AI for Medical Imaging

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AIforMedicalImaging Copyright (©) 2023

A Guide to Developing AI systems in Medical Imaging

A re you interested in developing cutting-edge Artificial Intelligence (AI) systems tailored for medical applications? If you're considering embarking on this exciting journey, there are crucial factors to keep in mind right from the outset. Building AI products for healthcare presents unique challenges distinct from those in other fields. In this blog, lets go in detail about the journey of developing and deploying AI products in healthcare domain. 1. Clinical Problem Statement First and foremost thing is identifying a problem statement with a true clinical need. While this may seem like an obvious starting point, it's essential to recognize that not all machine learning problem statements in clinical applications align with real-world clinical requirements. Imagine this scenario: There exists a non-invasive, cost-effective solution for a particular medical condition. Now, as an AI enthusiast, you're intrigued by the prospect of developing a new AI-driven product to ta

Thermal AI Open Source datasets: Part I

A re you intrigued by the potential of thermal imaging for clinical applications and eager to delve into developing AI algorithms using open source datasets? Look no further! In this Part I of the blog, we'll introduce you to a diverse thermal AI datasets tailored for different clinical use cases. These datasets can serve as foundational resources for developing and training initial versions of AI algorithms aimed at revolutionizing healthcare by combining AI with this affordable, non-invasive, non-contact and radiation free thermal imaging. .

AI over Thermal Images for Medical Applications: Thermal Imaging History and Manual Interpretation Challenges

A Quick History of Thermal Imaging for Medical Applications

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