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Sisällön tarjoaa Oleksandr Yagensky. Oleksandr Yagensky tai sen podcast-alustan kumppani lataa ja toimittaa kaiken podcast-sisällön, mukaan lukien jaksot, grafiikat ja podcast-kuvaukset. Jos uskot jonkun käyttävän tekijänoikeudella suojattua teostasi ilman lupaasi, voit seurata tässä https://fi.player.fm/legal kuvattua prosessia.
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Ep#016: Ensuring Fairness in Precision Medicine with Dr. Kadija Ferryman

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Manage episode 273656961 series 2800366
Sisällön tarjoaa Oleksandr Yagensky. Oleksandr Yagensky tai sen podcast-alustan kumppani lataa ja toimittaa kaiken podcast-sisällön, mukaan lukien jaksot, grafiikat ja podcast-kuvaukset. Jos uskot jonkun käyttävän tekijänoikeudella suojattua teostasi ilman lupaasi, voit seurata tässä https://fi.player.fm/legal kuvattua prosessia.
Precision medicine has the potential to transform healthcare in terms of diagnostics, treatment, and prevention of disease. But what does a future with a more personalized approach to medicine look like? Who will ultimately benefit from precision medicine?
In this episode, we dive into these questions with Dr. Kadija Ferryman, a cultural anthropologist whose research centers on the ethical dimensions of health risk technologies, especially as they relate to racial disparities in health. Kadija is an Industry Assistant Professor at NYU’s Tandon School of Engineering. She is also an affiliate at the Data & Society Research Institute, where she led a research study on Fairness in Precision Medicine. This ground-breaking study examined the potential for biased and discriminatory outcomes in the emerging field of precision medicine.
Together with Kadija, we talk about:
◦ BiDil: the first FDA-approved drug marketed to a single racial-ethnic group
◦ The promise and potential of precision medicine
◦ The 'Fairness in Precision Medicine' study
◦ The need for proactive ethical studies
◦ Bias in how we collect and examine electronic health data
◦ Bias in medical outcomes due to existing patterns of marginalization
◦ Human bias in AI and Machine Learning
◦ Implicit bias in healthcare professionals
◦ Establishing a more equitable medical future
Get in touch with Kadija:
◦ Twitter: @KadijaFerryman
◦ Web: http://www.kadijaferryman.com
◦ Web (Data & Society): https://datasociety.net/people/ferryman-kadija/
◦ Web (CRDS): https://criticalracedigitalstudies.com
Make sure to download the full show notes with our guest's bio, links to their most notable work, and our recommendations for further reads on the topic of the episode at pmedcast.com
  continue reading

48 jaksoa

Artwork
iconJaa
 
Manage episode 273656961 series 2800366
Sisällön tarjoaa Oleksandr Yagensky. Oleksandr Yagensky tai sen podcast-alustan kumppani lataa ja toimittaa kaiken podcast-sisällön, mukaan lukien jaksot, grafiikat ja podcast-kuvaukset. Jos uskot jonkun käyttävän tekijänoikeudella suojattua teostasi ilman lupaasi, voit seurata tässä https://fi.player.fm/legal kuvattua prosessia.
Precision medicine has the potential to transform healthcare in terms of diagnostics, treatment, and prevention of disease. But what does a future with a more personalized approach to medicine look like? Who will ultimately benefit from precision medicine?
In this episode, we dive into these questions with Dr. Kadija Ferryman, a cultural anthropologist whose research centers on the ethical dimensions of health risk technologies, especially as they relate to racial disparities in health. Kadija is an Industry Assistant Professor at NYU’s Tandon School of Engineering. She is also an affiliate at the Data & Society Research Institute, where she led a research study on Fairness in Precision Medicine. This ground-breaking study examined the potential for biased and discriminatory outcomes in the emerging field of precision medicine.
Together with Kadija, we talk about:
◦ BiDil: the first FDA-approved drug marketed to a single racial-ethnic group
◦ The promise and potential of precision medicine
◦ The 'Fairness in Precision Medicine' study
◦ The need for proactive ethical studies
◦ Bias in how we collect and examine electronic health data
◦ Bias in medical outcomes due to existing patterns of marginalization
◦ Human bias in AI and Machine Learning
◦ Implicit bias in healthcare professionals
◦ Establishing a more equitable medical future
Get in touch with Kadija:
◦ Twitter: @KadijaFerryman
◦ Web: http://www.kadijaferryman.com
◦ Web (Data & Society): https://datasociety.net/people/ferryman-kadija/
◦ Web (CRDS): https://criticalracedigitalstudies.com
Make sure to download the full show notes with our guest's bio, links to their most notable work, and our recommendations for further reads on the topic of the episode at pmedcast.com
  continue reading

48 jaksoa

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