Cognitive technologies are disrupting the Life Sciences sector. Artificial intelligence (AI) is one such emerging technology that is transforming the way industry thinks about diseases, prognosis, diagnosis, and treatment.
The outburst of data in Life Sciences industry over the past few decades has opened doors for implementation of AI across various functions.
Broadly, following are the two elements of application of cognitive technologies:
- Cognitive Automation
- Advanced cognitive solutions
The repetitive and mundane tasks like account management, customer support for drug FAQs, claims processing in healthcare, reporting solutions etc. can be automated. In addition to benefits like significant reduction in effort and cost, 100% accuracy and error reduction, it enables quicker decision making and avoiding significant delays in time-to-market of a drug.
On the other hand, advanced cognitive solutions impact complex processes like Clinical trial programs, Regulatory compliance, Pharmacovigilance, and strategic application of data and analytics.
From a drug marketing/launch perspective, advanced AI technologies like Machine Learning and Natural Language Processing (NLP) are being extensively used to derive in-depth insights from large quantum of data. The unstructured data is sourced from various sources including scientific literature, social media, patient records and any other biomedical repositories; processed and stored in a structured format; and analysed to extract actionable insights.
Some of the key areas where AI is impacting the Life Sciences industry are:
- Sentiment analysis of key opinion leaders or influencers of the industry
- Interpreting images and scanned documents into actionable data, which can be useful in Cancer prognosis
- Adverse event reporting solution for drugs in market
- Automation of repetitive functions in drug launch
Using NLP and Machine Learning algorithms, sentiment analysis extracts the underlying tonality or sentiment of a text Sentiment analysis of Key Opinion Leaders (KOL) – positive, neutral or negative – can be used for improving effectiveness of drug promotion strategies adopted by the companies. In addition to extracting KOL sentiments, a host of critical areas are covered including, drug performance, side effects, compliance-based issues, dosage, and program feedback.
Cancer detection using image identification is a key tool in Cancer prognosis. Use of deep learning techniques and advanced 3D Convolutional Neural Network (CNN), cancer nodule can be detected with up to 90% accuracy. Use of AI also reduces the chances of false positive, which solves one of the most prevalent problems in Cancer detection.
Under Adverse event reporting system, cognitive techniques are involved in differentiating irrelevant data from the adverse event data, identification of sentiments in unstructured data etc. Cognitive automation is used in adverse event report submission and constant flow of information. This is a regulatory compliance requirement, which is currently requiring significant of time, effort and resources.
As these cognitive technologies continue to mature, their adoption will further advance the Life Sciences industry by bringing intelligent insights, increased compliance, and effective drug development and promotion.