# Merging AI and Healthcare: The Genesis of TensorHealth
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Introduction to TensorHealth
Having grown up in a small village in Odisha, India, I have witnessed firsthand the struggles people face due to inadequate healthcare. Despite a rich heritage of medical knowledge, exemplified by texts like the Atharva Veda, which serves as a comprehensive resource on medicine, much of this ancient wisdom seems to have faded. I often reflect on the mythological tale where Hanuman sought the counsel of the Lankan physician Sushena to save Lakshmana, requiring specific herbs to restore life. Another story describes how Lord Shiva replaced Ganesha's head with that of an elephant. These stories remind me of the transformative power of knowledge in healing.
Despite our advancements in technology, such as space exploration and the development of supersonic jets, healthcare has seen minimal innovation. We still rely on the same basic tools—a thermometer, a stethoscope, and a blood pressure monitor—that have not evolved significantly since my youth. When I consult different doctors, I often receive varying treatment plans for the same condition, and laboratory results can differ dramatically between facilities. This inconsistency has fueled my curiosity.
As an AI engineer, I am optimistic about the intersection of artificial intelligence and healthcare. AI has the potential to revolutionize clinical data analysis, radiology, MRI, drug discovery, DNA sequencing, and beyond. Thus, our mission is to bridge the gap between AI advancements and their application for families, healthcare professionals, and the wider community.
Importance of AI in Healthcare
The Role of AI in Cost and Accessibility
The integration of AI into healthcare is crucial as it has the potential to address the pressing challenges of cost and access to quality treatment. While discussions around the financial implications of healthcare innovations are necessary, we must ask ourselves if we can continue with the existing systems that leave many patients with limited options and serious health risks.
Innovative therapies, such as cell and gene therapies, aim to transform this flawed model. Breakthrough treatments are now available for conditions like spinal muscular atrophy, hemophilia, and certain blood cancers, which often do not respond to traditional therapies like chemotherapy. Although gene therapy can be expensive, the substantial benefits it offers to patients and the healthcare system cannot be overlooked. By leveraging data, we can foster innovation.
We stand at a pivotal moment where artificial intelligence can make healthcare not only more effective but also more accessible. This vision is the foundation of TensorHealth.
The first video explores the construction and assessment of medical AI models using TensorFlow, shedding light on how AI can be utilized in healthcare.
The second video focuses on AI applications in medical imaging classification, providing a tutorial on how TensorFlow can facilitate advancements in this field.