India-US To Jointly Develop AI Platform To Combat Cancer, Diabetes

A number of significant agreements between the two nations in a variety of industries, including healthcare, have been signed as a result of Prime Minister Narendra Modi’s visit to the United States.

In order to establish an AI-enabled digital pathology platform for cancer diagnosis, prognosis, and therapeutic benefit prediction as well as AI-based automated radiation treatment for malignancies of the cervix, head, and neck, the U.S. National Cancer Institute will work with Indian experts.

Additionally, a contract to advance fundamental, clinical, and translational research on diabetes will be signed by the Indian Council of Medical Research (ICMR) and the U.S. National Institute of Diabetes and Digestive and Kidney Diseases.

AI may help medical practitioners with a variety of patient care and intelligent health systems. Machine learning and deep learning techniques are frequently used in healthcare for disease diagnosis, drug discovery, and patient risk detection.

The employment of AI approaches in a variety of health-related fields provides previously unheard-of chances to recover patient and clinical group results, lower costs, and detect the diagnosis of many types of disorders.

A wide range of AI-based applications have recently been investigated in digital pathology, biomarker creation, and therapeutics.

These applications include developing biomarkers to predict therapy selection and response and innovative analytical methodologies for realizing fresh data derived from conventional histology to advise treatment choice.

Therapeutics include variable dosing, combination regimen optimization, AI-driven drug target discovery, and more.

As a result of the ongoing advancements in AI, workflows that effectively combine diverse AI innovation strands have been created, greatly enhancing the diagnostic and interventional toolkits available to the clinical oncology community.

Clinical, engineering, execution, and health care economics advice are given in order to address difficulties in the conceptualization, confirmation, and implementation of AI in clinical cancer.

Lack of well-organized systems for integrating and resolving these data in advance of their existing silos is a challenge for the expansion of healthcare data. However, many frameworks and concepts make it easier to summarize and produce enough data for AI.

The AI community must incorporate active best practices of principled inclusion, software growth, scientific implementation, and individual-workstation interaction to create a unified best practice method for execution and safety.

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Understanding the use and applicability of various techniques such as SVM, KNN, Naive Bayes, Decision Tree, Ada Boost, Random Forest, K-Mean clustering, RNN, Convolutional neural networks (CNN), Deep-CNN, Generative Adversarial Networks (GAN), and Long short-term memory (LSTM) for various disease detection systems is crucial to comprehending how AI aids in the diagnosis and prediction of a disease.