity to revenue forecasting and asset valuation.^[16][22][2]

Internally, organizational strain is common. Rare disease programs often rely on small, overextended teams who navigate bespoke processes, tools, and partnerships, with limited ability to reuse learnings across programs.^[14][23]

These pain points are not new. What is new is the extent to which AI and related technologies can be directed at them in a systematic way, and crucially, the fact that solving these problems forces organizations to develop capabilities that are increasingly valuable across the entire portfolio.^[24][3]

Why rare disease demands different AI approaches

The distinctive constraints of rare disease create an environment where conventional AI methods often fail, forcing the development of more sophisticated, generalizable techniques. This constraint-driven innovation is where rare disease becomes strategically valuable beyond its own market boundaries.^[4][25]

AI must learn from scarcity, not abundance

Unlike common diseases where large datasets enable traditional statistical and machine learning approaches, rare disease forces AI methodologies to be fundamentally different. Few-shot and zero-shot learning techniques allow models to diagnose rare genetic conditions with as few as one labeled patient per disease, or even zero labeled examples.^[26][27][28]

Models like SHEPHERD learn from knowledge graphs and simulated patients rather than requiring thousands of real-world training examples. They encode existing biomedical knowledge about gene-phenotype-disease relationships and apply this to novel presentations. Because real patient cohorts are too small for traditional machine learning, researchers have developed methods to simulate realistic rare disease patients with novel genetic conditions, training AI models on synthetic data that maintains clinical validity without requiring actual patient records.^[27][28][26]

This constraint-driven innovation makes rare disease an ideal proving ground for AI techniques that can operate effectively with limited data, a capability that becomes increasingly valuable as pharma moves into more personalized, biomarker-defined segments across all therapeutic areas.^[3][4]

Unstructured data becomes the primary asset

In rare disease, the majority of clinically relevant information exists in unstructured formats: clinical notes, case reports, imaging studies, patient advocacy forums, and scattered literature rather than in clean, structured databases.^[29][4][3]

Natural language processing and multimodal AI extract diagnostic signals from free-text medical records to identify undiagnosed patients trapped in diagnostic odysseys. They synthesize insights from case reports and social media where rare disease communities document their experiences, and integrate genomic, imaging, and phenotypic data that would otherwise remain siloed.^{[20][30][31][3]}

Because a large proportion of electronic medical records consists of unstructured data, and because rare diseases often lack standardized data capture, the ability to systematically convert unstructured content into actionable insights represents a massive unlock. Once perfected in rare disease, these NLP and data integration capabilities can be redeployed across any therapeutic area where data fragmentation limits insight generation.^[31][29]

Small populations force new trial design paradigms

Rare disease clinical trials face an impossible trade-off: small patient populations make randomized controlled trials difficult, but regulators and payers still require robust evidence. AI-powered synthetic control arms address this by using historical trial data and real-world evidence to construct virtual control groups, reducing patient burden and accelerating timelines by eliminating the need to randomize patients to placebo when patient pools are already scarce.^{[32][33][34][17]}

They enable hybrid designs where a small randomized control is supplemented with external data to increase statistical power. The FDA and other regulators are showing increasing interest in synthetic controls, particularly for rare diseases and settings where placebo is unethical.^[33][35]

Organizations that master synthetic control arm methodology in rare disease will be positioned to apply these techniques to accelerate trials in broader indications, especially as regulators become more comfortable with the approach.^[35][17]

Platform economics emerge from necessity

Historically, each rare disease program required bespoke development of diagnostics, endpoints, natural history models, and trial infrastructure. AI enables a shift toward platform thinking.

Drug repurposing at scale becomes feasible when AI models screen thousands of approved drugs against thousands of rare diseases simultaneously, identifying therapeutic candidates for conditions that would never justify traditional drug development economics. This transforms rare disease from a one-asset-at-a-time approach to a systematic screening problem.^[36][37][38]

Reusable diagnostic and prognostic models, once trained, can be adapted across multiple rare conditions within a disease family or mechanistic cluster. Biomedical knowledge graphs that connect genes, phenotypes, drugs, and diseases become more valuable with each rare disease program, creating network effects where each investment strengthens future capabilities.^[39][40][36]

This platform mindset, where capabilities are built to be reused rather than rebuilt, has profound implications for portfolio strategy. Companies that structure rare disease AI investments as platforms rather than one-off initiatives can amortize costs across multiple indications and create durable competitive advantages.^[41][5]

How rare disease AI capabilities transfer to other therapeutic areas

The methodological innovations necessitated by rare disease constraints have direct applications across the portfolio.^[4][3]

Low-resource learning enables precision segmentation everywhere

The few-shot learning techniques perfected in rare disease are directly applicable to biomarker-defined subpopulations in oncology, immunology, or neurology where patient counts drop as segmentation becomes more precise. They support emerging indications where natural history is limited and real-world evidence is still being generated, and enable global expansion into markets with limited local data infrastructure.^[28][26][27]

As precision medicine drives therapeutic areas toward smaller, more targeted patient segments, the ability to generate robust insights from small cohorts becomes a transferable competitive advantage.^[42][43]

Unstructured data pipelines scale beyond rare disease

NLP and multimodal AI systems built to extract rare disease signals from messy data can be redeployed to mine real-world evidence from EHRs across any therapeutic area, identify eligible patients for clinical trials in common diseases where phenotypic complexity creates recruitment challenges, and support post-market surveillance by systematically analyzing adverse event narratives and patient-reported outcomes.^[44][29][31]

The technical infrastructure for processing unstructured clinical text, integrating multimodal data, and linking observations to structured ontologies is not disease-specific. It is a general capability that compounds in value as it is applied across therapeutic areas.^[3][4]

Synthetic control methodologies de-risk the broader portfolio

Once validated in rare disease trials, synthetic control arms can accelerate Phase 2 and Phase 3 trials in competitive indications by reducing control arm size, enable studies in settings where randomization to placebo is ethically challenging such as life-threatening conditions with available standard of care, and support regulatory submissions in geographies where conducting local trials is operationally difficult.^[32][33][35]

Organizations that build regulatory credibility and internal expertise with synthetic control arms in rare disease will find these methods increasingly applicable as regulators expand acceptance criteria.^[18][17]

Platform economics change portfolio evaluation

The shift from bespoke programs to reusable platforms changes how leadership teams evaluate opportunity. Adjacent indication expansion becomes more systematic when AI can identify mechanistic connections and repurposing opportunities across the portfolio. Build versus partner decisions shift when internal AI platforms create proprietary advantages that cannot be easily replicated by external vendors. M&A strategy evolves to prioritize acquiring not just assets but data, capabilities, and knowledge graph contributions that strengthen the platform.^[36][41]

Companies that demonstrate platform ROI in rare disease can justify broader AI infrastructure investments that benefit the entire portfolio.^[45][41]

Repositioning rare disease as a strategic learning laboratory

When rare disease AI initiatives are viewed through a portfolio lens, a different strategic picture emerges. Rare disease begins to look less like a series of isolated, high-risk bets and more like a set of interconnected opportunities that benefit from shared capabilities and generate insights that strengthen the broader business.^[6][5]

Portfolio and growth strategy

AI-informed insights into epidemiology, unmet need, and development feasibility can clarify which rare indications align best with a company's scientific strengths and risk appetite, reveal adjacencies where existing platforms or assets could serve multiple rare conditions, and support more confident business cases for entering or expanding rare disease segments.^[8][46]

Instead of treating rare disease as opportunistic, companies can evaluate it systematically alongside other growth pillars.^[47][48]

Capital allocation and valuation

Improved predictability in trial feasibility, evidence generation, and patient access can reduce perceived risk in financial models, enable more nuanced scenarios for peak sales and time to revenue, and strengthen the case for investment, partnerships, or M&A in rare disease portfolios.^[22][1]

AI does not remove all uncertainty, but by reducing some of the largest unknowns, it can improve the risk-reward profile that underpins capital allocation decisions.^[5][41]

Operating model and capability building

As AI capabilities in data integration, analytics, and decision support mature, they allow organizations to build shared rare disease capability stacks that support multiple programs rather than standing up new tools and teams for each asset. They harmonize how different functions such as R&D, medical, commercial, and market access use data to make coordinated decisions in rare disease. They scale successful approaches across geographies and therapeutic areas, instead of repeating early-stage experimentation.^[49][45]

This creates opportunities to reposition rare disease as a proving ground for advanced operating models that can later extend to broader portfolios.^[50][51]

What pain points AI can address and where leaders still need to lead

From a leadership perspective, the most compelling aspect of AI in rare disease is its alignment with long-standing pain points: bringing undiagnosed and misdiagnosed patients into view earlier and more consistently, improving confidence in recruitment, design, and operational planning for complex studies, making it easier to generate, integrate, and reuse high-quality clinical and real-world data, reducing redundant manual work across functions and programs, and turning bespoke efforts into patterns and playbooks that can be reused, refined, and expanded.^{[52][11][13][15][24][44]}

At the same time, AI introduces its own questions around data governance, validation, change management, and vendor selection. These are not purely technical issues. They are strategic and organizational.^[53][12]

This is where external partners can help life science companies move from promising concepts to fit-for-purpose solutions tailored to their specific portfolio, markets, and internal readiness.^[45][50]

Where specialized support adds the most value

For biopharma and biotech leaders, the challenge is no longer whether AI matters for rare disease, but rather where to focus, in what sequence, and how to ensure that efforts deliver measurable business impact rather than isolated pilots.

Support is often most valuable when organizations need to translate high-level AI and digital aspirations into a clear, staged roadmap for rare disease that aligns with corporate strategy, prioritize use cases that are both clinically meaningful and commercially relevant without overcommitting to unproven technologies, evaluate and structure partnerships with data, technology, and service providers in a crowded and rapidly evolving ecosystem, and design operating models, governance structures, and KPIs that make AI-enabled rare disease work both credible and sustainable.^[48][54][47]

These are inherently cross-functional questions that sit at the intersection of science, technology, and business strategy. They are also the questions that will determine which companies manage to reposition rare disease as a durable source of value and which remain stuck in a cycle of one-off initiatives.^[54][55]

Looking ahead

Rare disease will always demand a careful balance of mission, science, and economics. AI does not change that. What it can change is the degree to which companies are forced to choose between doing what is right for rare disease communities and doing what is sustainable for the business.^[24][52]

By tackling long-standing pain points in diagnosis, data, feasibility, and scalability, AI opens the door to a different conversation: one where rare disease is evaluated not just as a moral imperative or a reputational asset, but as a segment where thoughtful, technology-enabled strategies can create durable competitive advantage. More importantly, rare disease becomes a strategic laboratory where organizations develop AI capabilities that are increasingly critical across the entire portfolio as precision medicine, small patient cohorts, and fragmented data become the norm rather than the exception.^[40][55][3]

For organizations ready to revisit their rare disease portfolios and operating models through this lens, the next step is not to collect more point solutions. It is to ask a more fundamental question: How can advanced technologies be integrated into the way we design, prioritize, and execute rare disease strategies, so that every decision is both more patient-centered and more strategically sound, and how can the capabilities we build in rare disease become strategic assets that differentiate our performance across all therapeutic areas?^[50][45]

That is where the real transformation begins and where the right partnerships and frameworks can make the difference between incremental improvement and a step change in how rare disease fits into the broader pharma business.^[51][47]

^References