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NEW YORK, Oct. 30, 2024 /PRNewswire/ -- IVX Health, a national provider of outpatient infusion and injection centers, today announced the opening of two additional infusion centers in New Jersey. Conveniently located in the greater North Jersey area in Clifton and Paramus, these locations join existing centers in the South Jersey region in Cherry Hill and Mount Laurel. IVX Health's New Jersey infusion centers add to their swiftly expanding footprint across the Northeast region. IVX currently operates 14 locations across Connecticut, Delaware, New Jersey, and Pennsylvania — with over 100 infusion centers in 15 states across the country. All IVX Health centers feature private suites, walk-up parking, and flexible appointment scheduling – with availability on evenings and Saturdays. Patients living with complex chronic conditions such as multiple sclerosis, Crohn's disease, rheumatoid arthritis, and many more receive world-class care from expertly trained nurse practitioners and registered nurses specializing in infusion and injection therapy. "After my multiple sclerosis diagnosis, my neurologist recommended that I begin infusion treatment at IVX, and life changed after that," said Cam Wiggins, current biologic patient at IVX Health. "I have a great team. They really care about me and want me to do well. It never really feels like I am there to get an infusion." Learn more about Cam's story here. IVX Health's forward-thinking approach is purposefully designed to provide top-tier care in a comfortable, secure, and private environment. With an unmatched 1:3 nurse-to-patient ratio, IVX ensures that every patient gets the personalized care and attention they deserve. All IVX locations adhere to the same safety standards and protocols as those found in traditional hospital settings. "We are excited to continue growing IVX Health in New Jersey and across the Northeast," noted Jason Beasley, vice president with IVX Health. "Referring specialists choose IVX Health because of our commitment to world-class clinical excellence for patients and our teams' ability to make the referral process hassle-free. With over 4,000 5-star Google reviews, we know patients love IVX as well – and for that, we're truly grateful." IVX Health's patient and provider centered approach to infusion therapy has played a crucial role in driving the company's rapid growth and success. With over 100 centers across the United States and more than 70 therapies on its active formulary, IVX Health's ultimate goal is to positively impact the lives of as many patients as possible. For more information on IVX Health and its services, visit . IVX Health New Jersey IVX Health Cherry Hill 818 Haddonfield Rd, Suite D Cherry Hill, NJ 08002 Phone: (640) 230-7338 Fax: (856) 519-5309 IVX Health Clifton 225 Allwood Rd, Unit 8 Clifton, NJ 07012 Phone: (973) 200-1886 Fax: (551) 227-2823 IVX Health Mount Laurel 1203 Nixon Dr Moorestown, NJ 08057 Phone: (856) 519-5308 Fax: (856) 519-5309 IVX Health Paramus 501 N Route 17, Unit 10 Paramus, NJ 07652 Phone: (551) 227-2824 Fax: (551) 227-2823 About IVX Health IVX Health is a national provider of infusion and injection therapy for those with complex chronic conditions such as rheumatoid arthritis, Crohn's disease, multiple sclerosis, and other conditions. IVX Health's national footprint of outpatient infusion centers are conveniently located in the communities where patients live and work. IVX Health's experience-first model includes amenities such as flexible appointment scheduling with evening and weekend availability, guaranteed private suites, high-speed Wi-Fi, flat screen TVs with streaming TV and movies, and comfortable recliners with room for guests. For more information on IVX Health and its existing locations, visit . SOURCE IVX Health WANT YOUR COMPANY'S NEWS FEATURED ON PRNEWSWIRE.COM? 440k+ Newsrooms & Influencers 9k+ Digital Media Outlets 270k+ Journalists Opted In GET STARTED

Iambic Therapeutics has used its AI models to build a pipeline and put its first drug candidate in the clinic earlier this year. Now, the San Diego company is debuting a model with even bolder hopes for what AI can tackle in biotech. On Tuesday, Iambic announced Enchant, a transformer-based model to make clinical predictions. The model is trained on a wide array of preclinical and research data, along with a small amount of clinical data. In a white paper , the biotech said the model exceeded state-of-the-art performance in predicting certain properties like the half-life of a compound in the human body. Its leaders believe the model will excel at making many more predictions that ultimately determine how good an experimental drug will be in humans. “We’ve arrived at a really exciting point with this technology,” Iambic’s co-founder and chief technology officer Fred Manby told Endpoints News . “This demonstration that, yes, you get better at clinical predictions by training on more preclinical data really feels like a moment where something qualitative has changed about the way you can leverage AI in drug discovery.” Iambic’s leaders said Enchant aims to “break the wall” between preclinical and clinical research. The biotech already uses the model’s predictions for properties like half-life or solubility of compounds when selecting preclinical drug candidates. Other early efforts in this space include Google DeepMind’s Tx-LLM , Insilico’s nach0 , and OpenAI’s ChatGPT . Iambic CEO Tom Miller said Enchant’s performance outranks these other models, in a different way than how DeepMind led the competitive space in protein-structure prediction with AlphaFold. He described Enchant’s performance in making clinical predictions as a “zero-to-one situation” for the AI bio field. “The state-of-the-art has nothing to do with DeepMind,” Miller said. “This is a situation where that is just one among a bunch of other methods that aren’t very good and aren’t surpassing the state-of-the-art.” Specifically, Enchant beat the publicly available state-of-the-art performance for predicting the half-life of compounds. On a zero-to-one scale measuring how its predictions match reality, Iambic’s model scored 0.73, showing a moderate-to-strong correlation. The previous record was a 0.58 from a model developed by a research team at a small San Diego biotech called Expert Systems. Iambic started working on Enchant about a year ago, feeding it a wide variety of data, from PubMed and bioRxiv to the Protein Data Bank and Iambic’s own laboratory data. Enchant’s design — a multimodal transformer — mirrors ChatGPT in relying on the AI to make connections across seemingly disparate datasets. Iambic’s leaders believe doing so could better connect lab research on drugs to how they actually perform in the human body — a critical challenge for boosting clinical success rates. Iambic described parts of its performance, like the half-life metric, in a blog post, which was not published in a peer-reviewed journal. Manby said he expects future publications on Enchant and referenced a paper presented in July at a machine learning conference describing an earlier version of the model. While the new white paper provides some examples, it’s hard to tell if those are hand-picked measures of where it shines, or if Enchant is on track to predicting a wide range of clinical properties. Its leaders said they are confident it can beat state-of-the-art performance on many measures beyond half-life. “We’re optimistic we’ll be able to reproduce that across a whole slew of different clinical endpoints,” Manby said.

From left to right: Tommy Ibrahim, president and CEO of Sanford Health Plan; and Scott Gottlieb, former commissioner of the FDA and partner at New Enterprise Associates Large language models (LLMs) are poised to become a much bigger part of doctors’ clinical workflows, according to Scott Gottlieb, who served as commissioner of the FDA during the Trump administration. He shared this take on Tuesday at the 3rd Annual Summit on the Future of Rural Health Care in Sioux Falls, South Dakota. He was being interviewed on stage by Tommy Ibrahim, president and CEO of Sanford Health Plan. Ibrahim highlighted research that Gottlieb recently conducted with the American Enterprise Institute, a center-right/right-wing think tank. The study, which was released this summer, put five LLMs to the test — Open AI’s ChatGPT-4o, Google’s Gemini Advanced, Anthropic’s Claude 3.5, xAI’s Grok and Llama’s HuggingChat. presented by Health IT Accelerating Claim Processing: Strategies to Shorten the Life of a Claim These strategies and practices can significantly shorten the life cycle of claims, leading to quicker resolutions and improved financial outcomes. By Greenway Health The research team asked these LLMs 50 questions from the most challenging installment of the three-part U.S. Medical Licensing Examination. The AI models did quite well. Open AI’s ChatGPT-4o had the best performance with an accuracy rate of 98%. Llama’s HuggingChat had the worst accuracy rate at 66%, and the rest of the LLMs had an accuracy rate in the 84-90% range. The U.S. Medical Licensing Examination requires candidates to answer about 60% of questions correctly. The average passing score for the exam has historically hovered around 75%. Based on these study results, as well as the level of AI innovation Gottlieb is seeing out there in his role as partner at New Enterprise Associates, he is optimistic about the role that LLMs can play in the future of healthcare. But he doesn’t think this potential is being realized yet. Sponsored Post The Future of Hospitals and Pharma Companies Will Depend on Strength of Healthcare Analytics Insights PurpleLab® stands out from others in this sector by providing its data analytics services to several different groups of users across healthcare and pharma companies. By Stephanie Baum “I think we’re at the point right now that if you’re handling a complex case and you’re not using [LLMs], you probably should be. I think most physicians probably aren’t, because there’s not a good option within a health system setting where you can do it in a HIPAA-compliant fashion. There’s not a lot of systems that have deployed local instances of these chatbots,” Gottlieb explained. He also mentioned research that he is currently conducting to further test LLMs’ medical capabilities. Gottlieb and his research team are currently feeding ChatGPT-4o clinical vignettes from the New England Journal of Medicine. Every issue, the journal includes a vignette of a difficult-to-pin down clinical case and gives the reader a multiple choice-style selection of what the case might be — answers are revealed in the next issue. There are 350 examples of the journal’s clinical vignettes online, and Gottlieb and his team are feeding them all to ChatGPT-4o. “So far, it’s getting 100% — and it explains how it arrived at the diagnosis. It takes things from the clinical vignette and explains why those clues were the key clues in helping to arrive at this diagnosis. The clinical reasoning is really profound,” he declared. Gottlieb asked the audience to imagine a medical resident receiving a call for a complex case late at night. To him, it’s obvious that the resident should be able to use an LLM to help them more quickly reach a differential diagnosis. “I mean, you almost have to be doing it,” Gottlieb remarked. LLMs for clinical decision support haven’t been deployed at scale yet, though, he noted. These tools aren’t easily accessible for most doctors. To use LLMs for diagnostic support, health systems must either create their own models or modify existing ones by layering on local health data and adding patient data privacy controls — and that takes time and resources, Gottlieb explained. “But I think very soon everyone is going to have to think about how to deploy this point of care,” he said.