Cami Rosso is a contributing writer for Psychology Today. With a passion for exploring the intersection of technology, neuroscience, and human behavior, Cami's articles delve into the latest advancements in artificial intelligence and its impact on various aspects of society, from healthcare to chemistry.
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Cami Rosso's coverage revolves around cutting-edge developments in the intersection of technology and health, with a specific focus on artificial intelligence applications in neuroscience, medical research, and mental health. Her articles often cite data to support her findings.
Given this focus, Cami is likely to be responsive to pitches from experts who can provide insights into the latest advancements in AI within healthcare and neuroscience. Individuals or organizations involved in pioneering AI technologies or conducting groundbreaking research related to brain-computer interfaces and mental health are also likely candidates for effective outreach.
Considering that Cami does not have a specified geographic focus, she appears open to covering global developments in these areas rather than focusing on any particular region.
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New Purdue University study is first-of-its-kind shows how a conversational AI based on a LLM framework called Talk2Drive uses GPT-4 to interpret human commands to guide autonomous vehicles.
Harvard Medical School unveils an AI digital pathology tool that outperforms state-of-the-art deep learning methods, detecting multiple cancer types with up to 96 percent accuracy.
New Purdue University study is first-of-its-kind shows how a conversational AI based on a LLM framework called Talk2Drive uses GPT-4 to interpret human commands to guide autonomous vehicles.
An AI model can predict dual-target drugs that can inhibit two enzymes at the same time to treat complex diseases and accelerate polypharmacology.
An AI model can predict dual-target drugs that can inhibit two enzymes at the same time to treat complex diseases and accelerate polypharmacology.
A new study shows how AI can help identify long COVID or post-acute sequelae of COVID-19 in patients using electronic health record data.
MIT study examines the performance impact of AI combined with humans that provide insights with implications for the future of work.
MIT study examines the performance impact of AI combined with humans that provide insights with implications for the future of work.
A new study shows how AI can help identify long COVID or post-acute sequelae of COVID-19 in patients using electronic health record data.
A unique combination of explainable AI and repurposing animal camouflage detection algorithms can identify human brain cancer.
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