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Press Release Database
Recent press releases, indexed by topic.
AI · Healthcare
Color Health is working with OpenAI to pioneer a new way of accelerating cancer patients’ access to treatment. Their new Cancer Copilot application uses GPT-4o to identify missing diagnostics and create tailored workup plans, enabling healthcare providers to make evidence-based decisions about cancer screening and treatment.
Jun 17 · 04:15·OpenAI
SaaS · Healthcare
Founder Srikanth Nadhamuni got the idea for 10BedICU during the devastating 2021 Delta wave of COVID-19, which saw over 20 million cases in just a few months. With only 2.3 ICU beds per 100,000 people, India’s healthcare infrastructure was quickly overwhelmed. 10BedICU responded swiftly, setting up ICU units in government hospitals and equipping them with the latest medical technology and a telehealth platform called
Jun 6 · 10:00·OpenAI
AI · Healthcare
Oscar brings AI to health insurance, reducing costs and improving patient care.
Apr 1 · 00:00·OpenAI
AI · Healthcare
Healthify collaborates with OpenAI to improve millions of lives with sustainable weight loss.
Mar 13 · 07:00·OpenAI
AI · Healthcare
Lifespan uses GPT-4 to radically improve health literacy and patient outcomes.
Mar 6 · 08:00·OpenAI
AI · SaaS
Paradigm uses OpenAI’s API to improve patient access to clinical trials.
Mar 6 · 08:00·OpenAI
AI · Healthcare
WHOOP delivers personalized fitness and health coaching with GPT-4.
Jan 4 · 08:00·OpenAI
Healthcare
Summer Health reimagines pediatric doctor’s visits with OpenAI.
Dec 14 · 08:00·OpenAI
Healthcare
Abstract We propose a benchmark to measure whether a language model is truthful in generating answers to questions. The benchmark comprises 817 questions that span 38 categories, including health, law, finance and politics. We crafted questions that some humans would answer falsely due to a false belief or misconception. To perform well, models must avoid generating false answers learned from imitating human texts. W
Sep 8 · 07:00·OpenAI
AI · Healthcare
Abstract Some machine learning applications involve training data that is sensitive, such as the medical histories of patients in a clinical trial. A model may inadvertently and implicitly store some of its training data; careful analysis of the model may therefore reveal sensitive information. To address this problem, we demonstrate a generally applicable approach to providing strong privacy guarantees for training
Oct 18 · 07:00·OpenAI