AI in Pharma - Innovative Possibilities

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Artificial Intelligence (AI) is a branch of computer science that focuses on the creation of capabilities that can reason, learn, and act autonomously. AI is being used in a variety of industries, including the pharmaceutical industry and has numerous interesting use cases popping up and having quite the impact. The possibilities are only going to grow in the years to come as the technology and the humans that utilize it learn what should/shouldn't be done. The ways AI can be used are evolving as quickly as the technology and we've begun to utilize it to improve drug discovery, manufacturing, and personalize treatments for patients. It is also being used to speed up and simplify business processes across internal departments.

A few of the areas AI is impacting within the pharmaceutical industry:

Drug discovery: AI is being used to identify new drug targets, screen compounds for potential drug candidates, and design new drugs. It can also help to accelerate the drug discovery process and improve the success rate of drug development.
Drug development: AI is being used to develop new drugs more quickly. It can be used to design clinical trials, analyze data, and predict the safety and efficacy of new drugs. Imagine being able to collect head-to-head/comparison data, without actually dosing a patient?
Personalized medicine: AI is being used to personalize treatments for patients. It can be used to identify the best treatment options for an individual, considering their genetic makeup, medical history, and other relevant factors.
Manufacturing: AI is being used to improve the efficiency of manufacturing operations by optimizing production schedules, automating tasks, and improving quality control.
Operations: AI is being used to improve the efficiency of marketing operations, patient identification, sales follow up, and customer feedback collection. It can be used to target potential customers, assist with territory alignments, claims-linking and review readiness in MLR, provide on-demand portals for HCPs and mine the data therein.

However, the use of AI is not always prudent, as evidenced by Samsung's goof-up with OpenAI's ChatGPT platform (they shared IP into the platform without realizing the implications). It is important to think through everything from accidental exposure of intellectual property in the tool through corporate policies around content authorship (anything from internal SOPs to scientific journal articles) and development process. Are you allowed to use AI to author parts or all but disclose it or can you utilize it for research, but all content development must be original? What is considered ethical and responsible use of AI? The FDA issued guidance in January 2021 focused on AI/ML-based software as a medical device which provides an action plan they later posted on their page as their overall AI/ML action plan states “The AI/ML-Based Software as a Medical Device Action Plan outlines five actions that the FDA intends to take, including:

Further developing the proposed regulatory framework, including through issuance of draft guidance on a predetermined change control plan (for software’s learning over time)
Supporting the development of good machine learning practices to evaluate and improve machine learning algorithms
Fostering a patient-centered approach, including device transparency to users
Developing methods to evaluate and improve machine learning algorithms
Advancing real-world performance monitoring pilots” (FDA Site)

AI is a powerful tool that is revolutionizing the world. However, it is important to note that AI is not a silver bullet. It can only be as good as the data that it is trained on. Additionally, AI systems can be biased, and it is important to be aware of this potential for bias and to take steps to mitigate it (generally an important thing for us to self-assess as individuals, shameless plug for self-awareness and unconscious bias).

The question remains, how does one get started? Here are some steps on how to implement AI in pharmaceutical business operations:

Identify the business problem that you want to solve with AI
Gather the data that you need to train the AI system
Choose the right AI algorithm
Train the AI system
Test the system
Deploy the system
Monitor the system with real humans

It sounds so straightforward, easy even. Implementing AI in pharmaceutical business operations can be a complex process, but it can be a valuable tool for improving efficiency. By following the steps above, you can implement AI in a way that is successful and beneficial to your business.