# "Tecnología con impacto real en salud" | Ana Gorodisch | TEDxBarrioSanNicolasSalon Source: https://www.youtube.com/watch?v=Uy7ctOs-KZI Recap page: https://rapidrecap.app/video/Uy7ctOs-KZI Generated: 2025-11-20T17:50:50.375+00:00 --- ## Quick Overview Ana Gorodisch, a bioengineer, details the development of an AI-powered technology that analyzes biopsy images to detect cancer biomarkers with 95% accuracy in seconds, aiming to democratize oncology diagnostics by making them accessible, fast, and economical, especially in regions where current complex laboratory methods are unavailable or too costly. **Key Points:** - The speaker and her team developed an Artificial Intelligence (AI) tool to classify cancer based solely on biopsy images, achieving 95% accuracy in five seconds. - This technology targets the critical gap where traditional, complex, and expensive laboratory diagnostic inputs are inaccessible to many patients, particularly in Argentina and the region. - The team initially applied the technology to detect a colon cancer biomarker, later expanding it to endometrial cancer, achieving even better results in the latter. - The speaker highlights that the process of developing and validating the algorithm required six months of intensive work, including clinical application and data generation. - She emphasizes that the engineers, who were not medical doctors or biologists, found their engineering skills uniquely suited to bridging the gap between complex medical data and practical application. - The ultimate goal is to democratize access to oncological diagnostics by creating accessible, fast, and economical tools. - The speaker credits the intense collaboration and sacrifice of her founding team (four best friends) for their success, including foregoing personal time for the project. ![Screenshot at 00:14: The initial slide displaying a magnified, stained tissue sample \(biopsy\) illustrating the type of visual data the AI technology analyzes for cancer detection.](https://ss.rapidrecap.app/screens/Uy7ctOs-KZI/00-00-14.png) **Context:** Ana Gorodisch, a bioengineer, recounts the journey of developing a novel diagnostic tool that leverages artificial intelligence to analyze biopsy images for cancer detection. She frames this work against the backdrop of the emotional difficulty patients face upon receiving a cancer diagnosis without immediate answers, and the current inaccessibility of advanced diagnostic methods in many areas. Her presentation, delivered at TEDxBarrioSanNicolasSalon, emphasizes the intersection of engineering and medicine to create impactful, democratic healthcare solutions. ## Detailed Analysis Ana Gorodisch explains that when a patient receives a cancer diagnosis, the subsequent waiting period for results and treatment plans causes immense emotional distress for the patient, their family, and friends. She introduces a technology, developed by her team of bioengineers (who were not doctors or programmers), that uses AI to analyze biopsy images directly, determining if a tumor will respond to various treatments or predicting other biological responses. This method achieves 95% accuracy in five seconds, outperforming traditional methods that require complex, costly biological inputs and often take a month to yield results. The team first tested this on a colon cancer biomarker, then successfully applied it to endometrial cancer with better results. The development involved six months of intense work, validation, and clinical application. Gorodisch points out that the core value of their project is its ability to democratize cancer diagnostics by making them accessible, fast, and economical, especially in underserved regions where advanced lab equipment is scarce. She concludes by reflecting on the personal sacrifice and intense collaboration among her founding team—five friends who dropped other commitments to pursue this vision—and the satisfaction derived from building tools that bridge engineering and medicine for real-world impact. ### The Problem of Uncertainty - The moment a cancer diagnosis is delivered often leads to a worst day for the patient, followed by uncertainty about prognosis, treatment, and duration, which the current diagnostic process exacerbates. ### Biomarker Detection via AI - The technology analyzes biopsy images using AI to detect specific biological characteristics (biomarkers) that indicate how a tumor will react to available therapies, bypassing slow, costly laboratory inputs. ### Project Development and Validation - The team spent six months programming and validating the AI, which achieved 95% accuracy in five seconds, successfully applying it first to colon cancer and then to endometrial cancer. ### The Interdisciplinary Team - The founders, primarily bioengineers, initially struggled to fit into the traditional medical/oncology/biology framework but realized their engineering skill set was perfect for bridging data interpretation gaps. ### Impact and Democratization - The goal is to make these diagnostics accessible, fast, and economical, countering the bias inherent in centralized, high-resource diagnostic centers, and scaling the technology internationally. ### Personal Motivation - The speaker expresses deep emotion regarding the team's commitment, noting that she and her four friends sacrificed their jobs to focus entirely on this project, driven by a desire to create something with real impact. ![Screenshot at 00:14: A microscopic image of stained tissue \(biopsy\) displayed on the screen, representing the input data for the AI diagnostic tool.](https://ss.rapidrecap.app/screens/Uy7ctOs-KZI/00-00-14.png) ![Screenshot at 02:42: A graphic showing three interconnected pink circles labeled 'ACCESIBLE', 'RÁPIDO', and 'ECONÓMICO', defining the core values of their developed technology.](https://ss.rapidrecap.app/screens/Uy7ctOs-KZI/00-02-42.png) ![Screenshot at 05:39: A split image showing the team presenting their work at a conference \(left\) and another professional presentation slide \(right\) detailing their work on a deep learning algorithm for cancer diagnosis.](https://ss.rapidrecap.app/screens/Uy7ctOs-KZI/00-05-39.png) ![Screenshot at 07:07: The speaker gesturing emphatically while discussing the contrast between the 'sweet' fascination of oncology and the 'bitter' reality of not understanding their engineering role.](https://ss.rapidrecap.app/screens/Uy7ctOs-KZI/00-07-07.png) ![Screenshot at 08:33: The audience applauding enthusiastically at the conclusion of the presentation, showing positive reception to the talk.](https://ss.rapidrecap.app/screens/Uy7ctOs-KZI/00-08-33.png)