Suggestions
Matthew Marcetich
Healthcare | AI/ML | "Data Pulse" Author
Matthew Marcetich is a professional with a background in medical research, informatics, and analytics-related consulting.1 He has experience in building and deploying custom software solutions.1
Currently, Matthew Marcetich is on a career break, focusing on personal goal pursuit. This information suggests he may be taking time away from his regular professional activities to pursue personal objectives or projects.
One of Matthew's recent endeavors is authoring a book titled "How Healthcare Happens: Using AI and Machine Learning to Transform Healthcare".2 This book likely draws from his professional experiences in the medical and technology fields, exploring the potential of artificial intelligence and machine learning in revolutionizing healthcare practices.
Matthew's LinkedIn profile can be found under the username "matthewmarcetich". His professional background includes associations with Johns Hopkins Bloomberg School of Public Health, indicating a strong connection to the healthcare and public health sectors.1
In addition to his professional pursuits, Matthew has shown a philanthropic side. In September 2020, he offered to donate a surplus laptop to parishioners who could benefit from such a device, demonstrating his willingness to contribute to his community.3
Highlights
Happy to share my brief comparison of US and UK AI policy frameworks, which describes some implications for healthcare and was recently published by The Yuan. Sharing a few key takeaways below.
TL;DR... 💡 The frameworks were published ahead of a first-ever AI Safety Summit hosted at Bletchley Park Nov 2023
💡 The UK intends to be the best place in the world to build, test, and use AI
💡The US document repeatedly uses the terms "designers, developers, and deployers" as roles responsible for carrying out its framework's principles
💡 A “State of the Science” report will be published ahead of the next AI Safety Summit (South Korea or France)
💡 Both reports are organized around 5 similar principles
💡 As white papers, neither document introduces rigid requirements
💡 It's unclear if the suggestions in the documents will represent new burdens on clinicians or developers, but there is opportunity for potential burdens to become software features (i.e., an option to use AI)
https://lnkd.in/gzm6YeND
One example of a startup developing healthcare-related LLMs to address non-diagnostic use cases, such as explaining benefits and billing. Hippocratic AI received $50M in seed funding financed by a16z and others.
https://lnkd.in/d6TzJhee
#ai #startup #healthcare Hippocratic AI Andreessen Horowitz
