Built on decades of advancements in machine learning (ML) and neural networks, foundation models stand to address long-standing AI data and training limitations and introduce unmatched adaptability.
While foundation models are still emerging in healthcare, its principles are rooted in earlier successes across other industries, making the transition to healthcare a natural progression.
Foundation models have already made a significant impact in areas like language processing, paving the way for a new generation of versatile, AI-driven solutions. Here are three examples that show how foundation models are being used today:
Large Language Models
Multi-Modal Model
It’s important to note that the examples above are not specific to healthcare but do have possible healthcare applications. However, foundation models have significant promise. Aidoc’s CARE1™ (Clinical AI Reasoning Engine, Version 1) is a clinical-grade foundation model specifically designed for CT imaging.
Trained on millions of cases and anatomies, CARE1™ will soon enable comprehensive, real-time detection of suspected critical conditions across various medical imaging modalities, opening new possibilities for diagnostics. Notably, to our knowledge, it is the first foundation model submitted for FDA clearance.
Foundation models didn’t emerge overnight; their development reflects a series of interconnected advancements in AI, each building upon the previous era’s achievements:
As noted above, the current rise of foundation models is underpinned by two transformative advancements that have reshaped the landscape of AI:
Transformers:
The transformer architecture, with its attention mechanisms, allows models to focus on the most relevant elements within large datasets, including electronic health records (EHRs) and diagnostic images. This capability makes foundation models particularly adept at identifying complex patterns, enabling precise and context-aware predictions.
Data and Computational Power:
Advances in computational power enable foundation models to learn at scale through self-supervised techniques, extracting insights without the need for manual annotation. This capability allows it to process vast amounts of unannotated healthcare data while developing a nuanced understanding of diverse patient populations and clinical scenarios.
The adaptability of foundation models is what sets them apart from traditional AI solutions. Here’s how it happens:
Though still in early stages within healthcare, foundation models hold immense promise:
Short-Term Impact (1-3 Years): Foundation models will see broader use in imaging and diagnostics, helping clinicians manage workloads more efficiently and accurately. It will achieve this by enhancing image analysis for greater precision, automating routine tasks, offering evidence-based decision support, adapting to various medical specialties and streamlining workflows through integration with systems like PACS and EHRs.
Long-Term Impact (5-10 Years): Over the long term, foundation models will drive breakthroughs in personalized medicine by helping to tailor treatments to individual patients, advance predictive analytics to foresee health trends and risks, enhance clinical decision support with real-time insights and enable integrated healthcare systems that leverage AI to deliver comprehensive, patient-centered care across specialities and departments.
By addressing the limitations of earlier systems, bridging data silos and scaling across diverse medical applications, these models offer unparalleled versatility and are poised to accelerate AI adoption.
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