How AI Is Shaping Life Sciences: Challenges, Risks and Future Prospects
Artificial intelligence (“AI”) has become a buzzword—almost every article, news bulletin, podcast and other digital media seems to mention it.
Looking back at the past decade, the field of AI has been growing rapidly. Perhaps its crescendo was the mainstream explosion of ChatGPT, a generative AI chatbot that was the fastest-growing consumer application by January 2023. It is credited with starting the AI boom, a trend we still see today as investors pour millions of dollars into generative AI.
The life sciences and other technology companies, albeit having taken a more cautious approach, have not been left behind. According to a report from Grand View Research, the global life sciences AI market was valued at $1.7 billion in 2022 and is expected to grow at an annual rate of 10.9% from 2023 to 2030.
While the potential for AI to save lives in areas like treatment and drug discovery is undeniable, one thing is without a doubt—the risks of artificial intelligence are very real. In this blog, you’ll learn:
- The impact of AI on life sciences: A brief overview of startups utilizing AI technology for groundbreaking medical milestones like life-saving drugs.
- The unique challenges that these companies face: Key examples include data quality and bias problems, cybersecurity threats and technology and performance failures.
Keep scrolling below for more details.
Understanding the Impact of AI on Life Sciences
The future is AI; there’s no doubt about that. It’s already reshaping the life sciences and other technology companies on multifaceted fronts, with drug discovery, known for its cost, length and uncertainty, leading the way.
Today, life sciences and other technology companies use AI-powered tools to identify and map disease pathways as well as investigate complex protein interactions. This strategy is paving the way for new and effective drugs for various diseases. Here are a few examples:
- Insilico Medicine: Used Chemistry42, a proprietary AI-powered platform that runs on 42 generative algorithms to determine biochemical pathways that a drug compound could target.
- Deep Genomics: Developed a proprietary AI-powered platform that explores large datasets of mRNA biology to determine potential drug targets.
- Pathos: This company uses AI to scan vast oncology datasets to identify potential drug targets and validate them.
These are just some of the notable examples of AI’s transformative power. In areas such as personalized medicine, AI is taking center stage, helping medical professionals develop more tailored treatment plans and accurate diagnostics.
AI in Life Sciences Challenges
Even with all that being said, the life science market has generally taken a more cautious approach to AI. In fact, most AI-assisted achievements in this niche are still in their early stages and for good reason.
Here are AI technology’s most significant risks for the life sciences market.
Data Quality and Bias
An AI model is only as strong as the data it is trained on. After all, data is the lifeblood of any AI system; without it, AI algorithms cannot learn to make accurate predictions.
That being said, AI algorithms trained on incomplete datasets can make biased decisions. In contrast, those trained on inaccurate information can make equally incorrect conclusions, a risk manifested in real-world AI failures.
A notable example is Apple’s Credit AI algorithm gender-bias case, which began after popular software developer David Heinemier Hansson complained on Twitter that the credit line offered by his Apple Card was 20 times higher than his wife’s, even though they filed joint tax returns and he had the worse credit score.
After his tweet went viral, more Apple customers accused the Apple Credit AI algorithm of gender bias, prompting the New York Department of Financial Services to investigate Goldman Sachs, the company responsible for running Apple Card.
This is a risk the life sciences and other technology companies should take very seriously, as machine learning models and algorithms used in clinical trials and drug discovery heavily rely on medical data like electronic health records. Suppose such tools are trained on biased and incomplete data. The effects could be dangerous, potentially compromising patient safety.
Cybersecurity Threats
AI is a double-edged sword, a nature exemplified by its adoption in the hacker world. As this technology advances, the access barrier for criminals continues to decrease, allowing them to leverage AI-powered cyber-attack techniques.
Deepfakes are a prime example of how AI is reshaping cybercrime. Recently, a finance worker at a multinational firm in Hong Kong was duped into remitting $25 million after a video call with people they thought to be colleagues, only to realize they were deepfake recreations.
The risk of an AI-powered data breach or social engineering scam such as the above is very real for life sciences and other technology companies. In 2021 alone, more than 40 million patient records were compromised in U.S. data breaches.
However, AI, being a two-faced coin, presents an advantage. According to a report from the World Economic Forum, AI technology can be used to defend and prevent these attacks.
For example, artificial intelligence systems have a high accuracy in malware detection. AI algorithms can be designed to analyze systems for questionable activity, pointing to the possibility of a data breach.
Technology and Performance Failures
As life sciences and other technology companies continue to integrate AI applications in core operations, there’s always a risk of performance failures when AI models’ capabilities are exaggerated or when the output is inaccurate.
Take the case of an IBM AI-powered tool designed to help doctors provide treatment recommendations for cancer patients based on literature from past cancer cases.
Internal documents from IBM revealed that the tool recommended unsafe and inaccurate treatments to doctors in what was to become an AI performance failure whose causes were as complex as they were nuanced.
As such, life science companies need to take the risk of AI performance failures very seriously. While natural language processing technology can extract data from clinical trials and other areas, it is crucial to ascertain the validity of AI recommendations and data, as these models are prone to making errors.
Conclusion
We have come to the end of our discussion about the dangers of artificial intelligence in the life sciences market. It’s clear that integrating this promising technology necessitates caution from all involved stakeholders and as a tightly regulated industry, we expect to see policy changes soon.
While AI has proven to be a game-changer so far, we have yet to see its full capability because integration is still in its infancy. Looking at cases such as Insilico Medicine, a biomedical company that harnessed the power of artificial intelligence to create a new drug-like molecule 15 times faster than average, it is clear that the future is indeed AI.
However, as life science companies continue to utilize AI technology in drug discovery, clinical trials and personalized medicine, the associated risks cannot be ignored. After all, with algorithms and machine learning models, the tool is only as good as the data it’s trained on.
As such, the risk of data bias leading to inaccurate conclusions—putting patient lives at risk—is very real. With examples such as Watson for Oncology to look back on, governments, private companies and healthcare institutions must collaborate to ensure medical data systems are transparent and unbiased.
Additionally, life science companies must reorganize their cybersecurity measures and keep workers up to date with current trends. As AI advances, distinguishing between synthetic and human-generated digital media is becoming increasingly difficult. Deepfakes are becoming more convincing, to the point of tricking workers into paying millions of dollars in company money through sophisticated social engineering scams.
Hence, as life science companies continue to utilize the power of AI, it’s crucial to understand this technology’s two-faced nature, harnessing its benefits while mitigating its risks. Additionally, it’s important to acknowledge the role of AI as an enhancer of human capabilities rather than a replacement for them. Cultivating a regulated and symbiotic relationship between artificial intelligence models and life science experts is the only way for this technology to thrive in the near future.
FAQ
What Are the Key Applications of AI in Life Sciences?
- Drug discovery
- Personalized medicine
- Clinical trials and data analysis
- Diagnostics and imaging
- Disease prediction and prevention
What Are the Main Challenges of Implementing AI in Life Sciences?
- Data quality and bias
- Cybersecurity risks
- Performance and technology failures
- Regulatory hurdles
- Ethical concerns regarding AI decisions
How Does AI Contribute to Drug Discovery?
- Analyzes complex biological data
- Identifies potential drug targets faster
- Reduces cost and time in research and development
- Simulates biochemical interactions
Are There Any Real-World Examples of AI Failures in Healthcare?
- IBM Watson for Oncology recommending unsafe treatments
- Apple’s Credit AI algorithm showing gender bias
- AI systems making flawed predictions due to poor data quality
How Can life sciences and other technology companies Mitigate AI Risks?
- Use high-quality, unbiased data
- Regularly test and validate AI systems
- Strengthen cybersecurity defenses
- Collaborate with regulators for ethical AI use
What Does the Future of AI in Life Sciences Look Like?
- Further advancements in drug discovery and precision medicine
- Increased integration in clinical workflows
- Stricter regulations and ethical guidelines
- Enhanced collaboration between AI tools and healthcare professionals
