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Home » AI

Articles Tagged with ''AI''

Toku Clair platform

Toku receives CE, UKCA marks for cardio disease AI risk evaluation tool

Feb. 8, 2024
By Shani Alexander
Toku Inc. recently obtained CE and UKCA marks for its artificial intelligence (AI) technology, Clair, which evaluates the risk of cardiovascular disease (CVD) using retinal images captured during an eye exam.
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EU flags

EU nudges Artificial Intelligence Act further along

Feb. 5, 2024
By Mark McCarty
The European Union (EU) is steadily making progress on the Artificial Intelligence Act (AI Act), landmark legislation that will affect AI not just for medical uses, but for all uses across the EU economy.
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Hologic Genius Review Station

Hologic wins FDA clearance for Genius with cervical AI algorithm

Feb. 2, 2024
By Annette Boyle
Hologic Inc.’s focus on women’s health is looking like a brilliant move as the company lands U.S. FDA clearance for its Genius digital diagnostics system with the Genius cervical artificial intelligence algorithm, making it the first digital cytology system with clearance, the company said.
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Kakao Pasta app

Kakao debuts AI software app for CGM-diabetes care

Feb. 1, 2024
By Marian (YoonJee) Chu
Kakao Healthcare Corp. launched an artificial intelligence (AI)-powered mobile application called PASTA on Feb. 1 for continuous glucose monitoring (CGM)-based diabetes care. PASTA pairs with two types of CGM sensors, either from Seoul-based I-sens Inc.’s Caresens Air or San Diego-based Dexcom Inc.’s G7 system.
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Seegene Microsoft collab

Seegene pushes ambitious DTx tech-sharing initiative with Microsoft

Jan. 30, 2024
By Marian (YoonJee) Chu
In a post-pandemic world, South Korean molecular diagnostics firm Seegene Inc. is looking to ignite a new paradigm in the diagnostics business. Seoul-based Seegene is rolling out a new strategy of open innovation under its newfound initiative coined the “SG Onesystem,” which pledges to not only accelerate global partnerships for novel diagnostics but also “free the world of all disease.”
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TUS infographic

New non-invasive imaging tech visualizes liver fat

Jan. 26, 2024
By Marian (YoonJee) Chu
A new study published by researchers from the Tokyo University of Science (TUS) unveiled a new machine learning-aided, non-invasive imaging framework for rapid liver lipid visualization, which could help diagnose and treat steatotic liver diseases (SLD).
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Digital brain and silhouette

FDA’s device center meets MDUFA hiring goals in fiscal 2023

Jan. 25, 2024
By Mark McCarty
The U.S. FDA’s device center has at times struggled to make the volume of hires under the reigning Medical Device User Fee Agreement (MDUFA), but that wasn’t a problem in fiscal year 2023.
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Seegene Microsoft collab

Seegene pushes ambitious DTx tech-sharing initiative with Microsoft

Jan. 24, 2024
By Marian (YoonJee) Chu
In a post-pandemic world, South Korean molecular diagnostics firm Seegene Inc. is looking to ignite a new paradigm in the diagnostics business. Seoul-based Seegene is rolling out a new strategy of open innovation under its newfound initiative coined the “SG Onesystem,” which pledges to not only accelerate global partnerships for novel diagnostics but also “free the world of all disease.”
Read More
Niura neurodiagnostic
Patents

Niura’s neurodiagnostic earbuds also give personalized music recommendations

Jan. 18, 2024
By Simon Kerton
A team of inventors predominantly based in the state of New York are seeking patent protection for an earbud system that integrates real-time electroencephalography (EEG) monitoring with audio playback. Using specialized silicone electrodes, it captures and interprets EEG data, serving as a passive and continuous tool for screening neurological conditions.
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Exploding laptop
Drug Design, Drug Delivery & Technologies

Without enough data, machine learning can lead analyses astray

Jan. 12, 2024
By Anette Breindl
Researchers have reported that the predictive abilities of a machine learning algorithm trained using best practices on a large clinical dataset did not generalize beyond the data that was used to train it. The algorithm was able to predict, to a degree, which individual patients would benefit from the medication when the patients were from the dataset the algorithm was trained on. But when it was supposed to predict who would benefit in clinical cohorts that were not part of the training, it performed no better than chance.
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