How AI is Reshaping Life Sciences with Its Cognitive Capabilities?
Artificial Intelligence (AI) is a powerful force that is already reshaping our lives, environment, and interactions. It may be defined as a program whose aim is to produce human-like cognitive processes and potentially even improving on them. AI has many facets: it may be algorithmic as in game-playing programs or take a control-theoretic approach as in autonomous vehicles. It may also manifest itself as linguistic ability, creativity, spatial reasoning, learning, and many others. We are now recognizing that AI has begun to make huge inroads to Life Sciences, be it making discoveries from huge biological data using machine learning, combining health records and genomic data of various types, discovering new drugs or drug targets, finding new groups of cell types, making diagnosis, or customizing health procedures as in precision medicine.
There are numerous areas where the life science industry uses AI effectively today. The following sections describe six of those areas.
Histopathology image analysis and automated diagnosis were ripe for AI, given the technological progress in the digitalization of complete histology slides, which permit all microscopic magnifications. AI and pattern recognition, combined with complex algorithms and automated immunohistochemical measurement systems, have advanced pathologists’ ability to oversee the analysis and concentrate on more difficult cases.
Life sciences companies are exploring how AI can be leveraged to identify new indications for existing products or research new candidates. Examples include, but are not limited to:
• Using sophisticated learning algorithms to mine real-world structured and unstructured data to uncover insights can lead to the identification of new mechanisms of disease, potential new line extension, and design for pre-clinical experiments.
• Knowledge gaps in how candidates act on proteins to aid the design of new drugs can be filled.
• Knowledge can be extracted in real-time from commercial, scientific, and regulatory literature, allowing researchers to identify competitive white space, eliminate blind spots in research, and discover disease similarities.
Across the industry, product development timelines range from seven to 10 years from discovery to launch, with sights set on reducing them to five to seven years. Advancements in AI and machine learning to reduce the time it takes to develop, manufacture, and launch new patient therapies support the goal of reducing overall product development timelines. Scientists are integrating research data, lab data, and clinical data, in combination with new information sources (e.
