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Data Analytics and AI are Accelerating Medical Research - Dr. Julie Panepinto, Director of the Division of Blood Diseases and Resources at the NIH
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What does the future of disease research look like? How can artificial intelligence help researchers make new discoveries faster? How can medical professionals synthesize the vast amounts of patient data to offer the best, most personalized care possible
These are some of the questions we explore with Dr. Julie Panepinto, who leads the National Institutes of Health's Division of Blood Diseases and Resources
In this episode, we dive deep in to the Science part of STEM to learn about the latest advances in medical research, how data analytics and AI are accelerating these efforts, and how education can inspire the next generation of medical researchers
Hear all about:
- What scientists around the country are researching in the areas of blood diseases
- Why medicine must maximize quantitative and qualitative data together to best serve patients
- How AI will impact clinician's ability to detect and diagnose - especially in medical imaging
- Predictive risk modeling and the future of precision healthcare
- The human aspect of medicine, the importance of face-to-face care, and how data can help doctors develop more customized treatment plans for each individual
3 Big Takeaways from this episode:
- Medical research needs quantitative and qualitative data to produce the best results: The healthcare industry has billions of quantitative datasets from millions of patients. Additionally, patient reported outcomes help turn qualitative information about the patient's personal experience into quantitative data. When healthcare providers have access to both quantitative and qualitative data, they can create personalized treatment plans for each individual, a practice called precision healthcare.
- Data analytics and artificial intelligence enable predictive risk modeling in medical research: All the data just mentioned can be used in predicting and preventing diseases in individuals based on their unique risk factors. Listen as we discuss the generation of algorithms for predictive healthcare, genomic and curative treatments, and why the quality and structure of the data matters when training AI models.
- The future of healthcare will be data-driven, but it will never lose the human factor: Expect tele-health visits, chatbots, AI helping clinicians detect and diagnose individuals, and automated health plans based on data-driven models. But also know that the face-to-face connection will always remain a key factor to healthcare; for nothing can replace the doctor-patient relationship.
Resources mentioned in this episode:
- To learn more about Dr. Panepinto, visit her page
- Learn more about the research being done by the NIH Division of Blood Diseases and Resources
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