Understanding AI in Life Sciences

Building effective life science AI use cases requires an understanding of current trends, ethics considerations and how AI is being leveraged in the market.

Bespoke healthcare management solutions are now the holy grail for any life science services provider as customers demand more personalization than ever in the healthcare sector. This means life science providers need to keep biotech software development and AI for biotechnology in mind if they want to stay competitive in their marketplace.

However, what are the challenges of implementing AI in life sciences? How is AI being used in drug discovery and development? What are the ethical considerations of using AI in life sciences and what future trends are emerging in AI applications within life sciences?

The best way to answer these questions is to build various AI use cases in life science that address each of these questions – or indeed several of them – directly. But how can organizations do this quickly and effectively? Read on to find out more.

AI trends in life sciences

The first step to building valid AI use cases in life science for any organization is understanding current trends in the market. With that in mind, some common trends impacting the life science sector today are:

• Generative AI is booming in life sciences: Everyone has heard of generative AI, and it’s making waves in the life sciences and healthcare industries. This trend is expected to keep growing in 2024. According to Deloitte, over 90% of biopharma and MedTech leaders see how much AI is shaping the industry. In fact, 66% of life sciences companies already use it to improve operations, like compliance and supply chain management. For compliance, AI helps reduce errors by automating monitoring and reporting. In supply chains, it uses predictive analytics to better forecast demand and manage inventory, making logistics more efficient.
• More interdisciplinary partnerships: Another trend for 2024 is the rise in collaborations across different fields. Big pharma and digital health companies, once distant, are now teaming up to improve healthcare delivery. For example, Novo Nordisk and Kakao Healthcare are working together to help people with chronic illnesses manage their conditions digitally. In 2023, several major pharmaceutical companies like Gilead, Merck and Novartis launched the Digital Pharma Circle (DPC) to encourage conversations on how digital technology can transform the industry. This shift shows how collaboration is making healthcare more connected, something the world has needed for a long time.
• AI and machine learning are revolutionizing research: The integration of AI and machine learning in healthcare research is more than just a trend; it’s a game-changer. Tasks that used to take weeks or months can now be done instantly. AI can analyze millions of records at once, speeding up drug discovery, clinical trial recruitment and disease detection. One promising use is testing drug interactions virtually, which helps spot potential health risks and find effective treatment combinations without putting anyone at risk. This ability to fast-track drug development will benefit both researchers and patients in the years to come.
• Demand for data integration: AI has opened new research possibilities, but it’s also a wake-up call for organizations lagging in digital transformation. Research centers need access to accurate, complete data across all business areas to stay competitive. Companies that successfully integrate their data and use AI to accelerate product development and manufacturing will have a big advantage in the market.

Examples of AI in life sciences

The next step in building AI use cases in AI is understanding how AI is being leveraged in the life science sector today. There are many ways this is happening, but it is mainly involved in providing:

• Faster, safer drug development: Developing a drug usually takes 10-15 years, but AI could change that. Scientists are working on virtual cell modeling, which mimics human cells, allowing them to test reactions to infections, diseases and drugs faster and more efficiently. This is already happening, thanks to initiatives like the one led by Priscilla Chan and Mark Zuckerberg.
• Improved visual diagnostics: AI-powered computer vision is now being used in radiology to improve diagnostics. A promising development in 2023 was PANDA, a tool for detecting pancreatic cancer from CT scans as reported by Nature. It was found to be 99.9% accurate in identifying cancer-free images and 92.9% in those with cancer.
• Automated pharmacovigilance: AI is also helping in pharmacovigilance, the process of monitoring drug side effects. AI-powered systems can detect potential drug toxicity much faster and more accurately than manual methods, making drug safety checks more efficient.
• Enhanced bionic engineering capabilities: AI is enabling better communication between prosthetic limbs and patients’ nervous systems. For instance, a patient with a bionic arm controlled by AI was able to use their artificial limb in a way that mimics natural movement. The goal is to make these devices more responsive and lifelike.
• Better clinical trial matching: AI can speed up the process of finding suitable patients for clinical trials, which has traditionally taken months or even years. AI analyzes medical records and monitors patient health during trials, which improves resource allocation and enhances patient care.

AI ethics in life sciences

However, it’s important to keep in mind that as AI becomes more integrated into healthcare, ethical questions arise, including:

• Algorithmic bias: AI algorithms can sometimes reflect biases in the data they’re trained on, leading to disparities in healthcare treatment for different racial or socioeconomic groups. It’s crucial to use diverse, real-world data sets to avoid this and ensure fair treatment for all.
• Informed consent: Patients need to clearly understand how AI will use their data, and they should have the option to opt out. As AI evolves, it raises concerns about whether informed consent will stay valid over time. This is an issue that still needs to be addressed.

In short, AI maybe transforming the life sciences and healthcare sectors, but it’s important to stay mindful of the ethical implications as this technology continues to evolve.

Developing life science AI use cases with Software Mind

All this is what you need to keep in mind if you want to build AI use cases in life science that work for you. However, at Software Mind we know that developing life science AI use cases is easier said than done, and we also know that undertaking this kind of work can be extremely daunting.

That is where our experienced software experts come in. They can help choose the right life science AI use cases for you quickly and easily by connecting with you to understand more about why you need to leverage such use cases in the first place, which in turn will save you considerable time and money overall.

So, what are you waiting for? Our experienced software development team is happy to talk about what a properly implemented life science use case can do for you wherever you are.