Semalytix, a life science startup based in Bielefeld, Germany, has announced the launch of PatientGPT, the world’s first patient-centric large language model approach, revolutionizing drug development. This breakthrough interplay between supervised machine learning and large-language models will provide more accurate and efficient estimates of the effects and value of medications based on millions of patient experiences.
Chief Product Officer Janik Jaskolski commented: “We can not only analyze but also precisely query the effects of any medication based on patient experiences and unmet needs worldwide, down to the smallest detail. By the end of the year, we will have over 50 million patient data points in our patient experience data archive, which our own LLM solution will be tuned with. This allows us to gain exact insights into how people live with diseases and generate crucial new knowledge for patient-focused drug development.”
The PatientGPT prototype can access real-time data from 100 million sources in 26 languages and allows for the amplification of learning, such as the discovery of Viagra, which was originally a side effect of hypertension medication.
Chief Technology Officer, Professor Dr. Philipp Cimiano said: “PatientGPT truly unlocks the huge potential of patient experience data that will help shape new therapies and authentically and continuously answer what patients need most.”
The new technology also benefits from strong safety measures and anonymization guidelines for sensitive patient data. Semalytix has already demonstrated the potential of its AI-based research technology with case studies, innovation projects, and publications, with customers including many of the top 20 pharmaceutical companies worldwide.
Janik Jaskolski concluded: “We want to dramatically accelerate the speed of therapy development and improve how well new drugs match patients’ burdens. We contribute by making the global patient experience as accessible as possible in a compliant and ethical way.”
Semalytix, founded in 2015 by managing directors Janik Jaskolski, Philipp Cimiano, and Matthias Hartung, currently employs 25 employees.
A new patient-centric large language model approach, developed by life science startup Semalytix, has revolutionized the way drug development is carried out. This breakthrough technology interplays supervised machine learning with large-language models to provide more accurate and efficient estimates of the effects and value of medications based on millions of patient experiences.
The PatientGPT prototype can access real-time data from 100 million sources in 26 languages and allows for the amplification of learning, such as the discovery of Viagra. Additionally, the technology benefits from strong safety measures and anonymization guidelines for sensitive patient data.
Janik Jaskolski, Chief Product Officer at Semalytix, said: “We can not only analyze but also precisely query the effects of any medication based on patient experiences and unmet needs worldwide, down to the smallest detail. By the end of the year, we will have over 50 million patient data points in our patient experience data archive, which our own LLM solution will be tuned with. This allows us to gain exact insights into how people live with diseases and generate crucial new knowledge for patient-focused drug development.”
Chief Technology Officer, Professor Dr. Philipp Cimiano added: “PatientGPT truly unlocks the huge potential of patient experience data that will help shape new therapies and authentically and continuously answer what patients need most.”
Semalytix, founded in 2015 by managing directors Janik Jaskolski, Philipp Cimiano, and Matthias Hartung, currently employs 25 employees and has already demonstrated the potential of its AI-based research technology with case studies, innovation projects, and publications, with customers including many of the top 20 pharmaceutical companies worldwide.
Today marks a milestone for drug development with the launch of Semalytix’s PatientGPT, the world’s first patient-centric large language model approach. This interplay between supervised machine learning and large-language models provides more accurate and efficient estimates of the effects and value of medications based on millions of patient experiences.
PatientGPT prototype can access real-time data from 100 million sources in 26 languages and allows for the amplification of learning, such as the discovery of Viagra. Additionally, the technology benefits from strong safety measures and anonymization guidelines for sensitive patient data.
Janik Jaskolski, Chief Product Officer at Semalytix, commented: “We can not only analyze but also precisely query the effects of any medication based on patient experiences and unmet needs worldwide, down to the smallest detail. By the end of the year, we will have over 50 million patient data points in our patient experience data archive, which our own LLM solution will be tuned with. This allows us to gain exact insights into how people live with
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