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Podcast: Is AI Good for Medicine and the Patients It is Trying to Help?

Re-Think Health Podcast Season 3 Brings Experts to Discuss How Pragmatic is the Use of Deep Learning Technologies in Enhancing Health Outcomes for All

Highlights

  • We need to ask how pragmatic AI is in delivering good medicine and enhancing patient outcomes equally.
  • This podcast series dives deep into conversations with multi-disciplinary experts about the pragmatic, responsible, and equitable use of AI in healthcare.
  • Take a sneak peek at the topics covered in the series. New episodes are added every week.

The term “good” is a highly subjective term, especially when it relates to health. With the rush toward using tools such as artificial intelligence (AI), machine learning (ML), and deep learning technologies to trudge through health data for insights we need, we need to ask how pragmatic AI is in delivering good medicine and enhancing patient outcomes equally.

In the Re-Think Health Podcast Series Season 3: AI for Good Medicine, we go deep into conversations with multi-disciplinary experts – technologists, clinicians, researchers, scientists, ethicists, and policy stewards – from around the globe to answer the questions: 

  • How can we envision AI, ML, or other deep learning technologies in order to deliver good medicine for all?
  • Will these technologies cut through the health data swamp for better health outcomes?

As healthcare industry stakeholders, we are not looking for the next frontier of medicine if it is not pragmatic, responsible, and can be equitably valuable to all.

Photos and titles of guests for the Re-Think Health Podcast Season 3

Here are the top ten (10) insights obtained from the expert guests:

  1. It’s all about the data. Data is a depreciating asset, the longer it sits the less value it has. 
  2. Data is an abyss. If you want AI to make an impact on the health system, then we should make data reliable by design.
  3. Fairness is not a math problem. Equity in healthcare is not about the technology but rather the approaches we take to make healthcare accessible to all.
  4. Social determinants of health have significant, if not equal, value to diagnostic health data in closing the healthcare gap with AI.
  5. Explainable AI. Make it transparent and off-the-shelf so that clinicians understand how the algorithms are addressing the questions in the data to arrive at the insights needed.
  6. Going to the same well of data will only get you the same results. Secondary use of real-world data available in an open, trusted, and validated means will enable predictive analytics to have a material impact on clinical research.
  7. Harvest it, splice it, and mine it. RNA splicing holds many insights to fighting diseases caused by RNA errors for the development of targeted therapeutics in oncology. 
  8. The trillions of bytes of data in genomes and pathology are no match for AI which could generate the much-needed insights in months as compared to years with former approaches undertaken by oncology researchers.
  9. AI can close the healthcare gap depending on how you deploy it. Where you put the mind of AI, that’s what it will process. 
  10. There is a strong disconnect between the clinical side (physicians, nurses, etc.) and hospital IT system administrators when it comes to implementing, utilizing, and integrating these technologies into the continuum of care. 

Launched by the IEEE Standards Association (IEEE SA) Healthcare and Life Sciences Practice, Re-Think Health Podcast Series Season 3 features the following episodes:

  • The Balance – AI’s Healthcare Goodness for Marginalized Patients. Sampath Veeraraghavan, Chair of IEEE Humanitarian Activities Committee (HAC), brings to the table a vital debate: Can AI and ML support fairness, personalization, and inclusiveness to chip away at the epidemic of healthcare inequity, or will it further create more inequity in the healthcare system?
  • Riding the Third Wave of AI for Precision Oncology. This episode features Nathan Hayes, CEO and Founder of Modal Technology Corp, and Anthoula Lazaris, Scientist and Director of Liver Disease Biobank at the McGill University Research Institute. We discuss improving patient outcomes and the application of the “third wave of AI” with real-world data and practice to realize the potential for precision oncology.
  • Advanced AI and Sensors – Reaching the Hardest to Reach Patients at Home. Sumit Nagpal, CEO and Founder of Cherish, discusses how using advanced AI in advanced sensors and mindset can efficiently and effectively support the wellness needs of the rapidly growing older generation at home with dignity and integrity.
  • AI – the New Pipeline for Targeted Drug Discovery. Dr. Maria Luisa Pineda, CEO and Co-Founder of Envisagenics, discusses splicing with AI and HPC (high-performance computing) to find the path to targeted drug discovery. RNA splicing is at the forefront of providing insights into diseases that are linked back to RNA errors. Together with AI and high-performance computing (HPC) and the exponential amount of genetic data, the insights needed for targeted drug discovery in oncology and other genetic conditions can come faster and more accurately. 
  • Reducing the Healthcare Gap with Explainable AI. Dave DeCaprio, CTO and Co-Founder of ClosedLoop.ai, talks about healthcare disparities, which are a global challenge requiring more than just physical care. Identifying and leveraging social determinants, when mined correctly, are untapped keys to closing the healthcare gap. Off-the-shelf AI presents a new perspective on transparency, reduction of bias, and the path toward health stakeholders’ trust with explainability in its applications.
  • Getting Real about Healthcare Data and the Patient’s Journey. The time has come to unleash the value of unstructured data. AI and ML afford those opportunities across the healthcare domain. But AI and ML must be demystified. We need to embrace the value of natural language processing (NLP) in daily operating systems. Alexandra Ehrlich, Principal Health Innovation Scientist at Oracle, speaks about the opportunities of NLP as well as the challenges with navigating bias throughout accessible healthcare data.
  • Mind Your Data – The First Rule of Predictive Analytics in Clinical Research. Aaron Mann, SVP of Data Science at CRDSA (Clinical Research Data Sharing Alliance), speaks on how open data sharing is paving the way toward access to more quality, real-world, and inclusive data to enable predictive analytics to be more accurate, resourceful, and utilitarian in clinical research.
  • Can the Health System Benefit from AI as it Stands Today? With the focus on accuracy, ethics, and bias in AI algorithms, we cannot lose sight of the need for more and validated data. Dimitrios Kalogeropoulos, with World Bank, EU Commission, WHO, and UNICEF, discusses the question: Is it AI for Good Medicine or Good Medicine for AI? What does the data show?

Listen to Season 3 now. View previous episodes of the Re-Think Health Podcast.

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Maria Palombini

Director, Emerging Communities & Opportunities Development, Global Business Strategic Initiatives (GBSI); Healthcare Life Sciences Practice Lead, IEEE Standards Association (IEEE SA) - Maria leads the IEEE SA Healthcare & Life Sciences Practice working with a global community of stakeholder volunteers who are committed to establishing trust and validation in tools and technologies that will change the approach to discover therapies, deliver care, and ultimately enable a sustainable and universal quality of care for ALL. Maria is the founder of DisruptiveRx, an information gateway addressing the critical need for pharmaceutical and life sciences executives to connect innovation with business strategy to re-think the process of the drug development and distribution value chain. She has extensive experience in building global media brands in multiple industry sectors. She holds a B.S. and B.A. from Rutgers College and an M.B.A. from Rutgers Graduate School of Business at Rutgers University.

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