Artificial intelligence (AI) is transforming the entire life science industry from R&D to commercial business functions. According to the Artificial Intelligence in Life Sciences Market — Growth, Trends, and Forecast (2019-2024) report by Mordor Intelligence, the AI market in life sciences is valued at $902.1 million in 2019 and is expected to grow at a CAGR of over 21% for the next 5 years. According to Gartner, various important elements of AI technologies are at their peak in the hype cycle.
In comparison with other industries, the use of AI is modest in life sciences. However, even within life sciences, adoption of AI in regulated environments, as in the R&D value chain, is further behind other areas of life sciences, for a number of reasons.
AI in Clinical, Regulatory, and Safety
The scope of our discussions is limited to clinical, safety, and regulatory functions of pharma companies-which may span the R&D and CMO (Chief Medical Officer) organizations in a typical large pharma. We identify emerging use cases in these areas and the types of AI technologies that can impact them. In recent years, there has been a surge in data—such as real-world data (RWD) usage across the value chain, explosion of number of cases in safety, etc. This significant and substantial increase in data only enables the assessment of AI across these areas.
However, AI and machine learning (ML) capabilities are largely misunderstood and, due to their position at the peak of the hype cycle, people can have very high expectations from AI/ML inevitably leading to disappointment. This weakens the trust in AI at the executive level.
Use cases in R&D
Pharma companies today analyze and look for inferences from data that they are mandated to collect and analyze. Even though there is rapid data explosion in R&D, companies have not been able to leverage all of this new data to its potential for effective decision making. The need of the hour is to take advantage of the opportunities to generate value by deriving insights from this "dark" data (e.g., data from RWE/RWD, secondary research data, data from patient interactions like patient services, data from public sources about regulatory submissions/pathways, etc.) According to International Data Corporation (IDC) report sponsored by Seagate Technology, healthcare data alone will experience a compound annual growth rate (CAGR) of 36% through 2025. Thus, exploring such data and deriving insights from them is the first opportunity to leverage AI.
Some examples are:
R&D, being a cost center, is under constant pressure to do more with less, making sensible automations attractive options. In combination with other technologies, AI can play a significant role in reducing the cost of operations. However, automation also yields other benefits beyond just cost savings. Reducing overall processing times is an obvious benefit. Automation not only saves cost but also improves compliance and facilitates scaling of operations. For example, in pharmacovigilance case processing, if a case arrives on day 13 when it is due on day 15, it becomes impossible to handle unless turnaround time can be dramatically improved. Scaling of operations is achieved via automation particularly in regulated areas where scaling requires enough lead time to identify qualified resources and training them. Automation inherently can scale up or down as appropriate.
Automation can be performed in various ways, we focus essentially on using AI for automation. AI is required for automation in areas where decision-making and subjectivity are involved. In the R&D world, there are several areas that are process oriented but have human decision-making embedded in them making automation impossible without AI.
Some examples are:
Classification by AI technologies
As we explore the use cases above, we try to categorize them into three broad areas of AI, namely NLP, AI-based classification algorithms, and unsupervised learning.
NLP technologies: Natural Language Processing is the technology to process free form text and provide structured information. The opposite process of taking structured data and generating human readable free form text is known as Natural Language Generation. At the core, modern NLP systems attempt to convert words in documents into numeric vector representations that are then computable. These vectors encompass the words and the context of the words. The systems are trained with large number of documents. Today, state-of-the-art in NLP is being pushed forward by algorithms that increasingly use sophisticated mechanisms of processing large document collections and creating numeric vectors. NLP technologies are being trained across multiple languages and domain-specific ontologies and taxonomies, providing powerful ways of deploying automation to enhance information search and retrieval in the document authoring processes. In the medical and regulated realm, NLP is able to contextualize clinical terms within the right context and do this with very high levels of accuracy.
Classification algorithms: Classification is the method used to take structured data and generate business domain appropriate metadata that describes the data. There are many algorithms available for classification; however, most models are created through supervised machine learning. In supervised machine learning, subject matter experts annotate historical datasets with descriptive metadata and this information is used to train the machine. The models are very good at identifying the patterns and creating a model to predict the metadata for new information parsed into the model. Unlike NLP, which has broad industry-wide applications, classification models tend to get closer to specific business problems. Thus, subject matter expertise both at business and technical levels is key.
Properly designed classification models are significant tools in analytics and insight generation, acceleration of document authoring through reuse, and information search and retrieval. Classification algorithms require high-quality data and expertise in annotation. Yet, if there is enough data and the data is clean, one can expect high levels of accuracy, often in the range of 80%-95%, depending on the use case.
Clustering algorithms: Clustering algorithms are good examples of unsupervised learning. They break up data into cohorts with similar characteristics by using techniques like minimizing distance between characteristics of patients within the cohort while maximizing the distance across cohorts.
The table below shows some of the top use cases classified by AI technique:
We reiterate the message that AI has a wide range of application in the clinical, regulatory, and safety realm. However, many of the use cases described have not yet been mainstreamed.
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