Intelligencia AI’s Post

The future of pharma is here! We’re sharing our thoughts on AI, health tech, and data trends for 2025—let us know what you think! #Pharma2025 #AIinHealthcare #HealthTech #DataTrends #FutureOfMedicine

AI in Clinical Trials: Beyond the Buzzword AI is not just a buzzword; it’s transforming every aspect of our lives, including clinical trials and drug development. I recently came across a couple of articles that explore this topic. The takeaways are intriguing. While AI's primary application is currently in recruitment, its potential impact on trial design and analysis is significant. Additionally, the use of AI in preclinical activities, especially molecule identification, is gaining traction, with major pharma companies investing in AI tech (like AstraZeneca and BenevolentAI, or Takeda and Prometheus), making substantial investments. However, challenges in applying AI arise not only from the technology itself, such as inherent biases in algorithms, but also from ethical concerns regarding data access. Most importantly, the lack of alignment between the US FDA and EU EMA regulatory frameworks complicates global clinical trial design and execution. Take a look at these two articles for inspiration—the future is now. https://lnkd.in/ds2m2fAx https://lnkd.in/djhnxbtR #AIHealth #ClinicalTrials #DrugDevelopment #Recruitment #EthicsinAI #AI

Do you want to get a headstart on successfully implementing AI in your pharma or healthcare company? 🚀 We will share our article series "Successfully Implementing AI in Pharma" over the next four weeks. But if you want to get ahead of the curve, you can get the full white paper "Successfully Implementing AI in Pharma" right here - All you need to do is submit your details at the bottom of this page: https://lnkd.in/eENe-qyd In this white paper, we will be sharing our perspective and approach on how to succeed with your AI initiatives, drawing on our experiences assisting leading pharmaceutical companies not only in exploring AI as a trendy must-have but also in harnessing its full potential, achieving tangible benefits, and becoming industry frontrunners in the AI landscape 📈

Everyone knows AI is increasing in popularity and use cases. Interested in how we're applying it with a Cognitive Lens at BioVid? Join our webinar next week to learn more about our innovative approach to incorporating AI into our healthcare market research strategies.

PYMNTS : 'Google’s Drug Discovery LLM Signals Shift Toward Industry-Specific Approach' Adnan Masood, PhD., chief AI architect at UST, quoted Google DeepMind has developed an AI model to predict key properties of potential drugs, aiming to accelerate pharmaceutical research. The new Tx-LLM (Therapeutic Large Language Model) model exemplifies a shift toward specialized artificial intelligence tools for specific industries. This targeted approach could prove more valuable than general-purpose AI in addressing complex commercial challenges. “Industry-specific AI models are fundamentally reshaping business operations by leveraging the nuances of individual industries,” Adnan Masood, PhD., chief AI architect at UST, told PYMNTS. But pharma is not the only industry feeling the impact. Fine-tuning could help factories get smarter, too. “Manufacturing leverages custom AI to predict equipment failures and optimize production lines through real-time analysis of supply chain dynamics, energy costs, and market demand,” Masood said. This means less downtime, more efficient production and lower consumer costs. Masood calls this cross-pollination of AI techniques “algorithmic knowledge transfer.” For example, “An AI system developed for optimizing logistics in the eCommerce sector can be adapted to streamline patient flow in healthcare systems, breaking down traditional silos and fostering an innovation ecosystem that benefits multiple industries.” Read more: https://lnkd.in/gz3P6Pgj Krishna Sudheendra Manu Gopinath Alexander Varghese Leslie Schultz Vijay Padmanabhan Sunil Balakrishnan Niranjan Ramsunder Tony Velleca Praveen Prabhakaran Sajesh Gopinath Yuval Wollman Colleen Doherty Kavita Kurup Krishna Kishore Ankarboina David Berney Krishna Prasad Christopher Loughlin Youssef Mogadam Vivianne Farmer Vinay Kumar Vijayakumar Neha Misri Merrick Laravea Roshni Das Kailash Attal Kaushal Tripathi #UST #PublicRelations #MediaRelations #PR #CorporateCommunications

💡 Unpacking AI's Complexities in Pharma — In the first article of our three-part series, we delve into the obstacles AI faces in drug discovery. Read about the pivotal shifts needed for advancement. https://bit.ly/4cWo5Sc

Our human in the loop AI, combined with manual expert curation process, allows us to save you time and money by giving you access to high quality data that is Machine Learning ready. Data scientists can spend 35-80% of their time on data cleaning. Think about what you could accomplish without all that time spent cleaning

AI in Drug Discovery Strategic Intelligence Research Report - GlobeNewswire: Artificial intelligence (AI) and big data are at the forefront of driving innovation, from enhancing drug discovery to optimizing clinical trial ... #bigdata #cdo #cto

This is always a hot topic amongst DrugBankers. 🔥🔥 We see first hand the urgency happening with implementing AI and ML within early-stage drug discovery research. We also see the insurmountable pressure R&D is under with massive budget cuts in 2023, limited resourcing/staffing and being faced with steep competition in most therapeutic areas. There is a lot of potential for AI to completely change this side of the pharmaceutical industry but it’s a very large mountain to climb. It’s an interesting time to see which companies get there faster than others. Take a look at this article from Alexa Constantinides McCarron which talks about the current challenges we see in implementing AI and ML within drug discovery/ repurposing. #drugdiscovery #generativeai #bioinformatics #drugrepurposing

As we all know, there has been a lot of hype about AI and drug development, with what some consider outlandish claims. Below is a link from Drug Discovery Today that takes what (as far as I know of) a first look at the impact of AI on probability of success. The data is sparse (24 completed Phase 1 studies, and 10 completed Phase 1 studies), but the early data points to what could be meaningfully higher Phase 1 success probabilities (although most of the studies were in precedented pathways which could sway the data), but Phase 2 probabilities pretty much in line with historical benchmarks It will be interesting to see over the next decade if AI will truly increase success probabilities (especially as AI capabilities improve), or go the way of high-throughput screening (remember that?) https://lnkd.in/gMp9XtrB