Agreed, The VA is using CCDAs today for outbound communication and they started with C32s back in 2012. Looked at…
Microsoft adds intelligent meeting recap to Teams Premium, which generates meeting notes, a task list, and personalized timeline markers.
In Australia, five-hospital South Metropolitan Health Service orders doctors to stop using ChatGPT for work-related activity, citing confidentiality concerns. SMHS found that at least one doctor used ChatGPT to create a discharge summary, backtracking on an earlier statement in which it said that several doctors were creating notes in ChatGPT and then pasting them into the EHR.
AI chipmaker Nvidia hits a market capitalization of $1 trillion following a strong quarterly report, joining nine companies that have reached that mark including Apple, Microsoft, Alphabet, Amazon, and Saudi Aramco. A $10,000 investment in the company five years ago would be worth nearly $60,000 today.
Hyro, which offers conversational AI-powered healthcare workflow and conversation solutions, raises $20 million in a Series B funding round.
In England, AI drug discovery company Benevolent AI, which went public last year in a SPAC merger, will cut half of its workforce and scale back its laboratory facilities. The company had hoped to license its AI-designed drug candidate for atopic dermatitis, but it failed to improve symptoms in early-stage clinical trials.
Researchers use AI to design an antibiotic for treating hospital-acquired infections caused by the broadly resistant Acinetobacter baumannii bacteria. Researchers tested thousands of drugs for their ability to kill the bacteria or slow its spread, trained AI on the results, and then ran the resulting AI model against 6,700 other drugs to generate a 240-drug short list of candidates. The AI-chosen drug, abaucin, targets the bacteria specifically and therefore is less likely to cause drug resistance. Laboratory and clinical testing will take several years, with the first AI antibiotics expected to reach the market in 2030.
Google Health debunks five myths about medical AI:
- The more data, the better. Data quality matters more and expert adjudication in touch cases helps improve labeling quality.
- AI experts are all you need. Building an AI system requires a multidisciplinary team.
- High performance provides clinical confidence. Real-world validation is needed to make sure the model generalizes to real-life patients.
- AI fits easily into workflows. AI should be designed around human users.
- Launch means success. AI systems must be monitored to detect potential issues when patient populations or environmental factors change.
Nvidia profiles Nigeria-based physician, informaticist, and machine learning scientist Tobi Olatunji, MD, MS, who started Intron Health to transcribe physician dictation using AI with 92% accuracy across 200 African accents. The company was supported by Nvidia’s startup program. He earned a Georgia Tech computer science master’s and a UCSF master’s in medical informatics after he completed medical school in Nigeria.