artificial intelligence Archives - Medical Design Sourcing

Navigating the intersection of technology and human expertise in life sciences

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In this age of rapidly evolving technologies that fundamentally shift the way businesses operate, such as large language models (LLMs) and natural language processing (NLP), organizations are quickly realizing that success extends beyond innovative technology solutions alone. While these technologies offer immense potential in terms of innovation, data insights and operational efficiency gains, constraints exist in areas such as meeting regulatory requirements, managing data availability/ date volume/ data congruence and, in some cases, ensuring commercial viability. These limitations must be addressed for successful adoption across the healthcare industry and for implementation of such tools into broad based quality management systems and product solutions.

True success hinges on the effective implementation and strategic utilization of technology, guided by human expertise and supported by well-designe…

Demystifying deep learning: An accessible introduction to neural networks in health research and epidemiology

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As machine learning and deep learning technologies advance thanks to advances in computation, algorithms and data availability, the possibilities of the technology continue to expand in medicine. While these AI-driven approaches have real potential, such systems demand large volumes of representative data, careful privacy and security scrutiny and thoughtful long-term strategic planning. In this Q&A, Kathryn Rough, associate director of the Center for Advanced Evidence Generation at IQVIA, discusses the impact of deep learning on healthcare delivery and recommends steps to take during the design, training, evaluation and deployment phases to increase the likelihood that these models will be safe, effective and ethical when trained on real-world health data. Rough also explores the role of epidemiologists in evaluating these technologies as part of multidisciplinary teams and provides advice for health…

After Intuitive, Shockwave and Avail, Daniel Hawkins is now CEO at AI-powered MRI software startup Vista.ai

Former Avail Medsystems CEO Daniel Hawkins is now president and CEO of Vista.ai. [Photo courtesy of Avail Medsystems]Avail Medsystems founder and former CEO Daniel Hawkins has a new job as president and CEO of MRI software startup Vista.ai.

Palo Alto, California-based Vista.ai (founded and incorporated as HeartVista) makes AI-guided software for automating magnetic resonance imaging (MRI) exams.

Vista.ai was founded by Chief Medical Officer Bob Hu, Chief Architect and Head of Research Juan Santos and Chief Technology Officer William Overall. Its advisory board includes Stanford University doctors and professors and the radiology chair at the University of Wisconsin–Madison.

The company won FDA 510(k) clearances for its HeartVista Workstation with RTHawk application software for the acquisition of real-time and accelerated images from GE Healthcare whole-body MRI systems in 2014, 2017 and 2017. In 2019, the FDA cleared the company’s AI-assisted One Cli…

How syncing wearables with AI chatbots can accelerate recovery time

Smart integration of AI chatbots and data collected by wearable devices can help patients and health care providers respond to early signs of illness.

By Nate MacLeitch, QuickBlox

[Illustration by Kudryavtsev via Stock.Adobe.com]

More effective public health and medical interventions could save 1.2 million lives per year, according to the Organization for Economic Cooperation and Development (OECD).

But the healthcare industry isn’t solely responsible. There is usually a gap between identifying an illness and seeking medical attention. Even when a patient feels unwell, they often don’t know the best path of care and may avoid medical support because they minimize their symptoms, lack physical access to local healthcare, worry about healthcare costs, or simply don’t have a doctor.

Wearable medical devices can bridge this gap by monitoring vital signs — often remotely — to determine irregular behavi…

Understanding medtech AI with alpacas, llamas and Hologic VP of R&D/Innovation Mike Quick

The difference between llamas and alpacas is a great way to talk about medtech AI, Hologic VP of R&D/Innovation Mike Quick says. [Photo by linaskk via Stock.Adobe.com]

When Hologic VP of R&D/Innovation Mike Quick talks about artificial intelligence and medtech AI, he draws on his personal experience as an amateur alpaca farmer.

He and his wife had a herd of nearly a dozen alpaca when they lived in the Boston area. Then they moved to Arizona, where they now have three alpaca on a small farm in Phoenix.

“They’re a lot of fun,” he said in an interview with Medical Design & Outsourcing. “… The difference between llamas and alpacas — because it’s a common misconception of what they are — is a great way to talk about AI and the difference between machine learning and deep learning and how to learn to tell two different things apart.”

You start with t…

March 2024: Medtronic AI, Ortho tech at AAOS 2024 and Abbott PFA

How Medtronic’s using AI: Artificial intelligence insights and advice Abbott bets on balloons in the pulsed field ablation battle How Noah Medical’s robotic Galaxy system goes deep into the lungs The biggest stories from AAOS 2024 Surgical robots don’t improve knee surgery revision rates, study says Seize the AI opportunity

Less than a year into his new role as Medtronic’s chief technology and innovation officer, Ken Washington was presenting on artificial intelligence to leaders of the company’s operating units.

One of the GMs stopped him and asked for help making sense of all the buzzwords and acronyms.

Perhaps you know the feeling. It’s hard to grasp how AI seems to be everywhere, with advanced computing power making sense of vast datasets. It’s in the voice assistants on our smartphones, the streaming services on our various screens, mapping systems in our cars, the chat bots who respond when we need customer service, and online services ra…

Amgen opens AI-enabled Ohio biomanufacturing facility

CEO Bob Bradway rallies the manufacturing team at Amgen Ohio with a chant of “OH-IO.”

Earlier this year, NVIDIA and Amgen revealed plans to analyze one of the world’s largest human datasets using AI models trained on an NVIDIA DGX SuperPOD in Iceland. Now, Amgen is signaling its continued commitment to cutting-edge tech with a ribbon cutting for an advanced biomanufacturing facility in New Albany, Ohio.

Known as Amgen Ohio, the facility is designed to uphold the company’s reputation for quality and reliability. It will play a significant role in producing innovative biomedicines to address serious diseases, employing approximately 400 people across various roles.

The new facility “was designed with the latest innovation and technology to deliver safe, reliable medicines for ‘every patient, every time,’” said Bob Bradway, chairman and chief executive officer at Amgen.

Amge…

How Hologic tapped AI and volumetric imaging for cervical cytology — and potential applications beyond

Hologic designed its Genius cytology technology for more efficient and accurate review of cervix cell samples — and there’s more to come.

Hologic says its Genius cytology technology reduces false negatives of high-grade squamous intraepithelial and more severe lesions by 28% compared to microscopic review. [Photo courtesy of Hologic]

Hologic‘s Genius cytology system uses new scanning technology and artificial intelligence to flag cervical cancer cells and pre-cancerous lesions.

Hologic won a de novo classification in January 2024 for its Genius Digital Diagnostics System and Genius Cervical AI algorithm for cervical cytology. Besides replacing Hologic’s ThinPrep Imaging System — used for the majority of cervical cancer screenings in the U.S. — the technology behind the Genius system could one day also help screen for other kinds of cancer like bladder cancer as well as infectious organisms, Ho…

Off with the training wheels: AI-based patient characterization can improve clinical trial performance without large data sets

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Only 12% of new drug candidates that enter phase 1 clinical development ultimately receive FDA approval. This dismal success rate leaves millions of patients with unmet medical needs and drives up the costs for the small number of drugs that make it to market. More frustratingly, it leaves untold numbers of potentially transformative therapies back-burnered or discarded entirely, not because they don’t actually provide benefit, but because they were tested in trials that weren’t effectively designed to demonstrate benefit. The true failure hasn’t been in drug innovation but in identifying the patient traits that govern clinical trial outcomes.

The big challenges of big data methodologies

Artificial intelligence (AI) holds great promise in improving this success rate by providing data-driven approaches to identifying traits and their combinations that enable more effective paradigms to enrich patien…

Explainable AI lessons from the developers of the EarliPoint Evaluation for autism

Healthcare can’t fully embrace artificial intelligence until it’s better understood — and this device developer says explainable AI is critical.

By Sreeni Narayanan, EarliTec Diagnostics

EarliTec’s EarliPoint device uses artificial intelligence for embedded eye-tracking technology that measures more than 120 focal preferences per second. [Photo courtesy of EarliTec Diagnostics]

Artificial intelligence (AI) now performs feats that would have seemed far-fetched just a short few years ago. Users simply enter prompts and then quickly receive proposed actions or substantive answers in response, with a potential impact in healthcare that seems virtually boundless.

However, the calculations that occur between point A and point B — or the input and the output — are a mystery to most people. How GPTs and other AI programs do what they do remains poorly understood. While for many industries the how may be of…

Devices, disease, and digital: Holy Grail of healthcare AI

[Image courtesy of GE HealthCare]

In episode 3 of AI Meets Life Sci, DeviceTalks Managing Editor Kayleen Brown and Pharma and Biotech Editor Brian Buntz sit down with GE HealthCare Chief Digital Officer and GM of Oncology Ben Newton and Haley Schwartz of Catalyze Healthcare to discuss the impacts of AI to screen, diagnose, prognose, and treat cure disease while addressing real-world implementation issues from regulation and liability to clinician trust and adoption. They review the AI challenge of organizing disarrayed informational islands such as technology, clinical protocols, and digital solutions into cohesive, well-developed systems and offer insight into the medtech industry’s progress in this area.

Tune in and subscribe to AI Meets Life Sci on all major podcast channels and follow youtube.com/@DeviceTalks or AI Meets Life Sci YouTube Podcast to ensure you never miss an episode.

The AI Holy Grail for …

Supercomputer-based Bayesian approach to AI pays dividends for BPGbio

In an AI hype-filled biopharma industry, one company is taking a back-to-basics yet supercomputer-powered approach — using Bayesian analysis on massive patient datasets to guide drug discovery. The company crunches trillions of data points per patient. “It’s massive, which is why we use a supercomputer,” said Niven R. Narain, Ph.D., BPGbio CEO. The company has an exclusive relationship with Oak Ridge National Labs, using its Frontier supercomputer to perform complex computational tasks, including the analysis of multi-omics data, the development of predictive models, and the simulation of biological systems. Frontier is hailed as the world’s first exascale supercomputer, meaning it can perform more than 1 quintillion calculations per second.

BPGbio’s AI-powered platform, known as NAi Interrogative Biology, illustrates its approach to drug and diagnostic discovery. The platform includes a lmassive biobank of multi-omi…