The Potential and Challenges of AI in Biotech

Diving deeper into the role of AI in drug discovery, validation and clinical trials. Discussing how we could balance the risks whilst ensuring quality of data & algorithm.

Sep 18, 2023

Artificial intelligence (AI) is not just a buzzword anymore, it has huge implications in almost every field. Here, we look at its role specifically in biotechnology, and how it has revolutionised drug discovery, validation and clinical trials.

Although AI has great potential, it is also important to assess the risks and responsibility that comes with it. This includes building trust between clinicians using AI and patients receiving treatment developed by AI, developing robust data infrastructure to ensure both the quality of data and algorithm, and increasing transparency to break the barrier of communication.

Key Takeaways 💡

AI can make drug discovery and development quicker and better
Treat AI as a collaborative tool rather than a replacement tool
Build trust in the communication of AI by being transparent
Identify the bottleneck - quality of data & algorithm
Balance the risks of AI and speed of innovation

The current state of AI in biotech 🧬

Traditionally, the drug discovery process often takes a substantial amount of time. With AI, it can help transform the process to be more efficient, affordable and of better quality. This helps biotech companies model complex, non-linear data more effectively and speed up the drug development process. Whether this is in gene sequencing or AI-driven image analysis, it can help us better understand diseases and drug responses, and streamline the identification of potential drug development.

The potential of AI in biotech 💡

There is no doubt that AI can make the drug discovery and development process quicker and better. With the initial step being sped up, it can help biotech companies scale the production of drugs, making them more readily available to those in need. It is also possible to create more personalised treatments which can make a greater impact on patients. With its help in modelling complex data, AI can generate actionable insights such as disease prediction which lead to better prognosis and prevention. The agility of AI also drives new innovation, which is great when promoting new treatment options compared to the past.

The challenges📍

Although AI has huge potential, there are many challenges we still need to constantly address. Firstly, we need to ensure the quality of the algorithm. However, in order to do that, we need to first ensure the quality of data that feeds into the algorithm. To do that, it is important for biotech companies and scientists to build a robust data infrastructure and pipeline. This also means maintaining data privacy, anonymisation of data and the possibility of including a diverse data set to minimise bias. Identifying this bottleneck will help us to assess the potential risks as well. Often, we want to innovate and scale as quickly as possible. However, the danger remains in the lack of trust and abiding by the regulatory principles. It is therefore important to acknowledge what is being traded off in the process (eg. scalability) and reach a balance to ensure quality output as well.

Building the trust and presence of AI 🤖

One of the challenges is how to build trust in the presence of AI. It is important to address the fear behind both clinicians adopting AI and patients receiving AI-driven treatments. By being transparent about AI in each step of the drug discovery or clinical trial processes, we can build trust and address the gap in AI communication. It is also important to assess the impact social media has in influencing the way we perceive AI. Understanding the motivation why people get excited or are scared of using the technology will help us find ways to break down the education barrier to AI.

Ethics and principles ⚖️

Is it ethical to use AI knowing the risks it could impose?

But also..

Is it unethical to not use AI knowing the possibilities it could bring?

This is a never-ending debate and there is no ‘one-size-fits-all’ solution.

However, it is important to balance the need for innovation with ethical principles. The way to address the risks involved is to ensure quality in each step of the validation process. By building robust pipelines and acknowledging the regulatory processes, we can slowly build the foundation of trust in a collaborative and transparent relationship with AI.

Finding the ‘sweet spot’ 🍭

The ‘sweet spot’ would be to develop a framework so we can ensure we are using AI in a safe and responsible way. Instead of being fearful of AI as a potential replacement, we should see it as a collaborative and collective tool that enhances our capabilities.

There are lots of considerations when building a robust AI algorithm. Identifying the ‘bottleneck’ will help us understand ways to collect quality data, which then feeds into building quality AI algorithms. Diversity in data is also crucial for AI to perform effectively. This will help AI make more informed clinical decisions and drive positive outcomes.

Building trust is a fundamental part of integrating AI into biotech. To build this trust, it is important to be as transparent in the communication as possible. It goes both ways - clinicians trusting AI’s recommendations and patients trusting AI-driven treatment options. This will help break down the educational barrier so people can have a better understanding of the capabilities and limitations of AI. By making this information clear and accessible, we can make small but impactful steps to building a sense of security in the use of AI in biotech.

Actionable insights 💪

Firstly, the questions we need to ask are:

Why do we need AI?

What exactly is the problem we are trying to solve with AI?

Diving deeper into these questions will help us shift our perspectives to prioritise a more comprehensive approach to patient care with AI. The technology has great potential in accelerating and enhancing drug discovery, which can also lead to more efficient clinical trials and personalised medical treatments.

Although the regulatory processes might lag behind the speed of innovation, it is important to understand this is a crucial step in building trust in communication. Being transparent about AI technology can help us break down the barrier of communication and education to people. Acknowledging that AI is a collaborative and collective tool instead of a replacement threat allows us to recognise the capabilities of the technology.

Food for thought 💭

How does working with AI teach us to be human?

Conclusion ✍️

In conclusion, AI has huge potential in biotech. However, it is important not to ignore the challenges and risks it could impose. By being transparent about robust data infrastructure, we can bridge the communication barrier to build trust and address the ethics around it.