Ely Loew’s Post

Reading an article about Proteomics X AI, and found this introduction to be quite on point; verbatim: "About half of Americans answering a 2020 survey would not get in an AI-driven taxi, and about three-quarters of them believed AI (artificial intelligence) cars were “not ready for primetime” [1]. Whether driving a vehicle or discovering new medicines, trust in AI depends on accumulated community experience and the consequences of errors in specific cases." - It rings true! AI is a superpower, and to folks in healthcare x AI, it is a huge responsibility to use it correctly. Trust is not given, it must be earned. Source of quote: https://lnkd.in/g3xaynR3

OpenAI Day 2/12 Summary in 2 minutes - Reinforcement Fine-Tuning: Customizing AI Models Imagine transforming a general AI model into a domain-specific expert with just a few dozen examples. That's exactly what OpenAI's new Reinforcement Fine-Tuning (RFT) is making possible! 🧬 One Example: Rare Disease Research In a groundbreaking demonstration, researchers used RFT to enhance AI's ability to diagnose rare genetic diseases. By training the O1 mini model on just 1,100 medical case reports, they created an AI assistant that can: 1) Analyze patient symptoms 2) Identify potential causative genes 3) Provide reasoning for its predictions The results? The fine-tuned model improved gene prediction accuracy: Base model: 17.7% top accuracy Fine-tuned model: 31% top accuracy Key Takeaways: 1) Minimal training data required 2) Dramatically improved domain-specific performance 3) Potential to revolutionize fields like healthcare, law, and scientific research Quick Summary: The future of AI isn't about more data—it's about smarter, more focused learning! 🧠✨ #ArtificialIntelligence #MachineLearning #Healthcare #Innovation

GenBio AI Introduces Digital Organism Model Key Points 👇 ❶ GenBio AI introduces the first AI-driven digital organism, linking DNA and cells, speeding drug research and personalizing care. ❷ Six connected models let scientists simulate complex biology, boosting understanding and tackling disease. ❸ This all-in-one approach speeds discoveries, reshaping research and turning breakthroughs into real solutions. "GenBio will usher in a new era of medical and life science - through a paradigm shift powered by next-generation Generative AI technology beyond what has already brought us disruptive results such as ChatGPT..." - Professor Eric Xing, Co-Founder and Chief Scientist of GenBio AI #genbio #ArtificialIntelligence

MIT researchers have used deep learning to discover a new class of antibiotics that can effectively combat drug-resistant MRSA bacteria, offering hope for improved treatments with minimal toxicity. Find out more ⬇️ #gmdpacademy #MRSA #antibiotics #AI #deeplearning #medicalaffairs #medicinesdevelopment

Generative AI and life science One of the most promising use cases of generative AI is to accelerate drug discovery and research. Generative AI uses models to create novel protein sequences with specific properties for designing antibodies, enzymes, vaccines, and gene therapy. Healthcare and life sciences companies can use generative models to design synthetic gene sequences for applications in synthetic biology and metabolic engineering. For example, they can create new biosynthetic pathways or optimize gene expression for biomanufacturing purposes. Lastly, generative AI can be used to create synthetic patient and healthcare data. This is useful to train AI models, simulate clinical trials, or study rare diseases without access to large real-world datasets. #Healthcare #LifeSciences #GenerativeAI #DrugDiscovery #Research #Innovation Resource:

In all the hype around generative AI one can forget that it is only one part of the transformation we are going through. AlphaFold from Google DeepMind is transforming how we explore genotypes and phenotypes. https://lnkd.in/ggnCa4K4? #AI #AlphaFold #syntheticbiology

Friday Tech Insights: Should we be embracing AI in clinical trials? Artificial Intelligence is revolutionizing the clinical trial landscape, offering opportunities to enhance efficiency, accuracy, and patient outcomes. AI algorithms analyze data to identify drug candidates quickly, accelerating the discovery phase. With the potential to streamline participant recruitment, AI tools can process complex datasets efficiently and provide real-time patient monitoring, improving outcomes. While AI presents benefits, ethical and regulatory challenges must be addressed. Transparency in AI algorithms is crucial for trust and compliance. The industry must collaborate with regulatory bodies to develop guidelines for safe AI use in clinical trials. Discover more about AI's impact in clinical trials here: https://lnkd.in/eUGcs6gA #AI #ClinicalTrials #AIinHealthcare #DrugDiscovery #PatientRecruitment #MedicalInnovation

The following article is curated and summarized by ChatGPT: Researchers at the University of Cambridge have used AI to speed up finding new therapies for Parkinson's disease, making the process ten times faster and much more cost-effective. Their method, which leverages machine learning to screen millions of compounds for potential drug candidates, has shown that AI can dramatically increase the optimization rate of compounds that inhibit the protein clumping associated with Parkinson’s. The advanced AI system iteratively improves its predictions, yielding more potent and diverse compounds, with some showing promising activity even at much lower doses. This pioneering approach could revolutionize drug discovery for various diseases. #GenerativeAI #AIinDrugDiscovery #ParkinsonsResearch #MachineLearningHealthcare Read the full article: https://lnkd.in/d7BMqMDM

AI-powered drug discovery debuts a new antibiotic. An innovative milestone in AI-powered drug discovery emerges with the introduction of a new antibiotic – the first in over 60 years! Given the escalating threat of antibiotic resistance, the imperative for inventive solutions becomes increasingly evident. Utilizing deep learning, researchers successfully identified a promising antibiotic, marking a significant advancement in AI-powered drug discovery. The Collins Lab's deep learning model meticulously screened millions of compounds, pinpointing several effective ones against stubborn infections such as MRSA and VRE in mouse trials. This groundbreaking achievement exemplifies the positive impact of AI: 👉🏻 Streamlining the antibiotic discovery process, potentially reducing the timeline from a decade-plus to mere hours. 👉🏻 Providing a ray of hope by systematically extracting valuable insights from datasets far beyond human capacity. This research underscores the monumental role AI is poised to play in the future of drug discovery, particularly in combating antibiotic resistance rates. #artificialintelligence #ai #aiautomation

How UT researchers are using AI to create an Alzheimer's drug Lately, it seems like artificial intelligence (AI) is everywhere. When we shop online, to make music and even to chat. Now, researchers at the University of Texas at Austin are using it to help speed up the production of medicine used to treat Alzheimer’s disease. “AI will drastically accelerate our shots on goals and help us get things over the fence,” said Daniel Diaz, a UT postgrad Ph.D. student who now leads the Deep Proteins group for the Institute for Foundations of Machine Learning. Read the full article here: https://bit.ly/49vUr32