Financial Landscape

Alzheimer’s disease affects an estimated 6.9 million people in the United States, and as many as 18 million may remain undiagnosed. In 2024, the cost of caring for individuals with Alzheimer’s and other dementias is projected to reach $360 billion, with $231 billion covered by Medicare and Medicaid. Multiple Sclerosis affects nearly one million Americans, with an average annual per-patient cost exceeding $88,000.

Imaging, especially MRI, is central to diagnosis and monitoring for both conditions, yet remains expensive, inconsistently interpreted, and poorly optimized for early detection. Alzheimer’s has proven especially difficult to address due to its slow, variable onset and reliance on subjective clinical assessments.

This makes Alzheimer’s and MS uniquely positioned for disruption. AI platforms that can standardize imaging analysis, detect preclinical markers, and support real-time treatment decisions are not just clinically valuable; they represent one of the most strategically important frontiers in healthcare innovation today.

Clinical Impact

AI is fundamentally transforming how Alzheimer’s and MS are diagnosed and managed, resolving long-standing bottlenecks in timing, accuracy, and treatment stratification.

In Alzheimer’s disease, the clinical pathway has historically depended on subjective cognitive testing and late-stage neuroimaging. AI now makes it possible to detect early brain volume loss, amyloid deposition, and longitudinal changes in cortical structure with greater objectivity and speed. icometrix’s platform, icoBrain, automatically quantifies these biomarkers and delivers structured, decision-ready reports to support physicians in aligning treatment choices with imaging evidence. This capability is critical in the context of new anti-amyloid therapies, which require precise diagnosis and safety monitoring.

In MS, diagnosis and treatment planning are often delayed by difficulties in classifying disease subtype, whether relapsing-remitting or progressive. Recent research highlights how initiating the wrong treatment due to misclassification can worsen outcomes. AI models trained on imaging and clinical datasets are now differentiating these subtypes with high predictive accuracy, enabling earlier and more appropriate intervention. These same models are beginning to show promise in forecasting disease progression, allowing for proactive care strategies that were previously out of reach. One recent, notable example is MindGlide, an AI tool developed by researchers at University College London. This deep learning model demonstrated the ability to rapidly and accurately quantify brain structures and lesions in over 14,000 MRI images analyzed from more than 1,000 MS patients.

On the research front, AI is also being used in preclinical Alzheimer’s studies to analyze behavioral data and detect subtle phenotypes long before clinical symptoms appear. This is accelerating drug discovery timelines and redefining how neurodegenerative research is conducted. This shift from reactive diagnosis to proactive surveillance is also driving new models of clinical collaboration and data infrastructure.

icometrix has now collaborated with the American College of Radiology through ALZ-NET, a nationwide network focused on improving dementia care via structured MRI analysis. The initiative collects longitudinal real-world data across multiple sites to support evidence-based Alzheimer’s diagnosis and treatment. To encourage adoption, participating imaging centers will have access to icobrain ARIA, icometrix’s AI tool for monitoring amyloid-related imaging abnormalities, along with newly approved CPT codes for reimbursement—making this form of AI-enabled monitoring for Alzheimer’s not only clinically actionable but financially sustainable at scale.

Investment Trends

A new generation of AI startups is redefining how neurodegenerative diseases are diagnosed, monitored, and managed. These companies are not building research prototypes; they are delivering clinically integrated tools with regulatory visibility and real-world deployment pathways.

icometrix is at the forefront of this wave. Its flagship product, icoBrain, delivers FDA-cleared quantitative MRI analysis for Alzheimer’s and MS, enabling faster, more accurate diagnosis and treatment alignment. The company’s partnership with Philips to co-develop embedded AI MRI tools is a clear marker of industrial-grade integration, not speculative experimentation. These tools are already live in hospital systems, with reimbursement channels in place—a model that sets the benchmark for scalable AI in neurology.

Neurophet is tackling Alzheimer’s imaging with AI models that reduce dependency on expensive PET scans by extracting high-fidelity diagnostics from conventional MRI. Their work is aimed at enabling earlier, more accessible detection, and they are already in clinical collaborations with pharma partners like AriBio.

BeCare Link is approaching MS care through remote AI-powered neurological assessment. Its mobile platform, BeCare MS Link, enables continuous, real-world monitoring of motor function and disease progression—an approach that shifts MS management from episodic check-ins to longitudinal oversight, while reducing clinical burden.

Theia AI combines imaging data with longitudinal patient history to forecast MS progression risk. Its multimodal approach moves AI beyond static snapshots toward predictive modeling, a critical step in personalizing treatment before symptoms escalate.

BrainSee is an FDA-cleared AI platform designed to assess the risk of progression from amnestic mild cognitive impairment to Alzheimer’s dementia. Its commercial relevance lies in enabling earlier intervention for patients with mild cognitive impairment, long before traditional diagnostics would trigger action.

These companies represent more than technological innovation. They reflect a strategic pivot toward AI tools that are validated, reimbursable, and ready for integration into standard neurology practice. For investors and providers, this cohort is not speculative; it is investable infrastructure for the future of neurodegenerative care.

Future Directions

AI in Alzheimer’s and MS is no longer on the margins of healthcare innovation. It is being deployed in hospitals, reimbursed by payers, and trusted by clinicians. The economic and clinical pressures behind these diseases demand real solutions, not academic prototypes.

Companies like icometrix are showing what real-world neuro-AI looks like—FDA-cleared platforms, enterprise-grade integrations, and strategic partnerships with leaders like Philips. This is not future vision. It is operational infrastructure already in place.

For investors, payers, and providers, this is the window to move before neuro-AI becomes standard. Those who act now will set the benchmarks, own the margins, and lead the shift from reactive care to precision neurology.