Personal tools
You are here: Home Research Trends & Opportunities Life Sciences, Biomedical and ICT Convergence Smart, Precision and Preventive Medicine

Smart, Precision and Preventive Medicine

Precision Medicine_060422A
[Precision Medicine, Hussien Heshmat]

Transforming Health Through Accurate Understanding of Genes, Environment and Lifestyle



- Overview

Precision medicine and preventive medicine are complementary approaches to health care. Precision medicine is a personalized approach that aims to deliver the right treatment at the right time. It uses data such as genetic information to predict which treatments are most likely to be effective for patients.

Preventive medicine is probabilistic and applies to common diseases such as hypertension and hyperlipidemia. It emphasizes the “how” of prevention, such as the most cost-effective method of implementation.

Precision medicine has been used for many years to reduce the risk of complications, for example when a transfusion recipient's blood type matches the donor's blood type.

Precision medicine can be used to treat the following conditions: Breast and ovarian cancer, colorectal cancer, cystic fibrosis, diabetes, heart disease, hereditary hemochromatosis.

Precision medicine can also be used for prevention. For example, precision preventive medicine uses data from electronic health records, genomic testing, and genetic testing of diseased tissue to develop targeted preventive measures.


- 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. 


University of Chicago_050222B
[University of Chicago]

- 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.


- Computational Pathology for Precision Medicine

Computational pathology uses artificial intelligence to enable precision medicine and decision support systems by analyzing entire slide images. It has the potential to revolutionize cancer diagnosis and treatment. 

However, a major challenge to this goal is that for many specific computational pathology tasks, the amount of data is insufficient for development.

Computational pathology is a branch of pathology that uses artificial intelligence and machine learning to analyze disease in patient specimens. It can help improve diagnostic accuracy, optimize patient care and reduce costs. Computational pathology can:

  • Generate more precise diagnoses
  • Discover new damage patterns
  • Identification of pathological changes beyond the limitations of traditional visible light microscopy assessment
  • Enhancing oncology patient selection

Computational pathology is a key element in achieving the goal of personalized precision medicine. It can change the way pathology services are managed, making them more efficient and able to meet the needs of precision medicine.

Experts agree that by 2030, artificial intelligence will be used routinely and effectively in AP labs and pathologists’ clinical workflows. While artificial intelligence may be a new frontier for laboratories currently, as these tools become more sophisticated, they should be able to enhance and support laboratory professionals and pathologists.


[More to come ...]

Document Actions