Introduction 

Healthcare currently accounts for 17% of GDP in the United States, making it one of the country’s largest economic sectors and an industry with immense potential to transform the human experience and improve the lives of its citizens. In recent years, we’ve witnessed a technological revolution that’s reshaping the way businesses operate across various sectors. At the forefront of this revolution is Generative AI, a groundbreaking technology that’s opening new avenues for innovation, efficiency, and growth. This article explores a curated list of promising applications of Generative AI in the Life Sciences and Healthcare industries, showcasing how this technology is not just a buzzword, but a powerful tool driving real change.    

Life Sciences & Health Care: Accelerating Innovation and Enhancing Safety   

AI-Driven Molecular Design 

Generative AI is transforming drug discovery by enabling rapid modeling of protein structures and biomolecular interactions. This capability is accelerating the identification and validation of new drug candidates, potentially leading to faster development of treatments for a wide range of diseases. 

We enjoy following AI-driven molecular design innovations, including those done by these companies:

    • Iktos: Uses AI for drug discovery and design, focusing on creating small molecules rapidly through generative AI technologies. Their platform includes tools like Makya for de novo drug design and Spaya for synthesis planning. 
    • Atomwise: Utilizes AI to improve small molecule drug discovery, employing deep learning for structure-based drug design. Their AtomNet platform is used for computational discovery and research. 
    • Exscientia: Specializes in AI-driven precision medicine, focusing on discovering, designing, and developing drugs efficiently using AI technologies. 
    • Deep Genomics: Uses machine learning to identify disease-causing genetic patterns and develop precision genetic-based medicines. 

Immersive Safety Training 

In healthcare settings, Generative AI is being employed to create highly personalized and immersive occupational health and safety training materials. By simulating realistic scenarios, AI helps healthcare workers better prepare for emergency situations, ultimately improving overall safety practices in medical facilities. 

We have been reading up on immersive safety training, including the work done by these organizations:

    • Immersive Factory: Specializes in virtual reality (VR) safety training, offering immersive experiences to simulate real-life work environments. Their VR training courses are designed to improve safety in the workplace. 
    • Oxford Medical Simulation: Develops VR medical and nursing simulations to provide immersive training experiences for healthcare professionals, enhancing learning through realistic patient scenarios. 
    • BioflightVR: Provides end-to-end VR training solutions for the medical industry, using high-resolution data to create detailed training environments. 

 Intelligent Regulatory Navigation 

Pharmaceutical companies are using Generative AI to help navigate the complex and ever-changing regulatory landscape. By processing and analyzing vast amounts of regulatory documents from multiple jurisdictions, AI helps ensure compliance and potentially reduces the risk of costly regulatory violations. 

We enjoy keeping up with advancements in intelligent regulatory navigation, including the ones by these companies:

    • Regology: Provides AI-powered regulatory compliance solutions, helping organizations understand and follow applicable laws through a collaborative platform. 
    • ZS: Offers AI solutions for regulatory intelligence in drug development, aiming to accelerate pharmaceutical research and approval processes by improving the regulatory decision-making process.
    • Regulatory Pharma Net: Utilizes AI to enhance efficiency in pharmaceutical regulatory processes, focusing on areas like document analysis, compliance, and content authoring.

AI-powered Diagnostic Model for Personalized Treatment 

Healthcare companies are developing innovative AI-powered diagnostic models that combine patient records, genetic information, and drug molecule data to improve diagnosis and treatment recommendations. These AI-driven approaches aim to match patients with the most effective therapeutics for managing their condition. Initiatives like these represent a significant step towards personalized medicine, potentially enhancing diagnostic accuracy, treatment efficacy, and overall patient outcomes across various medical conditions. 

We remain curious about AI-powered diagnostic models for personalized treatment, and have recently read about these companies:

    • Tempus: Leverages AI to analyze clinical and molecular data for personalized cancer treatments, aiming to determine the most effective treatment strategy. 
    • Freenome: Focuses on next-generation cancer screening and diagnostic tests using AI, connecting patients with innovative diagnostic solutions. 
    • PathAI: Uses AI to address challenging pathology problems, enhancing diagnostic precision and effectiveness in the research and pharmaceutical industries. 
    • Spring Health: Provides machine learning technology to offer personalized treatment recommendations for mental health issues, aiming to improve treatment efficacy. 
    • K Health: Offers a HIPAA-compliant AI health app that generates personalized medical insights by analyzing a vast database of health interactions. 

How Cerebras is helping customers in this domain: 

Empowering Pharma and Life Sciences with Cerebras Systems 

Cerebras Systems, renowned for its groundbreaking AI supercomputers, is uniquely positioned to revolutionize the life sciences and pharmaceutical industries. By leveraging their unparalleled expertise in hardware, software, and machine learning, Cerebras is enabling organizations to train large models tailored specifically for scientific applications. 

Unmatched Computational Power 

Cerebras Systems delivers extraordinary computational capabilities, which have empowered institutions like GlaxoSmithKline (GSK) and Mayo Clinic to accelerate the training of large language models (LLMs) and other sophisticated AI systems. Learn more about our work with Mayo here and with GSK here. 

This acceleration is crucial for life sciences, where speed can significantly enhance research outcomes and patient care. For instance, Cerebras’ work in genomics demonstrates their ability to train large language models on complex datasets, such as the full COVID genome sequence, as detailed in their GenSLM blog. 

Scalability for Scientific Innovation 

The architecture of Cerebras Systems provides seamless scalability, accommodating both small-scale experimental models and expansive, industry-grade AI systems. This adaptability is invaluable in areas such as molecular design and patient outcome predictions, where complexity can increase rapidly. An example of this scalability is illustrated in the Med42 blog, where Cerebras fine-tuned LLaMA2-70B to pass the US Medical License Exam in just a week. 

Advancing AI Research and Development 

For industries at the cutting edge of AI innovation, such as pharmaceuticals and life sciences, Cerebras Systems provides the computational muscle necessary to push the boundaries of Generative AI. By harnessing the power of Cerebras’ CS-3 and AI supercomputers, organizations can more effectively explore and exploit the vast potential of AI in scientific research. For example, Cerebras’ developed an innovative suite of biomedical language models that leverage sparse pre-training on domain-specific biomedical text data. By inducing up to 75% weight sparsity, MediSwift achieves a 2-2.5x reduction in training FLOPs, significantly cutting down computational costs. These models are fine-tuned to outperform similar-sized existing language models on various biomedical tasks, setting new benchmarks in efficiency and accuracy. Read the paper here 

To explore how Cerebras Systems can enhance your AI projects in the life sciences, don’t hesitate to reach out. 

Citations:
[1] https://immersivefactory.com
[2] https://www.startus-insights.com/innovators-guide/ai-startups-advancing-drug-discovery/
[3] https://www.labiotech.eu/best-biotech/ai-drug-discovery-companies/
[4] https://augnito.ai/resources/5-startups-championing-ai-in-drug-discovery/
[5] https://www.startus-insights.com/innovators-guide/discover-5-top-virtual-reality-startups-impacting-medical-training/
[6] https://www.immersivehealth.io
[7] https://www.zs.com/insights/technology-regulatory-intelligence-accelerate-drug-development
[8] https://www.statnews.com/2024/02/20/health-ai-regulation-tech-startups-compliance/
[9] https://www.ycombinator.com/companies/regology
[10] https://www.greyb.com/blog/ai-drug-discovery-startups/
[11] https://iktos.ai
[12] https://www.regulatorypharmanet.com/the-impact-of-ai-on-pharmaceutical-regulatory-processes/ 
[13] https://www.xcubelabs.com/blog/generative-ai-in-healthcare-developing-customized-solutions-with-neural-networks/ 
[14] https://www.ai-startups.org/top/medicine/ 
[15] https://www.solutelabs.com/blog/top-ai-healthcare-startups 
[16] https://www.keragon.com/blog/healthcare-ai-companies 
[17]https://www.linkedin.com/pulse/20-gen-ai-healthcare-startups-shaping-future-recap-from-renee-yao-q7lkc 
[18]https://www.beckershospitalreview.com/digital-health/14-fastest-growing-generative-ai-startups.html