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In this episode, I sit down with Dr. Nina Kottler, Associate Chief Medical Officer of Clinical AI at Radiology Partners, to dive into the evolving role of AI in radiology and how it can shape the future of digital pathology. Dr. Kottler shares her unique journey, expertise, and practical frameworks for implementing AI that enhance patient care and streamline diagnostic workflows.

Episode Highlights and Key Moments:

• [00:00:45] Introduction to Dr. Nina Kottler
  Dr. Kottler discusses her background in applied mathematics, her journey into medicine, and her work at Radiology Partners, where she combines clinical practice with AI innovation.
• [00:04:30] Breaking Down Complex Problems in AI
  Nina explains her approach to tackling large clinical challenges by breaking them down into manageable parts, a method that’s essential for developing and optimizing AI solutions.
• [00:08:15] The Role of Data Orchestration
  We dig into “data orchestration” and how ensuring data is aligned with the right AI model is key to producing accurate and reliable clinical outcomes.
• [00:11:45] Life Cycle of an Exam in Radiology
  Nina takes us through each step in the radiology workflow—from the initial patient consultation to reporting—and highlights how AI can streamline and enhance each phase.
• [00:17:00] Evolution of AI Models in Healthcare
  We explore how AI has evolved, from early CAD systems to today’s multimodal and transformer models, and the exciting possibilities they bring to both radiology and pathology.
• [00:23:20] Addressing the Lag in AI Adoption in Healthcare
  We discuss the challenge of keeping up with AI advancements while balancing patient safety, regulatory standards, and the need for reliability in clinical settings.
• [00:27:50] Frameworks for Reducing Variability and Improving Accuracy
  Nina shares actionable frameworks that Radiology Partners uses to reduce variability and improve diagnostic precision—strategies that pathology can learn from.
• [00:32:40] AI in Workflow Optimization: Where It Has Real Impact
  We discuss specific use cases in clinical workflows that show where AI can bring the greatest value, especially in enhancing patient care through optimized processes.
• [00:36:50] The Power of Multimodal AI and Vision-Language Models
  Combining large language models with computer vision is moving diagnostics closer to comprehensive, AI-driven care—a promising development we explore in depth.
• [00:42:15] The Future of Agents in AI
  We dive into the concept of “agents” in AI and how these systems may soon coordinate multiple models for more complex and precise clinical analyses.
• [00:48:10] Where to Learn More about Dr. Nina Kottler’s Work
  Nina shares where you can catch her upcoming talks and presentations, plus resources for staying updated on the latest in AI for radiology and digital pathology.

If you're a pathologist, radiologist, or healthcare professional curious about AI’s impact on diagnostics, this episode is packed with practical guidance on integrating AI into clinical workflows. Join us as we explore how AI is shaping the future of radiology and pathology!

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Welcome back to the DigiPath Digest, fresh from PathVision!

In this episode we will dive into the latest updates from the PathVision conference, covering trends in AI-driven diagnostics, the expansion of digital pathology into primary care, and the exciting new frontier of glassless pathology.

Join me as I recap the highlights of PathVision and the latest updates from the digital pathology literature, including discussions on:

• AI Integration in Pathology: Learn how AI is advancing breast cancer diagnostics with tools like Ki-67 scoring models and multi-label AI for mammography, aimed at reducing unnecessary biopsies.

• Global Health & Digital Microscopy: Hear about innovative projects from Sweden and Finland focused on AI-supported digital microscopy in primary healthcare labs, bringing accessible diagnostics to underserved areas.

• Glassless Pathology with MUSE: Discover how glassless pathology is changing tissue imaging with MUSE (Microscopy with UV Surface Excitation), enabling diagnostics without the need for traditional glass slides. Dr. Zuraw breaks down what this means for future pathology workflows.

Plus, a shout-out to the vendors and partners making these advancements possible, and insights from Dr. Zuraw’s conversations with digital pathology trailblazers from around the globe, including new developments from Asia in digital pathology education and technology.

Timestamps:

• [0:00] PathVision Highlights & Global Attendees
• [5:15] AI in Diagnostic Workflows: Dr. Anil Parwani’s “Pathology Train Ride”
• [12:30] Moving Beyond Narrow AI: Multimodal and Foundational Models
• [18:45] Glassless Pathology: A New Frontier with MUSE Microscopy
• [25:10] Integrating Digital Microscopy in Global Health Labs
• [32:00] Breast Cancer Month: New Advances in AI for Diagnostics
• [42:00] One Health & AI for Disease Detection in Primary Care
• [48:30] Special Interviews: Jun Fukuoka and Asian Society of Digital Pathology

Links and Resources:

• Subscribe to Digital Pathology Podcast on YouTube
• Pathology News
• Signify Research Monthly Recap
• YouTube version of this episode

Publications Discussed Today:

📝 AI-Supported Digital Microscopy Diagnostics in Primary Health Care Laboratories: Protocol for a Scoping Review
🔗https://pubmed.ncbi.nlm.nih.gov/39486020/

📝 Ki-67 evaluation using deep-learning model-assisted digital image analysis in breast cancer
🔗https://pubmed.ncbi.nlm.nih.gov/39478421/

📝A Multi-label Artificial Intelligence Approach for Improving Breast Cancer Detection With Mammographic Image Analysis
🔗https://pubmed.ncbi.nlm.nih.gov/39477432/

📝 A comprehensive evaluation of an artificial intelligence based digital pathology to monitor large-scale deworming programs against soil-transmitted helminths: A study protocol
🔗 https://pubmed.ncbi.nlm.nih.gov/39466830/


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In this episode, I meet with Adam Cole, MD, and Jason Camilletti about how digital pathology transforms the field. Adam, the CEO of TruCore Pathology, and Jason, the CEO of PathNet Labs, share their unique journeys from the military to becoming digital pathology leaders. We explore their experiences, challenges, and innovations in integrating AI and digital tools into their practices.

Key Topics Discussed:

• [00:00:00] Introduction to AI in Pathology
• [00:01:00] Adam and Jason’s Military Backgrounds
• [00:05:00] Adam’s Story of Becoming a Mobile Pathologist
• [00:10:00] The Move to Fully Digital Pathology
• [00:14:30] AI’s Role in Pathology
• [00:20:00] Challenges in Implementing Digital Pathology
• [00:25:00] Improving Patient Outcomes with Digital Tools
• [00:29:00] Digital Pathology’s Impact on Patient Care
• [00:38:00] Using AI for Quantifying Tumor Volume
• [00:40:00] The Role of AI in Enhancing Diagnostics

Adam and Jason emphasize the immense potential of AI in pathology, but also the need for thoughtful integration. The future of pathology lies in using digital tools to provide faster, more accurate diagnoses while maintaining the critical human element. Tune in to learn how AI is reshaping the field and what it means for both pathologists and patients.

THIS EPISODE'S RESOURCES:

• TruCore website
• PathNet Website

OTHER EPISODES YOU MIGHT LIKE:

• The Evolution of Digital Pathology – from Improved Histology Quality to Fair Use of Pathology Data w/ Matthew O. Leavitt, DDx Foundation
• Achieving work-life balance in medicine as a pathologist with digital pathology w/ Todd Randolph, MD

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What does the FDA jurisdiction for LDTs mean for the labs? Do they need to worry? How do they need to change the way they operate?

In this episode, I talk with Dr. Thomas Nifong, a clinical pathologist and VP of CDX operations at Acrovan Therapeutics, about the recent FDA ruling on laboratory-developed tests (LDTs) issued on May 6th, 2024. We discuss the implications of considering LDTs as medical devices, requiring regulation, and explore the authority of FDA versus CLIA. The conversation also covers historical contexts, practical implications of regulatory changes, and the roles of organizations like CAP, ACLA, and AMP in legal challenges against the FDA. We dive into the differences in requirements between CLIA and FDA, New York's alternative approval route, and potential impacts on lab operations and compliance. Join us for an insightful conversation filled with essential information for those in the field of molecular pathology.

00:00 Introduction and Special Guest Announcement
00:24 FDA's New Rule on Laboratory Developed Tests (LDTs)
01:58 Recording the Podcast: A Casual Lunch Conversation
03:47 Understanding FDA's Authority Over Medical Devices
08:07 Disputes and Legal Challenges
12:03 Practical Implications and Industry Reactions
12:47 Understanding FDA's Focus: Safety and Efficacy
14:11 The Role of CMS and Medical Necessity
14:48 Congressional Involvement and Legal Authority
16:06 Impact on Labs and Future LDTs
18:33 Quality Systems and Compliance
20:16 Modifications and Software Updates
21:16 Conclusion and Next Steps

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In this episode, I had a fascinating conversation with Candice Chu, DVM, PhD, DACVP, about how artificial intelligence (AI) is reshaping veterinary diagnostics and education. Candice, a clinical pathologist and educator at Texas A&M, is using AI tools like ChatGPT to improve efficiency in clinical workflows and academic processes. We explored the practical applications of AI, ethical concerns, and its future impact on veterinary medicine.

Key Topics Discussed:

• [00:00:00] Introduction to AI in Veterinary Education and Diagnostics
  I ask Candice how AI is changing veterinary education and diagnostics, and she explains how AI is boosting efficiency in both areas.
• [00:01:00] Candice’s Journey in Veterinary Medicine
  Candice shares her journey from Taiwan to the U.S., her career in veterinary pathology, and becoming an educator at Texas A&M.
• [00:05:00] Custom GPT Model for Clinical Pathology
  Candice describes the development of her custom GPT model for clinical pathology and its role in improving diagnostic efficiency.
• [00:10:00] AI Tools for Academic and Clinical Efficiency
  We talk about how AI tools reduce repetitive tasks, giving professionals more time for critical thinking and decision-making.
• [00:14:30] Ethical Concerns When Using AI in Veterinary Medicine
  Candice emphasizes the ethical responsibility of using AI, highlighting the importance of human judgment in AI-assisted diagnostics.
• [00:20:00] How Veterinary Students Can Leverage AI
  Candice shares tips on how students can use AI to enhance learning, from simplifying research to generating case questions.
• [00:29:00] AI’s Role in Academic Writing and Veterinary Practice
  We discuss how AI tools streamline academic writing and research, and how AI will continue shaping veterinary practice in the future.
• [00:39:00] Critical Thinking and AI in Veterinary Medicine
  Candice and I conclude by discussing how critical thinking and professional responsibility are essential when using AI tools.

Candice highlighted the transformative role AI can play in both veterinary education and diagnostics, improving efficiency while requiring responsible use. While AI tools like ChatGPT offer many benefits, the human element—our critical thinking and judgment—remains crucial in ensuring accurate results and ethical practices.

This episode provides practical insights on how veterinary professionals, educators, and students can harness AI to streamline workflows and improve diagnostic accuracy. Be sure to listen to the full conversation for actionable tips on integrating AI into your practice!

EPISODE RESOURCES:

• About Dr. Candice Chu (Including her social media and achievements)
• Candice's Paper
• Undermind AI
• Youtube Episode of this Episode

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In this episode, Dr. Richard Fox shares how AI is transforming veterinary diagnostics. From his early career to the world of AI, Dr. Fox offers practical insights into the challenges, opportunities, and innovations that AI brings to pathology. Tune in to learn how AI is enhancing workflow efficiency, diagnostic precision, and the future direction of veterinary pathology.

[00:00] Introduction – Introduction to Dr. Richard Fox and his expertise in veterinary pathology and AI.

[03:00] Dr. Fox’s Career Journey – His shift from veterinary practice to pathology and AI.

[08:00] Entering the AI Space – How Dr. Fox became involved in AI, including his work with Aiforia.

[15:00] AI in Diagnostics – AI’s impact on diagnostic workflows and speeding up tasks.

[22:00] Quality Control in AI Models – Ensuring AI model accuracy and the importance of data consistency.

[28:00] AI Model Validation Challenges – Overcoming issues with model validation and retraining.

[35:00] Integrating AI into Workflows – How AI fits into veterinary pathology workflows and practical considerations.

[40:00] Future of AI in Pathology – Predictions on the future trends in AI and on-premises diagnostics.

[50:00] Common Questions About AI – Addressing concerns like AI replacing pathologists and optimizing workflows.

[58:00] Conclusion – Key takeaways and how to get started with AI in veterinary diagnostics.

The Episodes Resources:
Contact Aiforia
Richard Fox's LinkedIn Profile
Richard Fox's Email

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In this 14th episode of DigiPath Digest, I introduce a new course on AI in pathology, designed to help pathologists understand and confidently navigate AI technologies.

The episode focuses on various research studies that highlight the integration and effectiveness of AI in pathology, particularly in colorectal biopsies and kidney transplant biopsies, emphasizing the importance of seamless workflow integration.

You will also learn about challenges in manual assessment of tumor-infiltrating lymphocytes and HER2 expression in breast cancer. I advocate for more consistent and precise AI-driven approaches.

And there an opportunity for a discounted beta test of the new AI course.

00:00 Welcome to DigiPath Digest #14

00:24 New AI Course Announcement

01:51 Deep Learning in Colorectal Biopsies

09:17 AI in Kidney Biopsy Evaluation

16:12 Automated Scoring of Tumor Infiltrating Lymphocytes

24:22 AI for HER2 Expression in Breast Cancer

31:13 Conclusion and Course Details

THIS EPISODE'S RESOURCES

📰 A deep learning approach to case prioritisation of colorectal biopsies
🔗 https://pubmed.ncbi.nlm.nih.gov/39360579/


📰 Galileo-an Artificial Intelligence tool for evaluating pre-implantation kidney biopsies
🔗 https://pubmed.ncbi.nlm.nih.gov/39356416/


📰 Automated scoring methods for quantitative interpretation of Tumour infiltrating lymphocytes (TILs) in breast cancer: a systematic review
🔗 https://pubmed.ncbi.nlm.nih.gov/39350098/


📰 Precision HER2: a comprehensive AI system for accurate and consistent evaluation of HER2 expression in invasive breast Cancer
🔗 https://pubmed.ncbi.nlm.nih.gov/39350085/


▶️ YouTube Version of this Episode:
🔗 https://www.youtube.com/live/jkT8dTxelt4?si=xT6MNH7O4HuUnAN6

📕 Digital Pathology 101 E-book
🔗https://digitalpathology.club/digital-pathology-beginners-guide-notification

🤖 "Pathology's AI Makeover" Online Course 50% OFF
🔗 Let me know that you are interested in LinkedIn (just 10 spots available)

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Good morning, digital pathology trailblazers! Welcome to another exciting exploration of digital pathology and AI. I’m thrilled to have our global community here with us today from so many different time zones. Before we dive into today's content, a quick note: my equipment is being a bit finicky, but that’s life in the digital world!

Integrating Image Analysis with AI

Let's kick off with a recap of some recent updates. Yesterday, I had the privilege of presenting to a mixed group at Cincinnati Children’s Hospital. We discussed AI in image analysis, an essential tool bridging radiology and pathology as these fields rapidly evolve with new technologies like foundation models and large language models. A diverse audience—ranging from radiologists to pathologists—prompted me to adapt my presentation style on the spot. It was a dynamic discussion about the advancements in healthcare that shared perspectives from both sides.

Lymphovascular Invasion: A Case Study

Our first paper today focuses on a deep learning model for identifying lymphovascular invasion (LVI) in lung adenocarcinoma. This significant prognostic factor is crucial for advancing diagnostic consistency and reliability. Unlike broad foundation models, this work engages with dedicated image analysis applications targeting specific diagnostic challenges. The study demonstrated reduced pathologist evaluation time by nearly 17% and even more in complex cases, aligning with previous findings that AI enhances efficiency by around 21%.

AI Collaborations: Human and Veterinary Pathology

Next, we delve into a collaborative effort between human and veterinary pathologists, emphasizing the promise of AI integration in telepathology and digital pathology. These fields are converging to enhance information exchange, teaching, and research. I’m particularly excited about this paper due to my own veterinary pathology background and the potential it offers for both educational and clinical practices.

Spatial Profiling and Immuno-Oncology

We then journey into the intricate landscape of immuno-oncology with a study on PD-1 and PD-L1 in osteosarcoma microenvironments. Utilizing deep learning and multiplex fluorescence immunohistochemistry, researchers highlighted the spatial orchestration of these markers, providing insights into potential immunotherapeutic strategies. This work is an exemplar of how AI can illuminate complex biological landscapes, offering a path for future therapies.

Conclusion

Thank you all for joining this vibrant discussion. Whether you’re tuning in from early morning in Atlanta or late at night in Algeria, your engagement enriches our learning experience. Keep an eye out for more content and upcoming courses designed to unpack these groundbreaking developments in AI and digital pathology.

Until next time, keep blazing trails in digital pathology!

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Become a Digital Pathology Trailblazer get the "Digital Pathology 101" FREE E-book and join us!