The Effective Statistician - in association with PSI

Taming AI for Biostatistics: Darko Medin on Bio AI Works & Reliable AI Models

Feb 27, 2025

Darko Medin, a biostatistician specializing in AI products, dives into the challenges and innovations within biostatistics. He discusses the critical issues of AI hallucinations and the need for reliable outputs. Darko shares exciting applications in oncology and precision medicine, emphasizing the importance of validation and domain-specific rules to enhance model accuracy. The conversation also tackles the complexities of high-dimensional data and the vital balance between interpretability and explainability in AI models.

28:38

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Episode guests

Darko Medin

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Episode notes

Podcast summary created with Snipd AI

Quick takeaways

AI models in biostatistics face challenges like hallucinations and accuracy, necessitating rigorous validation and domain-specific rules for reliability.

The integration of AI in oncology and precision medicine showcases its potential to uncover hidden patterns in complex datasets and enhance research outcomes.

Deep dives

Integration of Biostatistics and AI

The discussion highlights the creation of BioAI Works, a platform aimed at bridging biostatistics and artificial intelligence. It focuses on addressing specific challenges faced by AI models, particularly in the context of scientific and statistical accuracy. By partnering with OIA Analytica, the goal is to develop diverse AI models that maintain a high level of reliability through rigorous checks and validation processes. This initiative aims to ensure that the outputs from AI models are not only innovative but also scientifically sound and applicable in various research domains.

Intro

2min

Integrating Biostatistics and AI: Enhancing Scientific Accuracy

6min

Enhancing AI Reliability Through Statistical Validation

2min

AI Hallucinations: Understanding and Navigating Risks

16min

Understanding AI Interpretability in Biostatistics

4min

Interview with Darko Medin

Key Points:

AI Challenges: Hallucinations, Accuracy, Reliability
AI Solutions: Validation, Cross-checking, Domain-specific rules
Use Cases: Oncology, Precision Medicine, Drug Discovery
High-dimensional Data: Hidden patterns, Complex datasets
AI Agents: Semi-autonomous, Goal-driven, Multi-step processes
Interpretability vs. Explainability: Statistical rigor, Scientific validation
Future of AI: Scaling, Faster iteration, Reliable outputs

Artificial intelligence is rapidly transforming biostatistics, but ensuring its accuracy and reliability remains a critical challenge. In this episode, Darko Medin shares valuable insights into how Bio AI Works is tackling these issues, from reducing hallucinations in large language models to uncovering hidden patterns in complex datasets.

If you’re interested in how AI can enhance statistical rigor and drive innovation in fields like oncology and precision medicine, you won’t want to miss this conversation.

Tune in now, and if you found this episode insightful, share it with your friends and colleagues who would benefit from learning about AI’s role in biostatistics!

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