Blog
Insights on medical AI R&D, with a focus on data AI-readiness, risk assessment, evaluation, and defensibility.
I explore the questions that shape medical AI systems before and beyond model development: whether data can support the intended use case, what the evidence actually allows us to claim, and how R&D decisions influence robustness, evaluation, and defensibility.
Helping readers build a solid understanding of essential AI and computer vision concepts.
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Dice Coefficient vs. IoU in Medical Image Segmentation
Dice Coefficient and Intersection over Union (IoU) are widely used to evaluate medical image segmentation. Both measure overlap between prediction and ground truth but behave differently. This post explains their differences, strengths, and offers guidance on choosing the right metric.
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What’s the Real Difference between Image Processing, Analysis, and Computer Vision?
Terms like image processing, image analysis, and computer vision are often used interchangeably, but do they really mean the same thing?
Clarifying nuanced questions, exploring trends, and sharing actionable know-how based on real-world challenges and research.
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Problem Framing in Medical Imaging AI
Two teams can work on the same medical imaging task and end up building very different systems. The difference often starts before any model is trained, at the moment the problem is defined. This article explores what problem framing actually means, where ambiguity comes from, and what a well-framed problem changes in practice.
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Understanding AI Values: Meanings, Subtleties & Nuances
AI ethics comes with a growing set of buzzwords: responsible, ethical, fair, transparent, explainable, and more. Some overlap, others differ in subtle ways. This post breaks down the key terms, explains their nuances, and points you to serious resources for deeper exploration.
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Data Distillation in Medical Imaging
Data Distillation in Medical Imaging Reducing Data Dependency ยท Part 3 Medical imaging faces persistent data challenges: strict privacy constraints, high annotation costs, limited and imbalanced datasets, and the logistical burden of…
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Medical Image Augmentation & Synthetic Data
Augmentation and synthetic data now play a central role in medical imaging AI, expanding datasets, improving robustness and enabling new applications such as domain translation, privacy preservation and rare-case simulation.
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Training Models With Little or No Labels
This article explores label-efficient learning approaches: semi-, unsupervised, and self-supervised learning, that enable AI models to generalize better in data-scarce domains such as medical imaging.
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The Data Bottleneck in Medical Imaging
The Data Bottleneck in Medical Imaging Medical imaging AI has achieved remarkable progress, but most breakthroughs still rely on massive, labeled datasets: a luxury often out of reach in healthcare….
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How Absolute is the “Ground Truth” in Medical AI Annotation?
In architecting safe-by-design systems, trustworthy AI must be addressed at every stage of the development lifecycle. One of the most persistent assumptions is the absolute certainty of medical annotation.
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Can Metadata be trusted as Ground Truth for Medical Imaging AI?
Metadata is often used as ground truth in medical imaging AI, but its availability does not guarantee its reliability. This article explores how a metadata traceability assessment can reveal inconsistencies, missing information and manufacturer-dependent limitations before model development begins.
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Scalable Dataset Triage in Medical Imaging AI
Scalable Dataset Triage in Medical Imaging AI Teams often receive large imaging datasets from multiple hospitals or partners. At first glance, the dataset may look ready for model development: thousands…
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Data Auditing: The Foundation of Responsible AI in Medical Imaging
Before training any neural network, before tuning hyperparameters or optimizing inference times, the first responsible step is to audit your data.
