We are pleased to announce this Research Topic developed in collaboration with the
6th Annual Conference of Arkansas Bioinformatics Consortium (AR-BIC2020), which focused on Artificial Intelligence (AI) for Precision Health. Since its inception in 2015, the Arkansas Bioinformatics Consortium (AR-BIC) has become an essential educator, influencer, and collaborator for bioinformatics. Its annual conference attracts more than 200 attendees and features many leading voices studying bioinformatics and artificial intelligence, data science, and health sciences. Specifically, the AR-BIC 2020 provided a forum that discussed the basic concept and methodologies of AI-driven applications in the fields of biomedical research, food safety, drug discovery/development, and clinical applications.
Driven by advances in computational power, innovative algorithms, and ever-increasing amount of data, the last decade has witnessed widespread applications of Artificial Intelligence (AI) in medicine and healthcare. AI is a broad concept of training machines to think and behave like humans. It consists of a wide range of statistical and machinal leaning approaches with a specific emphasis on learning from the existing data/information to predict future outcomes. The concept of AI was introduced during the 1950s, but its critical role in a broad range of applications has yet to be realized. The 21st century health science has increasingly used novel tools that generate information beyond conventional structured tabular “data”, such as imaging data. Meanwhile, newfangled AI methodologies, such as deep learning are capable of extracting complex patterns from multifaceted data streams.
This Research Topic is intended to present some of the state-of-the-art developments of artificial intelligence in precision health. In close collaboration with human intelligence, AI technologies can bring about more effective and personalized healthcare. Potential topics include, but are not limited to:
• Precision medicine / therapeutics
• Precision agriculture
• Drug discovery and development
• Clinical diagnosis and prognosis
• COVID-19
• Cancer research
• Natural language processing
• Regulatory science
• AI and data science methodology.
• Radiomics and quantitative imaging
• Innovative AI applications for patient privacy and security