Artificial Intelligence (AI) is a dynamic force that is reshaping our lives, our environment, and our interactions already. It can be described as a program whose aim is to generate and potentially even improve on human-like cognitive processes. AI has many facets: it can be algorithmic as in game-playing systems, or as in autonomous vehicles, it can take a control-theoretic approach. It can also manifest as linguistic ability, creativity, spatial reasoning, learning, and many more.Artificial Intelligence in Life Science.
We now realize that Artificial Intelligence in Life Science, be it making discoveries from massive biological data using machine learning, integrating clinical records and genomic data of different kinds, discovering new medicines or drug targets, identifying new classes of cell types, making the diagnosis, or customizing clinical procedures as in precision medicine.
There are several fields where AI is widely used by the life sciences industry today. The following sections identify six of these areas.
Advancing Diagnostics
Histopathology image analysis and automated diagnosis are ripe for AI due to the technical advances in digitalizing full histology slides that require all microscopic magnifications. AI and pattern recognition, together with sophisticated algorithms and automated immunohistochemical measurement systems, have increased the capacity of pathologists to track the study and focus on more complicated cases.
Advancing Research of New Products
Companies in the life sciences are researching how to use AI to find potential indications for current products or new candidates for study. Examples include the following but are not limited to:
- Using sophisticated learning algorithms to mine structured and unstructured data from the real-world to discover insights can lead to the discovery of new disease mechanisms, possible new line extension, and pre-clinical experiment design.
- Knowledge gaps in how candidates function on proteins can be filled up to assist design new medicines.
- Information can be drawn from commercial, scientific, and regulatory literature in real-time, enabling researchers to recognize competitive white space, remove blind spots in research, and discover correlations between diseases.
Speeding up Drug Development
Throughout the industry, the timelines for product development vary from 7 to 10 years from discovery to launch, with targets set on raising them to 5 to 7 years. Advances in AI and machine learning to reduce the time it takes to develop, produce, and deliver new patient therapies support the goal of reducing the overall timelines for drug development. In combination with new knowledge sources (e.g., social media and wearables) around the drug development spectrum, scientists are combining scientific data, laboratory data, and clinical data, providing a holistic image of the drug development candidate. Improving ways to collect and mine data in real-time enables scientists to use AI and machine learning to make better choices faster, which will speed up product development and scale-up processes.
Driving Enforcement by Transparency in Clinical Trials
Compliance is often a burden on companies and requires a cost-control approach while complying with the regulations. The rules 0070 and 0043 of the European Medicines Agency are examples of regulations recently adopted, requiring organizations to anonymize or redact patient details in clinical submissions. Although generalist automation tools are available, many do not satisfy the precision needed to meet the requirements of the policy. New applications are emerging using advanced NLP-based algorithms that integrate scientific-specific taxonomies and text-mining models. Using these advanced models, keywords, phrases, and patterns of data (such as dates of adverse events) can be detected that may need redaction or anonymization. These new technologies provide a higher degree of precision required to fulfill the policy requirements while also automating manual operations.
Improving Selection of Clinical Sites and Speeding up Patient Recognition
Nearly 80% of clinical trials fail to meet their deadlines for patient enrollment. By addressing high-probability targets, the combination of unanalyzed historical structured and unstructured clinical trial data into advanced AI models may improve and speed up clinical site and patient selection decisions. The continued use of advanced AI models during successful clinical programs allows for real-time adjustment and correction of courses. Engaging high-probability performance goals at the start of clinical trials, coupled with a willingness to make real-time course corrections increase the possibility of meeting deadlines for patient enrollment.
Utilizing Machine Learning/Predictive Analytics to Optimize Submission Dates
Life sciences industries are responsible for up-to-date product safety details. Pharmaceutical labels (e.g., packaging insert, patient information leaflets) are an effective way of sharing knowledge about safety. There are several criteria (e.g., artwork printing, manufacturing run dates) for applying the local product label to the relevant health authority when an adjustment to the reference label is required for safety purposes. SOP deadlines governing submission due dates for most businesses are more strict than what health authorities require, thereby raising the workload and expense. An optimal date for submission may be calculated by integrating and linking the correct data points with machine learning and predictive analysis.
