iCAN - Digital Precision Cancer Medicine’s Post

I am honored to join the Editorial Board of Scientific Data (Nature)📊. This opportunity allows me to actively contribute to advancing open science, promoting high-quality scientific data, and exploring the intersection of AI and data-driven research 🔍. A big thank you to the Scientific Data team for welcoming me. Ready to embark on this new journey 🚀! Learn more about the journal at Scientific Data https://lnkd.in/dsCtzrDQ. #ScientificData #EditorialBoard #OpenScience #FAIRData #DataScience #AI #MachineLearning #ScientificResearch #Innovation #Nature #BigData

Absolutely fascinating talk on AI and health covering the scope, the technology and the ethics of these new tools.

What will the future of AI-assisted biological discovery look like? As the use of AI in biology and health is gaining momentum, its not hard to imagine a future in which every biological scientist is supported by AI assistants that help synthesise data, design the next experiment, and enrich our mechanistic understanding. This future holds great potential to finally address the many bottlenecks we face in biomedical innovation, and perhaps lead to a world where we are working smarter, not harder in drug discovery. Recently, I had the great opportunity to present some of our recent work in the domain of AI assistants for biological discovery at the Oxford Machine Learning summer school (OxML) in the ML x Bio&Health track - where we covered: - Where can AI make an impact in Drug Discovery and Development? - Computational experimentation as a fast and cost-effective complement to wet-lab research - AI-guided experimental design and optimized exploration - AI-supported synthesis of evidence for causal understanding - In-silico readouts to accelerate and enrich wet-lab experimentation Slides can be found here: https://lnkd.in/dGFRvP5G At the event, I was deeply impressed with the immense knowledge and potential of the summer school attendees that were keen to potentially contribute more to this exciting area in the future. Many thanks for the amazing organising team at AI for Global Goals (Mona Alinejad, D.Phil. (Oxon), Rochelle E., Reza Khorshidi, D.Phil. (Oxon)), Arash Mehrjou for kindly helping with the Q&A, and the team for the outstanding work!

Artificial Intelligence awarded two Nobel Prizes for innovations that will shape the future of medicine - npj Digital Medicine Artificial Intelligence has made a monumental impact on medicine, recently earning two Nobel Prizes. John J. Hopfield and Geoffrey E. Hinton received the Physics prize for their foundational work in machine learning, while Demis Hassabis and John M. Jumper were honored in Chemistry for developing AlphaFold2, a groundbreaking AI tool for protein structure prediction. These advancements not only revolutionize scientific research but also raise ethical concerns regarding AI's role in healthcare. As we embrace AI's potential, we must remain vigilant about its implications for society and patient care. #AIinHealthcare #NobelPrize #MachineLearning #DigitalHealth #EthicsInAI #FutureOfMedicine #HealthTech ai.mediformatica.com #digital #intelligence #medicine #this #artificialintelligence #learning #prediction #technology #algorithm #digitalmedicine #neuralnetworks #machinelearning #digitalhealth #healthit #healthtech #healthcaretechnology @MediFormatica (https://buff.ly/3Zn3WzM)

Curious about AI assistants? Slide Deck from Patrick Schwab GSK Working smarter, not harder in drug development: AI assistants for accelerating biological discovery #ai #drugdiscovery #drugdevelopment #AIassistans

🚀 Revolutionizing Biomolecular Research with AI: The Game-Changing DRAK Framework 🚀 In the ever-evolving field of biomolecular research, precision in designing and predicting molecular structures is crucial. Enter DRAK: Domain-specific Retrieval-Augmented Knowledge for Molecular Sciences. This innovative framework leverages large language models (LLMs) to transform our understanding and prediction of complex molecular structures. 🌟 Highlights: Advanced Techniques: DRAK integrates knowledge graphs and retrieval-augmented methods to enhance the accuracy of LLMs in molecular tasks. Impressive Results: Outperforming existing models in molecule design, reagent prediction, and retrosynthesis prediction. Broad Implications: Accelerating research in drug discovery and bioengineering, leading to faster development of new drugs and materials. DRAK represents a monumental leap forward in applying AI to biomolecular research, offering scientists powerful new tools for groundbreaking innovations. #AI #BiomolecularResearch #DRAKFramework #ScientificInnovation #DrugDiscovery #Bioengineering 🔗 Read the full article here 👇

Large Language Models could play a big role in reducing the burnout in healthcare industry where low risk tasks can be offloaded to these models. These presentations show the ongoing research in developing the new Foundational Models are evolving. Thanks to presenters also talk about the risks, accountability and share history of development of the models that are evaluated in the healthcare domain. Recordings from AIMI Symposium are available here : https://lnkd.in/gg6csf25 #stanfordaimi #healthcare Stanford University School of Medicine Stanford Center for Artificial Intelligence in Medicine and Imaging (AIMI)

I just received my certificate from Oxford Machine Learning Summer School. I want to thank the organizers, AI for Global Goals, for the opportunity to join this amazing experience and meet so many talented researchers and engineers. Even though the presentations were mainly aimed at current PhD students, I believe that attending both the Healthcare and Representation Learning & Generative AI events gave me a lot of valuable exposure to possible future research topics. #OxML2024 #artificialintelligence #GenAI #medicine

At CloudWalk, our AI journey extends to the post-AGI horizon, exploring less obvious territories like consciousness and artificial life. I believe that the convergence between art, intelligence, consciousness, and nature will allow us to better understand the unfiltered, divergent nature of language models' latent space - a reality that will soon be omnipresent. This imminent future demands deep and multifaceted analysis. Art, in parallel with science, serves as an instrument for exploring these new frontiers. To navigate what's to come, we need to embrace diverse perspectives and strategies for understanding. And, with that, we launch our first Artistic and Research Residency: https://lnkd.in/d3B-Rguj Please help this post reach the unconventional pioneers minds that will transform the future.