AI in healthcare Archives - Medical Design Sourcing

Anosmia to amyloidosis: nference’s AI is decoding healthcare data at scale

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The complexity of patient records, with their mix of unstructured notes and diverse data types, defies traditional analysis. While a thorough review of even a single patient’s file can be tedious for a human, AI tools offer the power to analyze tens or even hundreds of millions of records, unlocking data patterns that would otherwise remain hidden.

A burgeoning number of companies are popping up to address the tasks of amalgamating and deciphering patient records using AI tools. Among them is nference, which has attracted backing of notable institutions such as the Mayo Clinic, Duke Health, Emory Healthcare, Vanderbilt University Medical Center and The Bill & Melinda Gates Foundation. In 2020, the company was highlighted as one of the top digital startups in the world.

Nference’s AI platform has a vast array of data at its disposal. In all, it captures more 11 million patien…

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 …

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.

The AI Holy Grail for decision support

Ben Newton

AI technologies emerged quickly over the last several years in medical imaging and oncology…

Wrangling medical imaging data: Strategies to streamline AI-powered workflows

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The value of artificial intelligence (AI) and machine learning (ML) in medical imaging is undeniable: more accurate diagnoses, predictive insights and streamlined workflows. However, as Big Pharma and medical research institutions amplify their AI and ML endeavors, they confront pivotal challenges. Chief among these challenges are the intricacies of labeling and annotating medical images.

Approximately 80% of the time it takes to prepare real-world data for downstream analysis is used on seemingly foundational tasks like locating, curating and structuring data. When we narrow our focus to medical imaging, the stakes rise significantly. The sheer volume of data required to do this work makes using human-only annotation close to impossible. And in this domain, the most minor details can critically influence diagnostic accuracy, making it imperative that data is not only accessible but clearly organiz…

Microsoft and 1910 Genetics are discovering health data’s hidden gems: Here’s how

In episode 2 of AI Meets Life Sci, DeviceTalks Managing Editor Kayleen Brown, and Pharma Editor Brian Buntz explore how Big Tech is developing artificial intelligence (AI) infrastructure in a variety of healthcare organizations.

Elena Bonfiglioli, global business leader for Health and Life Sciences at Microsoft, weighs in on the subject, also sharing that she found her passion for technology while programming welfare policy simulations and being continually intrigued by finding the insightful hidden gems held within population data. She explains that changing the DNA of an organization and shifting mindsets are imperative for future businesses to enable the responsible use of AI in healthcare.

Jen Nwankwo, founder and CEO of 1910 Genetics and a Microsoft collaborator, holds an impressive pedigree in biochemistry and biophysics, eventually earning her PhD in pharmacology. She emphasizes that her goals with AI and machine learning (ML) are to solve longstanding p…

Unlocking generative AI requires reshaping culture, operations, and talent dynamics

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In drug discovery and development, generative AI and natural-language processing (NLP) tools promise more than incremental productivity gains. For companies that can integrate such tools strategically into their workflows, the tools open the door to a fundamental rethinking of operational processes. For instance, generative AI tools can accelerate drafting of research articles, novel target identification, and the creation of SOPs for recipe and formulation. NLP, conversely, can mine unstructured scientific data and complex research papers. Because roughly 80% of healthcare data is unstructured, NLP promises to unlock previously inaccessible insights, transforming raw data into actionable knowledge.

But deploying such tools at scale requires a mix of strategic thinking, curiosity and new approaches to cultivating talent. As we enter 2024, life science organizations must rethink their approach to ad…

How the latest AI executive order might impact drug development in the U.S.

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The White House has released an executive order that contains what it hails as “the most sweeping actions ever taken to protect Americans from the potential risks of AI systems.” Relevant to drug development, a fact sheet on the order describes its aim to help further “the responsible use of AI in healthcare and the development of affordable and life-saving drugs.” The order also is designed to protect against the risks of using AI to synthesize new chemicals and biological materials, and will require government agencies funding life science projects to follow these standards as a prerequisite.

Other provisions of the order that could affect drug developers include its potential to affect AI-assisted clinical trials. The order could require drug developers to ensure that their algorithms do not exacerbate discrimination in clinical trials or patient interactions. Drug …

Radiology reimagined: Bayer and Google see AI as a win for practitioners and patients

AI is set to continue making inroads in radiology in the coming years, according to two executives from Bayer and Google.

In an interview at Google Cloud Next, Bayer’s Guido Mathews and Google Cloud’s Shweta Maniar highlighted the transformative influence of the technology on the radiologist’s workflow, the increasing integration of AI into radiological education, and its potential to mitigate burnout and reduce error rates.

Bayer offers contrast agents and injectors for major radiology modalities, including CT, MRI, an angiography.

In addition to focusing on radiology, Bayer Pharmaceuticals is using generative AI models like Google Cloud’s Vertex AI and Med-PaLM 2 to streamline drug development. Bayer is also using Google’s high-performance computing resources for quantum chemistry calculations.

AI’s radiological reboot

In 2016, deep learning pioneer Geoffrey Hinton predicted that AI systems would outperform radiologists by 2021…

CEO: Insilico on how AI can ‘imagine the perfect molecules’ for drug targets

Insilico’s AI-driven drug discovery process, showcased in their AI-powered robotics lab [Image courtesy of Insilico Medicine]

Insilico Medicine, an AI-based biotech startup, announced details of their first AI-designed drug candidate to enter human clinical trials. INS018_055 is an experimental treatment for idiopathic pulmonary fibrosis, a rare lung disease. Through their Insilico AI-driven drug discovery platform, they discovered and designed INS018_055 in just 30 months, significantly faster than the industry average. Phase 1 trials of INS018_055 have been completed in New Zealand and China, showing a favorable safety profile. Insilico plans to launch Phase 2 trials in 2023 to further evaluate the drug’s efficacy and tolerability. If successful, INS018_055 could provide a new treatment option for patients with idiopathic pulmonary fibrosis. The trial results also demonstrate the potential of AI to accele…

Sanofi bets on AI-powered decision making

Unveiling Sanofi’s AI-powered pharma strategy, the French pharma giant announced its plans to tap AI for insights across the company. With “plai,” an internal app, AI is utilized to provide real-time data supporting decision-making.

Sanofi’s goal, as CEO Paul Hudson stated in a news release, is to become the first pharma company powered by AI at scale, “giving our people tools to make better everyday decisions.”

How Sanofi’s AI-powered pharma strategy compares to other industry moves

Several other companies have initiated similar AI-powered moves over the years. For instance, in December 2016, Pfizer and IBM inked a deal to use IBM Watson for drug discovery in immuno-oncology. Roche has explored using machine learning for diabetes diagnostics while companies like Novartis have advertised hundreds of AI hires in recent years.

Challenges of AI in healthcare and Sanofi’s AI-powered pharma strategy

Yet, deploying AI in healthcare sett…

8 considerations to boost clinical trial productivity with AI while dodging hallucination hurdles

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The development of new drugs is undeniably a data-intensive endeavor. Despite impressive advances in AI over the past years, researchers often continue to grapple with crushing data volumes. This hurdle is particularly apparent in clinical trials, where crucial data is often stored in machine-unfriendly formats such as PDFs, PowerPoint or HTML or other formats.

This article explores strategies to harness AI for data management in clinical trials while avoiding potential pitfalls such as data integrity issues and large language model hallucinations, which can lead to unreliable or distorted outputs.

1. Understand the complexity of clinical trial data

The complexity of clinical trial data can be difficult for someone outside the field to appreciate, according to Jeff Elton, CEO of Concert AI. “There can be 60 to 70 different levels of inclusion and exclusion criteria,̶…

UBS: Generative AI is no silver bullet for drug discovery

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Imagine a world where the process of developing life-saving drugs is as streamlined as a modern assembly line.

In such a reality, generative AI in drug discovery might churn out promising compounds with similar efficiency and precision as a factory robot assembling a car. Moreover, such technology could chip away at the steep cost and lengthy timelines typically associated with drug development, which can cost north of $2 billion and take more than a decade.

However, such a vision may be more hype than reality, according to a new UBS report. While it is true that AI is carving out a niche in life sciences, a recent Q-Series report from UBS titled “Will Generative AI deliver a generational transformation?” reaches mostly muted conclusions about the potential of generative AI in drug development. In essence, the investment bank projects that generative AI…