Key Theses on Healthcare AI Implementation

Earlier tech advancements have established the groundwork for the healthcare AI revolution. Years of investment in promoting the adoption of EHRs and digitizing clinical and administrative data have transformed healthcare into a rich landscape for AI development. With vast amounts of text, images, videos, lab results, and other information now machine-readable, such datasets provide the fuel needed to power AI systems.

By exploring AI's growing influence on healthcare, we can identify three main hypotheses that guide investment strategies in this fast-changing field.

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The Alignment of Modalities, Business Models, and Market Fit Drives Value Creation

Combining the AI solution’s modality and business model determines the total addressable market (TAM) and gross margins. Companies tackling similar healthcare workflows with different business models can see up to a 25x difference in TAM. Achieving the right modality-business model-market fit is key for healthcare AI startups.

Modalities

• AI-enabled Software. Platforms enhanced by AI to improve functionality and UX.
• Copilots. AI assistants that improve productivity by automating tasks within workflows.
• Agents. Autonomous systems perform tasks with minimal human input.
• AI-enabled Services. Traditional services are augmented by AI to increase accuracy.

In healthcare, unique modalities like diagnostics and therapeutics stand out. AI diagnostics automate disease detection through imaging or biomarkers but face regulatory approval and practitioner trust challenges. However, companies like Cleerly and Viz.ai are breaking through these barriers. AI therapeutics focus on treatments, with the most common use case being AI-aided drug discovery, as seen in Seismic Therapeutic for autoimmune diseases.

Business Models

Modality informs business models, typically falling into two categories: usage-based and performance-based. Companies may combine multiple models, but understanding how each affects TAM and margins is crucial.?

For example, a hypothetical AI startup in ophthalmology could range from a TAM of $84 million for a copilot product to $2.1 billion for an AI-enabled service, depending on the model used. That shows how modality-business model alignment directly impacts value creation.

Multimodal AI Is a Game-Changer for the Industry?

AI advancements have led to models that excel across various data types (text, images, etc.). Healthcare's multi-dimensional data, from clinical records to imaging, makes it an ideal space for multimodal AI. This emerging field combines diverse data types, unlocking new opportunities in areas like radiogenomics and operational informatics.

Companies like Theator and RadAI lead the way in multimodal AI, integrating vision-language models for surgical video analysis and diagnostic tools. Besides, research is advancing in applications like multimodal clinical simulators and biomedical discovery tools. So, the potential for the future is vast: combining data from wearable devices, imaging, and genomics to improve diagnoses and optimize healthcare systems. Finally, with the power of multimodal AI, healthcare can confidently expect breakthroughs across clinical, operational, and scientific areas.

Healthcare Needs Tailored Infrastructure

The current infrastructure for cybersecurity, data privacy, and AI model performance monitoring in the medical sector must catch up to other industries. While security is critical, solutions built specifically for healthcare AI are still in their early stages.

• Data generation & management models require high volumes of quality data. Although marketplaces like Protege and Omny Health are helping bridge this gap, scalable tools for de-identifying and managing sensitive patient data are still necessary.
• Model monitoring AI models can degrade over time due to data changes, a process called model drift. Monitoring solutions are essential to ensure AI remains accurate, particularly in high-stakes tasks like diagnostics or supply chain management. Tools that track model performance and alert users of inconsistencies are crucial in mitigating risk.
• In terms of governance, Safe AI implementation in healthcare requires clear ownership within organizations, often through roles like Chief AI Officers (CAIOs). These experts, with knowledge in healthcare and AI, ensure techs are integrated safely and effectively.

Thus, while the potential for AI is vast, its success relies on aligning modalities with business models, advancing multimodal AI, and building industry-specific infrastructure and governance.

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