AI leverages computers to mimic the problem-solving capabilities of the human mind. An important subset of AI is machine learning. Machine learning utilizes labeled and unlabeled or unstructured datasets to inform its algorithm. Machine learning utilizes deep neural networks to learn, similar in some respects to the neural network in the human brain. Utilization of neural networks has been one of the most significant recent breakthroughs in AI and has greatly reduced the manual effort needed when building AI. Very large data sets and increased processing power has enabled this breakthrough.
What are the applications for AI?
AI can be applied in numerous real-world applications, including natural language processing, advanced personal assistants or chatbots, recommendation engines, computer vision, and much more. It is very good at simplifying, organizing, and analyzing complex data.
How can AI be used in life science
AI is unlocking value across industries. An estimated $15 trillion in economic value is estimated to be unlocked by AI by 2030. How can the life sciences industry utilize this powerful technology? One potential use case is utilizing natural language understanding to identify learner questions or intents and provide instantaneous answers from complex documents or data sets. Documents, such as the Prescribing information (PI), can be complex and not easily understood or communicated effectively. Utilizing AI assistants to both provide answers and ask the learners questions and then evaluate theiranswers may greatly improve retention.
As learners ask questions and answer questions, life science trainers and management are able to visualize the metrics in real time via a dashboard. A system like this also grows stronger via machine learning, as it receives more questions/data.
What are the stumbling blocks when implementing AI into life sciences training?
The first step is identifying the business need and then selecting the specific technology and how to use it. AdMed, in particular, implemented and then tested differing approaches, eventually, to land on our current solution with IBM Watson. This process can be time and effort intensive, but it is needed to refine the solution in order to go to market with a product that clearly adds value in life sciences training.
Training the system is another potential stumbling block. Hosting rounds of testing and subsequent system training internally refines the system, but data directly from end users is needed to refine the solution and ensure it is highly effective and accurate. This is addressed by providing access directly to end users in the training phase. End-user testing allows for an understanding how the learners ask questions in different ways and what intents they ask about.
Legal, medical, regulatory (LMR) approval of a new solution that utilizes a new technology can sound daunting. If implementation and training of the system was accomplished correctly, this step is easier than it sounds. Ensuring the LMR team is aligned with the solution at project initiation, clearly mapping out base intents and answers, as well as setting clear guidelines for the scope of information covered by the assistant are important steps.
Where do I start?
Identify your business need(s). Engage with an experienced training partner with a successful approval and implementation track record in the life sciences training field. Gain early consensus internally from both LMR and management. Identify the end user and plan to include them in the training phase. Finally, execute the development and production and be a trailblazer for AI in your organization!
Please reach out to AdMed, Inc. if you are interested in learning more about our AI solutions.