The AI-Driven Future of Healthcare: 14 Groundbreaking Data Analysis Methods for the 21st Century: Featuring Vovk’s Conformal Prediction, the History of Deep Seek, and Insights from the 2008 Beijing Olympics

January 31, 2025

The AI-Driven Future of Healthcare: 14 Groundbreaking Data Analysis Methods for the 21st Century: Featuring Vovk’s Conformal Prediction, the History of Deep Seek, and Insights from the 2008 Beijing Olympics

After the success of my previous post,
"My Thoughts on the Future of Biostatistics in the Era of Artificial Intelligence: A Real and Fictional Personal Life History Intertwining Leo Breiman’s Ideas, Triathlon Sports, and the Importance of Change – The Philosophy of My Friend Yinchao,"
I’m excited to continue exploring the transformative landscape of biostatistics and artificial intelligence (AI) in healthcare.

Reflecting on Change in the New Era of Biostatistics and AI

Before resuming our series of interviews, it’s important to reflect on the significance of change in the evolving field of biostatistics, especially with the rise of AI. This week, we witness the emergence of Deep Seek, a low-cost but powerful Chinese generative AI. Generative AI will be transforming the healthcare.

Highlighting Robert Tibshirani’s Interviews

Recently, the renowned American Professor of Statistics at Stanford University, Robert Tibshirani and their team, launched a series of interviews on YouTube titled "14 Statistical Ideas that Changed the World." You can explore these insightful discussions here. These interviews shed light on groundbreaking statistical methodologies that have significantly impacted various fields, including healthcare.

The 14 Groundbreaking Statistical Ideas

Below are the 14 pivotal papers and their authors that have greatly influenced biostatistics and beyond:

Author	Paper
Nan Laird	Random-Effects Models for Logitudinal Data
Yoav Benjamini and Yosef Hochberg	Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Leo Breiman	Statistical Modeling: The Two Cultures
Brad Efron	Bootstrap Methods: Another Look at the Jackknife
Art Dempster, Nan Laird, and Donald Rubin	Maximum Likelihood from Incomplete Data via the EM Algorithm
Leonard Baum and Lloyd Welch	Baum-Welch Algorithm
Alan Gelfand and Sir Adrian Smith	Sampling-Based Approaches to Calculating Marginal Densities
Sir David Cox	Regression Models and Life-Tables
Paul Rosenbaum and Donald Rubin*	The Central Role of the Propensity Score in Observational Studies for Causal Effects
Trevor Hastie and Robert Tibshirani*	Generalized Additive Models
Vladimir Vovk*	A Tutorial on Conformal Prediction
Jerry Friedman*	Greedy Function Approximation: A Gradient Boosting Machine
Jamie Robins*	Association, Causation, and Marginal Structural Models
Rob Tibshirani*	Regression Shrinkage and Selection via the Lasso

Building the Healthcare of the Future: Which Idea Leads the Way?

With these 14 transformative ideas in mind, the question arises: Which of these papers will be the cornerstone of future biostatistics, shaping the healthcare systems of tomorrow?
While each paper holds unique significance, Vladimir Vovk’s "A Tutorial on Conformal Prediction" stands out as particularly crucial in today’s data-driven AI predictive systems.

Who Is Vladimir Vovk?

Vladimir Vovk is the creator of the Conformal Prediction framework—a groundbreaking approach in statistical modeling. He was the last student of the eminent mathematician Andrei Kolmogorov, widely regarded as one of the most influential mathematicians of the last century. Originally from Kiev and trained as a mathematician and logistician, Vovk pursued his undergraduate and master’s studies in Moscow before earning his Ph.D. He is renowned for defining conformal prediction, an idea that Robert Tibshirani currently cites as one of the 14 most important contributions to statistics. Today, Vladimir Vovk is a professor at the University of Holloway, near London, one of the UK's most influential academic institutions.

The Power of Conformal Prediction in Healthcare

In an era where automatic clinical decision-making systems are proliferating, ensuring the reliability and accuracy of predictive models is paramount. Vladimir Vovk’s conformal prediction provides a robust framework for quantifying uncertainty in model predictions, offering finite-sample, non-asymptotic guarantees for marginal coverage. Unlike traditional methods that often depend on asymptotic properties and specific model assumptions, conformal prediction delivers model-free and distribution-free measures of prediction uncertainty. This significantly enhances the trustworthiness of AI-driven tools in clinical settings, where decisions can have life-or-death consequences.

Key Benefits of Conformal Prediction:

Reliability: Provides valid measures of uncertainty without relying on stringent model assumptions.
Flexibility: Applicable to a wide range of models and data types, making it versatile for various healthcare applications.
Interpretability: Offers clear prediction sets, aiding clinicians in understanding and trusting AI recommendations.

Analyzing the Tibshirani Interview to Vladimir Vovk

In the interview series, Vladimir Vovk delves into the origins of conformal inference, tracing its roots back to the foundational work of Kolmogorov and Von Mises in probability theory. Surprisingly, while Kolmogorov's axiomatization of probability has gained widespread acceptance, it did not convince Kolmogorov himself.

Vovk also discusses open problems, such as the loss of efficiency when equipping predictive algorithms with conformal inference—a topic that remains central in my current research, especially concerning statistical objects in high-dimensional and general metric spaces.

Conformal inference fundamentally relies on the assumption that the analyzed observations are exchangeable. This property offers can be critical to derive non-asympotic guarantees, for instance, in predicting electoral outcomes in the USA or the UK, that UK is more exchangeable than USA.

The final part of the interview is particularly fascinating as it addresses challenges in natural language processing (NLP), where ground truth often does not exist. The interpretation of language can depend on intrinsic linguistic characteristics, context, and even physiological human thought processes. Vovk suggests that conformal prediction can offer a form of ground truth in natural language, thereby enhancing the reliability of AI-driven language models.

The Future of Large Language Models in Healthcare: The Role of Conformal Prediction

In medicine, electronic health records (EHRs) contain vast amounts of textual information from clinical histories. Efficient exploitation of this data relies on NLP techniques that can harmonize and accurately interpret medical language. Conformal prediction emerges as an ideal tool to create the necessary ground truth to understand what patients and clinicians think, feel, and communicate. By providing reliable confidence measures, conformal prediction enhances the trustworthiness of NLP applications in healthcare, ensuring that AI-driven insights are both accurate and actionable.

The introduction of low-cost generative AI systems like Deep Seek, combined with conformal prediction techniques, has the potential to revolutionize personalized medicine.

The Rebirth of China: From the Beijing Olympics to AI and Data Science

Just as China surprised the world by winning numerous medals at the 2008 Beijing Olympics despite a relatively short tradition in some sports, the country is now emerging as a formidable competitor in the global AI race. This resurgence mirrors the contributions of many Russian and Eastern European scientists who revolutionized science under resource-limited conditions.

This phenomenon highlights how countries not traditionally at the forefront of the AI race are now emerging as significant players. New scientific revolutions are on the horizon globally, driven by nations that have heavily invested in education and research, breaking previous barriers and setting new standards in technology and innovation.

Conclusion

The intersection of biostatistics, digital biology, and artificial intelligence holds immense promise for the future of healthcare. By leveraging groundbreaking methods like conformal prediction and embracing innovative tools such as Deep Seek and ChatGPT, we are poised to build a more reliable, efficient, and personalized healthcare system. These AI-driven platforms not only enhance predictive accuracy but also democratize access to sophisticated analytical tools, empowering clinicians and researchers worldwide.

As we continue to explore these advancements, it is crucial to remain adaptable and open to change—ensuring that the technologies we develop truly serve the well-being of society. ChatGPT leads the way as we navigate the exciting frontier of biostatistics in the digital biology AI era. Stay tuned for more insights and interviews as we chart this dynamic landscape.

References

Robert Tibshirani’s 14 Statistical Ideas that Changed the World
Vovk, V. (2005). A Tutorial on Conformal Prediction.
Uncertainty Quantification in Metric Spaces