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Smart, Precision and Preventive Medicine

Precision Medicine_060422A
[Precision Medicine, Hussien Heshmat]

Transforming Health Through Accurate Understanding of Genes, Environment and Lifestyle



- Precision Medicine

Medicine is difficult because each patient is different. Doctors keep saying "Medicine is not an exact science" The individual differences of patients make choosing therapy and applying it to different clinical scenarios very challenging. How to tailor medicine for each and every individual person? How can genetic testing guide us in the choice of anti-platelets? How can these polymorphisms determine the response of a patient to chemotherapy or to warfarin? How can these tests be incorporated into daily practice? Mixing clinical variables, genetic variants, and molecular profiles, all into Artificial Intelligence can lead to "Precision Medicine".

Precision medicine aims to collect, connect, and apply vast amounts of scientific research data and information about our health to understand why individuals respond differently to treatments and therapies, and help guide more precise and predictive medicine worldwide." 


- Data Science and Modern Medicine

The analogy to finding clues to a disease through vast amounts of data is finding a needle in a haystack. But by applying "rigorous statistical methods, data scientists hope to find out exactly where that needle is. Algorithms, artificial intelligence, machine learning and other technologies are changing the way doctors identify, treat and manage disease. 

In the past, the process of understanding disease can be slow and laborious. Years ago, to see if their hypothesis held true, scientists often had to manually curate and review data to begin to get a clearer picture of how the disease behaves. 

Today, thanks to cutting-edge tools and applications in data science, researchers can grasp how diseases behave on a faster timeline. Think about it: Just two years after the emergence of the SARS-CoV-2 virus and COVID-19 (the disease it causes), scientists are already understanding how the virus infects the body, how to help treat it, and how to reduce the risk of serious illness—the Thanks in large part to unprecedented data sharing among researchers from around the world. 


- Big Data Technologies and Biomedical Research

Big data technologies are increasingly used for biomedical and healthcare informatics research. Large amounts of biological and clinical data have been generated and collected at an unprecedented speed and scale. For example, the new generation of sequencing technologies enables the processing of billions of DNA sequence data per day, and the application of electronic health records (EHRs) is documenting large amounts of patient data. Big data applications present new opportunities to discover new knowledge and create novel methods to improve the quality of healthcare. 

To address the challenges of big data, innovative technologies are needed. Parallel, distributed computing paradigms, scalable machine learning algorithms, and real-time querying are key to analysis of big data. Distributed file systems, computing clusters, cloud computing, and data stores supporting data variety and agility are also necessary to provide the infrastructure for processing of big data. Workflows provide an intuitive, reusable, scalable and reproducible way to process big data to gain verifiable value from it in and enable application of same methods to different datasets.


- AI in Medicine

Artificial intelligence (AI) refers to the application of machines to mimic intelligent behavior to solve complex tasks with minimal human intervention, including machine learning and deep learning. 

The application of AI in medicine has improved healthcare systems in multiple areas, including diagnosis confirmation, risk stratification, analysis, prognosis prediction, treatment monitoring, and virtual health support, and has great potential to revolutionize and reshape medicine.

For example, in immunotherapy, AI has been applied to unlock potential immune signatures to indirectly correlate with response to immunotherapy and directly predict response to immunotherapy. Considering the outstanding capabilities in selecting appropriate subjects, improving treatment regimens, and predicting individualized prognosis, AI-based high-throughput sequence and medical image analysis can provide useful information for the management of cancer immunotherapy.


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