AI in Healthcare
Key Artificial Intelligence Projects to Fight Against COVID-19
Dr. Amit Ray
Compassionate AI Lab
In our Compassionate AI Lab, we broadly classified our fight against COVID 19 Artificial intelligence (AI) based research projects into six groups. They are AI for COVID vaccine development, AI for COVID drug discovery, AI for COVID diagnosis, AI for COVID testing, AI for COVID growth rate forecasting, and AI for social robots.
Researchers across the world are in search of urgent drugs and vaccines that can save millions of lives of infected people and perhaps prevent infections for the future generations. The exacerbated time, cost and high failure rate of traditional path of drug discovery and vaccine development has prompted the need for efficient use of machine learning techniques. In these projects, we are trying to solve one the most complex problems of humanity ever encountered
1. AI for COVID 19: An Overview
The massive outbreak of the COVID-19 has prompted various scientists, researchers, laboratories, and organizations around the world to conduct large-scale research to help develop vaccines and other treatment strategies. Biology and medicine of coronavirus are data rich, complex, and often ill understood. Problems of this nature may be particularly well suited to deep learning techniques.… Read more..
What is holding back the large scale implementation of machine learning systems in healthcare and precision medicine? In this article Dr. Amit Ray, explains the key obstacles and challenges of implementing large-scale machine learning systems in healthcare. Dr. Ray argued that lack of deeper integration, incomplete understanding of the underlying molecular processes of disease it is intended to treat, may limit the progress of implementing large-scale machine learning based reliable systems in healthcare. Here, nine obstacles of present day machine learning systems in healthcare are discussed.
Machine Learning in Healthcare
Recently, machine learning algorithms, especially deep learning has shown impressive performance in many areas of medical science, especially in classifying imaging data in different clinical domains. In academic environment, Deep learning and Reinforcement learning methods of Artificial Intelligence (AI) has shown tremendous success in numerous clinical areas such as: Omics data integration (such as genomics, proteomics or metabolomics), prediction of drug-disease correlation based on gene expression, and finding combinations of drugs that should not be taken together. Deep learning is very successful in predicting cancer outcome based on tumour tissue images. Machine learning are used for medical decision support systems for ICU and critical care. Artificial Intelligence in Healthcare Current Trends discusses the current status of AI in healthcare. Read More »What’s Holding Back Machine Learning in Healthcare
7 Limitations of Molecular Docking & Computer Aided Drug Design and Discovery
Over the past decades, molecular docking has become an important element for drug design and discovery. Many novel computational drug design methods were developed to aid researchers in discovering promising drug candidates. In the recent years, with the rapid development of faster architectures of Graphics Processing Unit (GPU)-based clusters and better machine algorithms for high-level computations, much progress has been made in areas such as scoring functions, search methods and ligand-receptor interaction for living cells and other approaches for drug design and discovery.
A large number of successful applications have been reported using a variety of docking techniques. However, despite their success in academic environment for concept validation, their real life application is very limited. There are many obstacles and number of issues remain unsolved. In this article Dr. Amit Ray, explains the key obstacles and challenges of molecular docking methods for developing efficient computer aided drug design and discovery (CADD) methods. Dr. Ray argued incomplete understanding of the underlying molecular processes of the disease it is intended to treat may limit the progress of drug discovery. Here, the seven limitations of present CADD methods are discussed.
In vivo, In vitro and In silico: Experimentation for Drug Discovery
Experimentation for Drug Discovery pathways are classified into three groups: in vivo, in vitro and in silico. … Read more..