Teaching Medical Informatics and Informatics Pathology 2017-2019

Teaching of undergraduate Medical Technology students at Universidad San Sebastian (Campus Los Leones, Santiago) across three consecutive academic years (2017, 2018, 2019). Unlike the graduate-level Medical Informatics module I taught at Universidad de Chile, this was an undergraduate course for students whose primary career orientation was clinical laboratory practice, not research or engineering. The pedagogical challenge was fundamentally different: making optics, image acquisition, and digital image processing accessible and relevant to students focused on becoming clinical professionals.

This teaching was carried out in parallel with my roles as a researcher at the ALGES Laboratory (Universidad de Chile) and as founder of Micromundo SpA, a technology startup focused on microscopy and image analysis solutions.

Context and approach

The course covered Pathology Informatics — the intersection of digital imaging and clinical pathology. The central question for students was practical: how are microscopy images acquired, processed, and analyzed in modern clinical and anatomical pathology laboratories? Topics were framed around the workflow a medical technologist encounters in practice:

• Image acquisition: principles of brightfield and fluorescence microscopy optics, camera sensors, digitization, and whole-slide imaging
• Image quality: noise sources, illumination correction, color calibration, and what constitutes a reliable digital specimen
• Digital image processing fundamentals: filtering, segmentation, thresholding, and morphological operations applied to histological and cytological images
• Quantitative analysis: cell counting, area measurement, staining intensity quantification, and their relevance to clinical reporting
• Informatics in pathology: digital pathology workflows, image storage and retrieval, and the emerging role of computational tools in diagnostic laboratories

Curriculum evolution over three years

Three consecutive years of teaching allowed significant curriculum refinement:

• 2017: Initial offering, focused on establishing the core structure and gauging the appropriate level of mathematical detail for this student population
• 2018: Revised practicals based on 2017 feedback — more clinically grounded exercises using histopathology image datasets, reduced emphasis on mathematical derivations in favor of applied interpretation
• 2019: Mature version of the course with a well-calibrated balance between conceptual understanding and hands-on skill development, including a final project where students completed a full image analysis pipeline on real pathology specimens

Hands-on practicals

Practical sessions were the backbone of the course. Students worked with real microscopy images from pathology contexts, learning to use open-source scientific software for tasks they would encounter in clinical practice: measuring structures, segmenting regions of interest, and producing quantitative reports from image data.

Software

1. FIJI
2. ImageJ