Reimagining clinical trials through innovation and technology. In this compilation episode, part 3 of our Health Innovation Series, we explore how Deep6 AI, CEO Wout Brusselaers, Lokavant, CEO Rohit Nambisan, and Science 37, Chief Delivery Officer Darcy Forman, are transforming the way clinical trials are conducted. How is Deep6 AI revolutionizing patient recruitment for clinical trials? Deep6 AI leverages AI and natural language processing to precisely match eligible patients to clinical trials by mining real-world data from electronic medical records (EMRs). This enables faster recruitment, fewer dedicated resources, and de-risking of research efforts for hospitals, life sciences companies, and contract research organizations (CROs). Traditional recruitment methods relied on physicians identifying eligible patients or querying structured data, leading to large pools requiring manual review. Deep6AI ingests all patient data into a cloud platform and uses NLP to create multi-dimensional patient vectors, enabling precise matching by searching a small pool of truly eligible patients. How is Lokavant transforming clinical trial operations? Lokavant was founded to solve operational challenges in clinical trials that existing systems could not handle. Their platform ingests diverse data sources and enables configurable analytics across use cases like site/safety monitoring and enrollment forecasting, leveraging data from over 2,000 studies for predictive analytics. How is Science 37 decentralizing clinical trials globally? Science 37 brings clinical trial access to diverse patient populations through a virtual/decentralized model, leveraging technology to orchestrate remote visits and engagement. Their network of investigators and mobile nurses enables enrollment of underserved communities matching census data demographics. Science 37's "meta-site" virtual site enables remote participation through a single app, overcoming roadblocks to drive an innovative, patient-centric clinical trial delivery model. Patients develop strong rapport with nurses, expressing gratitude for in-home care during trials. Featured Companies: Deep6 AI - Wout Brusselaers, CEO & Founder Website: https://deep6.ai/ Full Episode: Link to episode Lokavant - Rohit Nambisan, CEO Website: https://www.lokavant.com Full Episode: Link to episode Science 37 - Darcy Forman, Chief Delivery Officer Website: www.science37.com Full Episode: Link to episode Get in touch with AWS here to learn how we can help your organization accelerate healthcare innovation. Please take a moment and let us know what you think of the podcast, access our feedback survey here.

In part two of our five part compilation mini-series on health innovation we explore groundbreaking advancements in diagnostics and biomarkers. 1. How is Us2.AI democratizing cardiac care through AI-powered echocardiograms? Us2.AI is revolutionizing cardiac imaging by automating echocardiogram analysis with AI, enabling non-specialists to perform accurate heart scans. This empowers early diagnosis and treatment in underserved areas. Co-founders James Hare and Dr. Carolyn Lam share insights on their AI-Echo software, which is utilized globally in clinical practice settings to improve patient outcomes and expand healthcare access. 2. What is Truvian's innovative approach to disrupting blood diagnostics? Truvian is developing a benchtop system that can provide lab-accurate blood test results in 20 minutes from a small sample, aiming to increase access to routine health screenings. CEO Jay Srinivasan discusses their proprietary approach, which they are seeking FDA clearance and CLIA waiver for, to deliver accessible blood testing from one small blood sample in minutes. 3. How is Sparta Science optimizing human movement and performance? Sparta Science's force plate technology captures detailed movement data to identify injury risks and guide interventions, improving human performance across various conditions and clinical trials. Founder & CEO Dr. Phil Wagner shares insights on their journey from democratizing force plate technology to evolving into a human data platform reshaping the landscape of human measurement. Get in touch with AWS here to learn how we can help your organization accelerate healthcare innovation. Please take a moment and let us know what you think of the podcast, access our feedback survey here. Featured Companies: Us2.AI - James Hare (Co-founder/CEO), Dr. Carolyn Lam (Co-Founder & Non-Exec Head of Medical Affairs) - Company Website, Full Episode Truvian Health - Jay Srinivasan (CEO) - Company Website, Full Episode Sparta Science - Phil Wagner (Founder & CEO) - Company Website, Full Episode

Join Andrei Georgescu (VivoDyne CEO), Serafim Batzoglou (Seer Chief Data Officer), Dave Longo (Ordaōs CEO), Alex Zhavoronkov (Insilico Medicine Founder), and Young Kwon (Alchemab Therapeutics PhD) as they revolutionize drug R&D. Discover how VivoDyne's bioengineering platform is transforming tissue sample research. Learn about Seer's nanoparticle technologies that unveil more proteins than ever. Uncover Ordaōs' innovative mini-proteins reshaping drug development and the impact of AI on discovering therapies for complex diseases. It's a deep dive into the future of medicine!

In this episode of the AWS Health Innovation Podcast, Yin He, Principal Business Development for Healthcare and Life Sciences Startups, interviews Karim Galil, Co-Founder and CEO of Mendel. Mendel harnesses the power of AI to structure unstructured patient data, building comprehensive patient journeys that reconcile conflicting data points and empower healthcare professionals with a more objective, data-driven approach to medicine. How does Mendel unlock the potential of medical data? Mendel.ai aims to structure unstructured clinical data and reconcile conflicting information to generate comprehensive patient journeys. This approach moves medicine towards more objective, data-driven practices while accounting for physician expertise. How does Mendel bridge the divide between technical and non-technical users? Mendel caters to both technical (data analysts) and non-technical (clinicians) users by providing explainable AI systems that foster trust and adoption. Responsible marketing highlighting capabilities and limitations is crucial. What is Mendel's approach to AI in healthcare? Mendel combines deep learning with a knowledge graph representation, ensuring explainability and consistency in medical AI outputs. This paves the way for an "Iron Man and Jarvis" collaborative relationship between physicians and advanced AI systems. What entrepreneurial traits does Karim Galil emphasize? Karim Galil draws inspiration from personal experiences as a physician, emphasizing resilience, passion for problem-solving, and a mindset of continuous improvement as key traits for aspiring entrepreneurs in the healthcare AI space. What is Mendel's vision for indexing medical knowledge? Mendel envisions a future where a company can index and make sense of all medical records, akin to libraries for books and Google for the internet, revolutionizing healthcare delivery through comprehensive data accessibility. Get in touch with AWS here to learn how we can help your organization accelerate healthcare innovation. Please take a moment and let us know what you think of the podcast, access our feedback survey here. To learn more about Mendel, visit their website at mendel.ai.

Pushing the boundaries of human biology, Vivodyne has developed a groundbreaking microfluidic bioengineering platform capable of growing tens of thousands of human tissue samples at scale. Our guest is Andrei Georgescu, the CEO of Vivodyne, whose unique background bridging electrical engineering and bioengineering led him to develop this innovative approach. Hosting the interview is Yin He, Principal Business Development for Healthcare and Life Sciences Startups at AWS, who will explore how Vivodyne's platform combines the principles of organs-on-chips and organoids at high throughput. How does Vivodyne's microfluidic platform revolutionize drug discovery? Vivodyne's microfluidic bioengineering platform enables growing tens of thousands of human tissue samples at scale, providing unprecedented insights into human biology before clinical trials. This approach aims to revolutionize drug discovery success rates by gathering massive human data currently lacking. What is the key innovation behind Vivodyne's approach? Rather than top-down engineering, Vivodyne leverages principles of organs-on-chips and organoids to enable bottom-up self-assembly of complex human tissues at high throughput. This allows studying tissue interactions, systemic effects like immune responses, and linking multiple systems to model integrated physiology. How does Vivodyne's platform align with ethical principles? By producing foundational human data on gene edits, drug metabolism, off-target effects and more, Vivodyne's automated microfluidic platform displaces animal testing facilities while significantly reducing costs. This aligns scientific advancement with ethical principles. What challenge is Vivodyne tackling, and how? Integrating isolated biological knowledge into a holistic, system-level understanding remains extremely challenging. Vivodyne is tackling this immense complexity by combining microfluidics' control with high-throughput human tissue data generation to enable AI modeling of human biology at unprecedented scales. What advice does Vivodyne's CEO have for biotech entrepreneurs? As a biotech entrepreneur, Vivodyne's CEO Andrei Georgescu advises tackling very difficult yet high-impact problems, as the potential upside outweighs incremental approaches despite perceived higher risk. Transformative innovation requires embracing immense challenges. To learn more about Vivodyne's groundbreaking work, visit their website at vivodyne.com. Get in touch with AWS here to learn how we can help your organization accelerate healthcare innovation. Please take a moment and let us know what you think of the podcast, access our feedback survey here.

Imagine being able to design novel drug molecules with the same ease as typing words into a computer, and being given the instructions into how to synthesize that molecule in the lab. That's the incredible potential of Iktos, our guest on today's episode of the AWS Health Innovation Podcast hosted by Alex Merwin, Head of Growth for Healthcare & Life Sciences Startups at AWS, and featuring Yann Gaston-Mathé, the Founder & CEO of Iktos. 1. How is Iktos revolutionizing the field of drug discovery with AI? Iktos' groundbreaking approach harnesses deep generative AI models trained on vast molecular databases to automatically design novel, optimized molecules for drug discovery challenges. This innovative technology promises to accelerate pharmaceutical R&D by rapidly generating promising molecular candidates tailored to specific therapeutic targets. 2. What is the unconventional journey of Iktos' founder, Yann Gaston-Mathé? Yann Gaston-Mathé, Iktos' founder and CEO, followed an atypical path, publishing his first academic paper at over 40 after diverse experiences spanning engineering, biology, consulting, and entrepreneurship before embracing the AI innovation that became Iktos. 3. What is Iktos' integrated AI-robotics platform, and how does it provide a competitive advantage? Iktos developed an integrated AI-robotics platform to automate molecular synthesis and generate real-world data for active learning, recursively improving its AI models' performance through continuous feedback loops, a key competitive advantage. 4. How has Iktos demonstrated the transformative impact of its technology in drug discovery? Leveraging its AI expertise, Iktos successfully optimized drug candidates for a Japanese pharma client, meeting 7-8 out of 8 success criteria within 12 months, demonstrating the transformative potential of its technology in accelerating drug discovery pipelines. 5. How does Iktos foster a culture of multidisciplinary innovation? Iktos cultivates a collaborative culture promoting cross-team knowledge sharing and brainstorming between AI, computational chemistry, and medicinal chemistry experts, fostering multidisciplinary innovation at the convergence of these fields. Learn more about Iktos and their AI-driven drug discovery solutions on their website. Get in touch with AWS here to learn how we can help your organization accelerate healthcare innovation. Please take a moment and let us know what you think of the podcast, access our feedback survey here.

Exploring the intersection of AI and biology, this episode features Jonathan Baptista, Co-Founder and CEO of DeepLife. DeepLife uses machine learning to curate and annotate single-cell RNA-seq datasets, enabling downstream bioinformatics analysis and target identification. Host Alex Merwin from AWS learns all about DeepLife's groundbreaking approach to accelerating drug discovery and precision medicine through AI-driven simulations and generative models. How does DeepLife leverage AI for drug discovery simulations? DeepLife creates digital twins of cells, enabling virtual experimentation and mechanistic modeling to identify potential drug targets without extensive lab testing. Their platform simulates cellular responses to perturbations, providing insights into disease mechanisms and therapeutic opportunities. What inspired Jonathan Baptista's pivot from aerospace to computational biology? Jonathan's background in aerospace engineering inspired applying simulation tools to complex biological systems, inferring "laws" from data using AI/ML techniques instead of defined physics equations. This approach aims to accelerate drug discovery for precision medicine. How does DeepLife map the biomolecular landscape? DeepLife spent years developing pipelines to harmonize and annotate high-quality biomolecular data from public repositories and partners, mapping available datasets to identify therapeutic areas suitable for their AI-driven modeling approach. How does DeepLife leverage generative AI for biomedicine? Leveraging recent advances in generative AI models for text and images, DeepLife improved annotation and simulation of cell states after perturbations, benchmarking and selecting optimal techniques for different drug discovery tasks. What is DeepLife's "collaborative AI" approach? DeepLife's "collaborative AI" approach generates potential biological hypotheses that researchers can validate and explore further, enabling visualization of simulated cells and predicted perturbations to uncover novel mechanistic insights. Learn more about DeepLife here and their mission to build the next generation of system biology. Get in touch with AWS here to learn how we can help your organization accelerate healthcare innovation. Please take a moment and let us know what you think of the podcast, access our feedback survey here.

Transforming how scientists interact with lab equipment and data, LabVoice has developed a hands-free voice assistant tailored to meet the unique needs of scientists working in the lab. Joining us to discuss LabVoice's innovative solution is Steve Soccorso-McCoy, Head of Sales at LabVoice. Our host for this episode is Eric Zimmerman, Principal Healthcare & Life Sciences BD, Venture Capital & Startups at AWS. How does LabVoice streamline lab data drudgery? LabVoice's voice assistant empowers scientists by offloading tedious data tracking and information access tasks. This frees up time for more valuable scientific work, aligning with principles of customer obsession and eliminating undifferentiated heavy lifting in life sciences R&D. What is voice-powered human-lab interaction? The platform provides seamless voice control by integrating capabilities like voice commands, OCR, and barcode scanning. This enables hands-free execution of tasks and data access, enhancing productivity for scientists working at benches or fume hoods. How did LabVoice's visionary founders approach the problem? Co-founders Matt Hahn and Fred Bost envisioned voice tech's potential after consumer successes like Alexa. LabVoice takes an end-user focused approach by closely collaborating with scientists to understand and streamline their unique workflows. How does LabVoice bridge software silos seamlessly? By integrating with popular scientific software vendors, LabVoice bridges gaps between data creation, recall, and capture. This unified data strategy empowers scientists with holistic access across previously siloed systems. What is the process for deploying and adopting LabVoice? While initial technology deployment is fast, full scientist adoption takes 4-6 weeks including change management. However, the compounding productivity benefits across workflows create lasting "aha" moments and workflow transformation. What is LabVoice's vision for the future of voice-powered science? LabVoice envisions a future with seamless vendor interoperability, simplified UIs, automatic data documentation, and ELNs/LIMS evolving into data-centric analysis tools. Voice, AI, AR/VR will further empower scientists' knowledge management and operations. To learn more about LabVoice's innovative voice assistant for scientists, visit their website at https://labvoice.ai/. Get in touch with AWS here to learn how we can help your organization accelerate healthcare innovation. Please take a moment and let us know what you think of the podcast, access our feedback survey here.

Imagine being able to design the perfect drug molecule from scratch, one that could cure diseases with unprecedented efficacy and minimal side effects. This is the ambitious vision driving Ordaōs, a human-enabled machine-driven drug design company focused on biomedical research and drug discovery. In this episode of the AWS Health Innovation Podcast, Yin He, Principal Business Development for Healthcare and Life Sciences Startups, interviews Dave Longo, the CEO of Ordaōs, to explore the cutting-edge world of AI-driven drug discovery. Here's what you'll learn in this fascinating conversation: Rethinking Drug Discovery Paradigms Ordaōs challenges conventional drug discovery approaches, criticizing the "discovery mindset" of hunting for suboptimal drugs and then optimizing them. Instead, they propose designing optimal drug molecules upfront using AI and technology. This aligns with their mission to revolutionize biomedical research and drug discovery through human-enabled machine-driven drug design. Unleashing AI's Potential in Pharma Ordaōs envisions a future where AI revolutionizes drug discovery, from target identification to optimization, by leveraging its ability to analyze vast data and generate novel insights. They aim to reduce the entire drug development timeline, from idea to FDA approval, to four years or less within 15-20 years, driven by AI and ML efficiencies. Bridging Bits and Atoms Ordaōs aims to bring "order of digital bits to the chaos of physical atoms" by applying AI and ML to protein therapeutic design. Their team of "molecular natives" – ML scientists focused on biology or biologists focused on ML – employs a "symphony of models" to create better protein designs. Designing Mini Protein Therapeutics Ordaōs focuses on designing mini proteins (40-80 amino acids) and small antibodies as new therapeutic modalities. Mini proteins bridge the gap between small molecules and antibodies, offering advantages like better tumor penetration, oral delivery potential, and blood-brain barrier crossing over traditional antibodies. Challenging Assumptions, Persisting Through Challenges Ordaōs emphasizes the importance of challenging assumptions and thinking from first principles when starting a new venture, rather than following established dogma. They advise aspiring entrepreneurs to persist through challenges and constantly question the foundations of assumptions made by respected experts in the field. Learn more about Ordaōs and their innovative approach to drug discovery at ordaos.bio. Get in touch with AWS here to learn how we can help your organization accelerate healthcare innovation. Please take a moment and let us know what you think of the podcast, access our feedback survey here.

Imagine a world where healthcare data flows seamlessly across providers, payers, and life sciences companies, enabling truly coordinated and personalized care. That future is rapidly becoming a reality, thanks to companies like CareJourney, our guest on this episode of the AWS Health Innovation Podcast. CareJourney is a healthcare analytics vendor specializing in processing claims data and deriving actionable insights for over 300 million patient lives across ACOs, health systems, payers, life sciences companies, and more. In this conversation, Aneesh Chopra, the President of CareJourney and Mirza Baig, from the AWS Health AI team, leading Architecture & Product join Alex Merwin, Head of Growth for Healthcare & Life Science Startups at AWS, to discuss how CareJourney is leveraging AWS services and FHIR APIs to unlock the promise of value-based care through data integration and analytics. CareJourney's solutions empower healthcare organizations to operate more efficiently, provide higher quality care, and drive better patient outcomes. Here are some of the key topics covered in this episode: Timely Data Access: Enabled proactive interventions through daily FHIR data. Overcame delays in receiving patient information. Fraud Detection Insights: Identified potential Medicare fraud by analyzing claims. Created alert system to prevent paying fraudulent claims. Care Coordination Enablement: Integrated specialty companies into unified care journeys. Connected siloed data sources for coordinated care. Data-Driven Healthcare: Leveraged AWS for data ingestion and analysis. Drove insights and next-best action recommendations. Interoperability Commitment: Adapted to evolving data standards like FHIR. Simplified data sourcing across the healthcare ecosystem. Industry Collaboration Importance: Encouraged debate on cloud platform healthcare functionality. Focused efforts to build data interoperability solutions. Get in touch with AWS here to learn how we can help your organization accelerate healthcare innovation. Please take a moment and let us know what you think of the podcast, access our feedback survey here.