Harrison.ai

Committed to a more accurate and accessible healthcare system? Meet our AI model - Harrison.rad.1. Designed to enhance healthcare accuracy and accessibility, our radiology-focused AI solution, Harrison.rad.1, has been shared with select healthcare professionals and regulators worldwide. Why? We want to foster collaborative discussions around the responsible and ethical integration of AI within the healthcare landscape. Our goal is to shape a future where this technology empowers safe, ethical, and responsible healthcare advancements. Learn more about Harrison.rad.1 and its potential to revolutionise healthcare here: https://lnkd.in/gx27bNxM

AI for efficiency, humanity for care. Computers can't replace the human touch. Harrison.rad.1 helps doctors spend less time on reports and more time with patients. A more sustainable healthcare system starts here. Recently, Dr. Aengus Tran and Dimitry Tran spoke with SmartCompany to discuss how AI can help alleviate the pressure of doctors' workloads. Read more here: https://lnkd.in/gE-wjnNs

In radiology, GenAI holds the potential to stave off radiologist burnout and realise greater personalisation of patient care. We spoke to Jeff Chang co-founder at Rad AI, Emily Lewis, MS, CPDHTS, CCRP, AI, and innovation lead at biopharmaceutical company UCB and Harrison.ai CEO Dr Dr Aengus Tran to learn more. Click here for the full article - https://lnkd.in/drFaJpe8 #radiology #genai #aiinhealthcare #medicaldevice #medtech

We're proud to announce that our co-founder and CEO Dr Aengus Tran has been named in the prestigious list of Top 100 Innovators 2024: The Next Wave in The Australian. It's an awesome achievement, especially considering that Harrison.ai’s technology has powered the care of over 6 million patients worldwide in the last few years. Congratulations to the extremely talented and massively inspiring names who have made it to the list! Here's to game-changing innovation! helen trinca Jared Lynch

“For radiology, the remedy to the issue of potentially unreliable or plainly limited data to work upon exists in pre-trained, radiology-specific foundation models such as Australia-based Harrison.ai’s dialogue-based LLM, Harrison.rad.1.”   Our CEO, Dr Aengus Tran, recently discussed with Medical Device Network the game-changing role of GenAI in radiology and why Harrison.ai is leading the charge.    With radiologist shortages globally and increasing pressure on healthcare systems, technologies such as Harrison.rad.1 tech could help in delivering faster, diagnostic support, easing the burden on clinicians while ensuring that patients get the care they need.   You can find the full piece here: https://lnkd.in/gvEfZZUj

We are happy to announce that Harrison.ai has been recognized as the winner of the NSW Development Impact Award at the Export Council of Australia annual event. This honour reflects our ongoing commitment to driving impactful change through AI innovation, particularly in healthcare. A huge thank you to our incredible team, partners, and supporters who have helped us push the boundaries of what’s possible with AI. Together, we are scaling the future of healthcare and making a global impact. Congratulations to all the other winners and finalists! Premier's Department NSW

See Harrison.rad.1 in action with radiology expert Dr. Andrew Makmur. Ready to see the future of radiology? Watch Group Chief Technology Officer and Consultant Radiologist at National University Health System (Singapore) Dr. Andrew Makmur interact with Harrison.rad.1 and learn more about its potential. If that’s not enough to satisfy your curiosity, read more about our new technology here: https://lnkd.in/gx27bNxM

AI technology is one of the most exciting, powerful tools ever created. We’ve seen AI transform selfies, craft meticulous emails and even develop cocktail recipes based on emotions - but what about the potential of AI to save and improve lives? Meet Harrison.rad.1 - the world’s most capable multimodal foundational model for medical use. What we’ve seen is that Harrison.rad.1 is outperforming other AI models in radiology tests. Not only is this a testament to the hard work and dedication of the team at Harrison.ai, but the passion to drive collective progress and improve patient outcomes in the medical field. Learn more about Harrison.rad.1, the latest frontier in radiology-specific foundational models: https://lnkd.in/gx27bNxM

We announced our revolutionary multimodal Large Language Model (LLM) Harrison.Rad.1, but what are the potential uses of such technologies? From providing radiology access in underserved areas - to accelerating the development of AI products - learn more about Harrison.rad.1's potential use cases in our technical blog: https://lnkd.in/gfupgBJ3

The latest research from the Mass General Brigham AI team, published in the Journal of the American College of Radiology, shows that the Annalise.ai solution can accurately detect overlooked vertebral fractures (VCF) on chest X-rays, potentially improving lives and saving money for both patients and the healthcare system. VCFs are crucial indicators of serious bone issues. Patients with a history of compression fractures face a five-fold increased risk of recurrence compared to those without. VCFs are often caused by osteoporosis, which can be spotted on chest X-rays, providing an opportunity for early treatment and the prevention of further fractures. However, only about 21% of women aged 50-64 are screened for osteoporosis. Bernardo Bizzo MD, PhD, a radiologist and senior director of Mass General Brigham AI business and  his team highlighted the cost benefits of identifying osteoporosis early. For instance, the osteoporosis drug Alendronate costs just $0.80 per week, while treating a hip fracture was estimated at $50,508 in 2014. One study estimated that preventing secondary fractures could save around $418 per patient. This research found that the  #Annalise Critical Care AI  device could detect vertebral compression fractures with 89.3% accuracy for VCF-positive cases (sensitivity) and 89.2% for negative cases (specificity) in a study of 595 patients. This meets the FDA’s benchmark sensitivity and specificity of 80% for computer-assisted triage devices. Additionally, the study revealed that Annalise.ai could help identify patients with undiagnosed osteoporosis. Only 36.4% of correctly identified vertebral fracture cases had a formal diagnosis of the condition, and just 59.3% had a diagnosis of osteoporosis or osteopenia.  Read more here: https://lnkd.in/g5xX7Yid