IXICO eyes accelerated business development activity as it hires new CCO Proactive uses images sourced from Shutterstock IXICO PLC has announced the appointment of Tanya Voloshen as its chief commercial officer, in a newly created role, as the firm seeks to accelerate business development activity and broaden its US footprint. The AIM-listed neuroscience imaging and biomarker analytics group said Voloshen will be based in Boston, Massachusetts. Voloshen joins the AIM-listed neuroscience imaging and biom ...

Summary of Nvidia's Presentation at J.P. Morgan's 44th Annual Healthcare Conference Company Overview - **Company**: Nvidia - **Industry**: Healthcare and AI Semiconductors - **Key Speaker**: Kimberly Powell, Vice President and General Manager of Healthcare at Nvidia Core Points and Arguments 1. **Shift in Healthcare Technology**: Nvidia is experiencing a once-in-a-generation platform shift in the healthcare industry, with accelerated computing and AI becoming integral to healthcare solutions [5][41] 2. **Agentic AI Deployment**: The deployment of agentic AI in healthcare is occurring faster than in any other industry, with significant advancements in robotics and simulation [6][13] 3. **Open Models and Innovation**: Open models are crucial for innovation, with 80% of startups built on these models. Nvidia became the largest contributor to open-source AI in 2025, with over 650 language models and 250 datasets [8][9] 4. **Healthcare Market Size**: The U.S. healthcare market is valued at $4.9 trillion, and AI is being deployed at an unprecedented scale to address acute challenges in the industry [14] 5. **AI as Digital Coworkers**: Healthcare systems are beginning to hire AI systems as digital coworkers to alleviate the shortage of healthcare professionals, projected to be tens of millions by 2030 [13][14] 6. **Return on Investment (ROI)**: The cost of AI inference has decreased significantly, making it viable for mass-market healthcare adoption. For example, the cost of running an agent has dropped from $1 to $0.01 [48] 7. **Impact on Clinical Development**: AI is transforming clinical development processes, making them less labor-intensive and more efficient. Companies like ConcertAI and Cytoreason are leveraging AI for better planning and execution of clinical trials [18][19] 8. **Partnerships and Collaborations**: Nvidia is collaborating with companies like Thermo Fisher to build AI infrastructure for labs, enhancing the quality and throughput of scientific experiments [26][27] 9. **Future of Drug Discovery**: The integration of AI in drug discovery is expected to reinvent the $300 billion R&D industry, with AI-driven models accelerating the process [23][32] 10. **Investment in AI Infrastructure**: Nvidia announced a $1 billion investment over five years in partnership with Lilly to co-innovate in AI lab infrastructure, aiming to flip the current lab-to-compute ratio from 90-10 to a more compute-centric model [37][38] Additional Important Content - **AI in Lab Automation**: AI agents are being developed to autonomously run experiments and analyze results in real-time, significantly reducing manual work and increasing data quality [25][27] - **Emerging AI Science Companies**: New companies are emerging that focus on AI-driven scientific research, utilizing Nvidia's platforms to enhance their capabilities [20][36] - **Global AI Infrastructure**: Nvidia's technology is being integrated into public clouds worldwide, with expectations that every country will develop its own AI healthcare infrastructure [51][52] - **Democratization of AI**: The accessibility of AI tools and models is enabling scientists to become AI researchers, fostering a new paradigm in scientific discovery [44][46] This summary encapsulates the key insights and developments presented by Nvidia at the conference, highlighting the transformative role of AI in healthcare and the company's strategic initiatives to lead this change.

Summary of Clinical Trials and AI Integration Industry Overview - The focus is on the clinical trials industry, which is facing challenges related to cost and efficiency, with a growing interest in integrating artificial intelligence (AI) to improve processes [1][8][9]. Key Points and Arguments Challenges in Clinical Trials - Clinical trials are costly and inefficient, with costs exceeding $2.5 billion for drug development [4]. - A study found that 54% of phase 3 trials fail, primarily due to insufficient efficacy (57%) and safety concerns (17%) [3]. - Recruitment issues are significant, with only 31% of UK trials meeting enrollment targets [4]. - From 2012 to 2022, R&D spending increased by 44%, yet the number of novel drug approvals remained flat, leading to higher average drug development costs [5]. - As of 2024, it is estimated that up to 80% of clinical trials exceed their forecasted timelines [5]. Potential of AI in Clinical Trials - AI is believed to have the potential to enhance various stages of clinical trials, including trial design, site selection, recruitment, monitoring, and analysis [9][10]. - AI can analyze real-world data (RWD) to improve trial design by refining patient eligibility criteria and optimizing endpoints [12]. - In site selection, AI can predict enrollment numbers and identify optimal trial locations, thus reducing costs and improving speed [14]. - For recruitment, AI can create comprehensive patient profiles from diverse data sources, improving eligibility matching and targeting underrepresented populations [16]. - AI can enhance monitoring by tracking site performance metrics in real-time, allowing for early identification of operational risks [18]. - In the analysis phase, AI can accelerate data cleaning and identify treatment effects that traditional methods may miss [20]. Companies Utilizing AI in Clinical Trials - A variety of companies are integrating AI into clinical trials, categorized into three groups: 1. **Full-fledged CROs**: Companies like IQVIA, Icon, and Fortrea are developing AI tools to enhance their internal trial processes [24]. 2. **Health-tech Companies**: Firms such as Medidata, ConcertAI, and Flatiron Health offer software platforms that utilize AI for various trial stages [24]. 3. **Diagnostics Companies**: Tempus and Caris Life Science focus on in-house sequencing and real-time patient matching [24]. Data and Partnerships - High-quality data is crucial for building effective AI models, with companies emphasizing the size and quality of their datasets [30][31]. - Partnerships are essential for enhancing datasets and improving AI models, with companies collaborating to combine resources and expertise [37][39]. Other Important Insights - The clinical trial industry is in the early stages of AI integration, with significant potential for transformation but also challenges due to regulatory complexities [39][40]. - The need for innovation in clinical trials is critical, whether through AI or other means, to address rising costs and operational inefficiencies [40]. This summary encapsulates the current state of the clinical trials industry, the challenges it faces, the potential role of AI, and the companies leading the charge in this transformation.

Dublin, May 14, 2025 (GLOBE NEWSWIRE) -- The "Artificial Intelligence (AI) in Clinical Trials - Market Insights, Competitive Landscape, and Market Forecast - 2032" has been added to ResearchAndMarkets.com's offering. The Artificial Intelligence (AI) in clinical trials market is projected to experience robust growth from USD 1.35 billion in 2024 to USD 3.33 billion by 2032, reflecting a compound annual growth rate (CAGR) of 12.04% from 2025 to 2032. This expansion is largely driven by the increasing global b ...