Continuous operations 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 mechanical learning approaches with a specific importance 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. In close collaboration with human intelligence, AI technologies can bring about more effective and personalized healthcare.
Broad application in different fields
- Precision medicine / therapeutics
- Precision agriculture
- Drug discovery and development
- Clinical diagnosis and prognosis
- Cancer research
- Natural language processing
- Regulatory science
- AI and data science methodology
- Radiomics and quantitative imaging
- Innovative AI applications for patient privacy and security
Many researchers with domain expertise are unable to easily apply machine learning (ML) to their bioinformatics data due to a lack of ML and/or coding expertise. Methods that have been proposed thus far to automate ML mostly require programming experience as well as expert knowledge to tune and apply the algorithms correctly.
- First to make it easy to construct sophisticated models of biomedical processes
- Second to provide a fully automated AI agent that can choose and conduct promising experiments for the user, based on the user’s experiments as well as prior knowledge
For half a century, bioinformatics and computational biology have provided tools and data analysis approaches, so the beginning of the omics era showed a novel challenge for researchers that converged to the area of bioinformatics from the fields of informatics, mathematics, and statistics. In most cases, the solutions offered appeared difficult to use for researchers working in biomedical areas. This occurred in particular when sophisticated approaches from the field of data science and artificial intelligence (AI), were applied to biomedical data.
Machine learning, statistical learning, and soft-computing approaches, such as deep neural networks or genetic algorithms, have also become terms used in the bio- bio world, with an incomplete comprehension however, of their potential. In recent years, omics, multi-omics, and inter-omics experiments have presented a further step toward the investigation in biology, opening the window on personalized medicine, for example for diagnostics. The era of big data in medicine is near and shows yet a further step forward.
In particular, bioinformatics the science of analyzing complex biological data using computational methods has seen significant advancements due to the rise of artificial intelligence.