The discovery of new molecules by analysing massive data sets with complex classification algorithms has improved the accuracy and speed of drug discovery. This is possible with a big data environment with large data sets available from clinical trials, published and unpublished data. In addition to drug discovery, Artificial Intelligence (AI) helps identify new biologics to fight diseases like cancer. The future is promising since AI can enable more personalised and predictive methods of treatment. For instance, a biological neural network algorithm predicts recurrence in patients detected with breast cancer depending on the underlying root cause, and, at times, doctors recommend avoiding treatments like chemotherapy.
Automating clinical trials is another prominent role of AI in reducing repetitive and manual tasks. It often takes years for a drug to come to use and clinical trials take the most time given the complexity and inefficiencies of conventional systems and processes. Automation and NLP algorithms help accelerate clinical trials by reducing manual effort and rework. Noise suppression, correlation and anomaly detection techniques help in profiling and curating the data sets from diverse sources. This also helps in cutting down the costs, improving outcomes and reusability in the trial lifecycle. AI, for instance, has helped develop COVID vaccines with a relatively shorter lifecycle.
Connected devices with the Internet of Things (IoT) have been widely used in the manufacturing and supply chain network, starting with demand sensing, supply chain integration, testing, packaging, warehousing, etc. Prescriptive AI algorithms are helping suppliers, labs and manufacturers by predicting demand-supply levers. In addition, it helps distribution and delivery operations.
IoT wearables on the other hand generate real-time data about patients in monitoring their activities and vitals like blood pressure, glucose, heartbeat, etc. Improved and real-time medical prescriptions are possible including diet and activity recommendations for patients and the general health of individuals. Insurance providers have started using these data for profiling and early detection of health risks. Insurers promote healthy living for their insured by use of this data based on their lifestyle. In the near future, claim prediction models will help minimise the health premiums for the insured with a healthy lifestyle.
Prescription recommendations to doctors and nursing staff are evolving with AI and bots providing fault prevention assistance based on the digital patients’ records with the current state, lifestyle, age and other allergy-related information. Models and bots correlating symptoms are evolving especially in the telehealth world. NLP-enabled medical prescriptions have reduced manual transcription efforts by reducing defects and costs, and improving the knowledge base.
Social listening in pharma provides a lot of insights to doctors, pharmacists and patients about patient experiences, peer medications, price positioning, side effects and much other information that can be consumed by different stages and teams. These insights can also help influencer marketing based on social groups.
Pharma companies are responsible to enable regular monitoring and reporting of metrics on periodic intervals to the regulatory authorities. AI-based systems help streamline regulatory submissions and actively monitor drug safety signals for approved drugs. FDA has been using AI since 2000 in the review and approval of drugs. AI-based technologies are used in drug labelling to read and understand text, content and other warnings that are part of the regulation.
Data strategy is key for health and lifesciences in identifying the variety of structured and unstructured data within the enterprise and healthcare ecosystem, and seek for data from external agencies, social media and syndicated data. After identification, setting up a system to collect, store and curate the data is essential for the AI algorithms to describe, predict and prescribe intelligence.