AI Solutions for Life Sciences
Drug Discovery and Manufacture
Machine learning plays many roles in early-stage drug discovery, such as the development of new drug compounds, and in discovery technologies, such as next-generation sequencing. One of the first in this field is precision medicine, which makes identification of complex diseases and possible treatment modalities more efficient. The research uses unsupervised learning, which seeks data patterns without predicting outcomes. Life sciences companies are exploring how AI can be leveraged to identify new indications for existing products or research new candidates.
Clinical trial research is a long and arduous progress. Machine learning can help make it less in various ways. One is by using advanced predictive analytics on a wide range of data to identify candidates for clinical trials for target populations much more quickly. Analysts at McKinsey describe other machine learning applications that can make clinical trials more efficient by facilitating tasks such as calculating ideal sample sizes, facilitating patient recruitment, and using medical records to minimize data errors.
Driving Compliance In Clinical Trial Transparency
Compliance is often a burden on companies and requires an approach to mitigate costs while meeting regulation. The European Medicines Agency’s policies 0070 and 0043 are examples of regulations that have recently been introduced requiring companies to anonymize or redact patient information in clinical submissions. New applications are emerging utilizing advanced algorithms based on customized NLP technologies incorporating scientific-specific taxonomies and text-mining models. Using these advanced models, it is possible to identify keywords, phrases, and data patterns that may require redaction or anonymization.
Xen.AI can help the Life Sciences companies to apply artificial intelligence, machine learning, deep learning and data science technologies in the following areas to improve the efficiency and reduce the operating cost.