I will be taking a brief hiatus for the next three months. I’m going to be using this time to step back, reflect, and rework the format of the show to bring you even more valuable insights and engaging conversations. I’m looking forward to returning in the fall with fresh episodes, new guests, and even deeper dives into the challenges and opportunities shaping mission-driven machine learning-powered companies.

In the meantime, I'm thrilled to share another way you can continue to learn and engage with the world of AI through Pixel Scientia Labs. While the podcast is on pause, I invite you to explore our Webinar Initiative at pixelscientia.com/webinars.

Links:

• Webinars from Pixel Scientia

What role can artificial intelligence play in detecting breast cancer earlier, when it's most treatable? In this episode of Impact AI, we hear from Nico Karssemeijer, Chief Science Officer of ScreenPoint Medical, about how his team is using AI to transform breast cancer screening. Drawing on more than four decades of experience in medical imaging, Nico shares how ScreenPoint’s AI tools assist radiologists by analyzing mammograms, highlighting suspicious areas, and even learning from years of patient data. The conversation explores what it takes to build trustworthy medical AI, overcome challenges with data diversity and device bias, and the importance of clinical validation. To find out how AI is being integrated into real-world healthcare to improve outcomes (and what goes into building a successful AI-powered medical company), tune in today!

Key Points:

• What led Nico to turn decades of research into a breast imaging AI startup.
• How ScreenPoint uses AI to support radiologists in early detection.
• Challenges of working with diverse data from different imaging devices.
• The importance of training models with clean, representative data.
• Strategies for reducing bias across vendors and populations.
• How independent, real-world validation drives trust and clinical adoption.
• Finding a balance between model accuracy and explainability.
• Why domain expertise is crucial for building a successful AI-powered startup.
• Driving adoption in medical AI through clinical partnerships and rigorous trials.

Quotes:

“It’s amazing how much more information you can get out of the mammograms [using AI]. That surprises me all the time.” — Nico Karssemeijer

“You can't just say, ‘This mammogram is abnormal,’ because then [the radiologists] are puzzled. – The algorithm is getting so good that it identifies areas the radiologists would probably not see by themselves. – You have to – mark the area in the exam where a lesion is found.” — Nico Karssemeijer

“It's incredibly important to have enough domain expertise when you start a company, because it's easy to fail because you don't understand well enough what the customer wants [or] where the field is going.” — Nico Karssemeijer

Links:

Nico Karssemeijer

ScreenPoint Medical

Nico Karssemeijer on LinkedIn

Nico Karssemeijer on Google Scholar

Resources for Computer Vision Teams:

LinkedIn – Connect with Heather.

Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health.

Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project.

How can we harness medical imaging and artificial intelligence to shift healthcare from reactive to predictive? In this episode, I sit down with Ángel Alberich-Bayarri to discuss how artificial intelligence is revolutionizing radiology and precision medicine. Ángel is the CEO of Quibim, a company recognized globally for its AI-powered tools that turn radiological scans into predictive biomarkers, enabling more precise diagnoses and personalized treatments.

In our conversation, we hear how his early work in radiology and engineering led to the founding of Quibim and how the company’s AI-based technology transforms medical images into predictive biomarkers. We unpack the challenges of data heterogeneity, how Quibim tackles image harmonization using self-supervised learning, and why accounting for regulations is critical when building healthcare AI products. Ángel also shares his perspective on the value of model explainability, the concept of digital twins, and the future of preventative imaging. Join us to discover how AI is disrupting clinical decision-making and preventive healthcare with Ángel Alberich-Bayarri.

Key Points:

• Hear about Ángel’s background and how his career led to founding Quibim.
• Find out how Quibim turns radiology images into predictive clinical insights.
• Different use cases of Quibim’s technology and why biopsy data is important.
• He explains why Quibim avoids relying solely on radiologist annotations.
• Challenges of using medical imaging: data fragmentation and scanner variability.
• Explore Quibim’s self-supervised image learning harmonization techniques.
• How Quibim increases the explainability of the model while maintaining accuracy.
• Why understanding clinical workflows and radiologist adoption behavior is critical.
• Uncover how regulations influence the development of Quibim’s technology.
• Ángel’s advice for entrepreneurs and leaders of AI-powered startups.
• Quibim’s plans for predictive modeling, digital twins, and AI for preventative medicine.

Quotes:

“We would like AI to be able to mine all this hidden information we have right now in the images. Our vision is long-term, being able to understand what is happening until this point within the human body.” — Ángel Alberich-Bayarri

“What [Quibim is] investing in is the next frontier that not only detects and diagnoses disease, but also predicts or prognoses what is going to happen.” — Ángel Alberich-Bayarri

“Human behavior has a lot of nuances that need to be appreciated when AI is adopted.” — Ángel Alberich-Bayarri

“The bolder the claims you make, it’s the higher level of evidence you need to achieve.” — Ángel Alberich-Bayarri

“Taking care of health before we have symptoms, it’s just going to be a growing business, and therefore, a lot of AI tools will be needed to understand our inner us.” — Ángel Alberich-Bayarri

Links:

Ángel Alberich-Bayarri on LinkedIn

Ángel Alberich-Bayarri on X

Quibim

Resources for Computer Vision Teams:

LinkedIn – Connect with Heather.

Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health.

Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project.