AI in Life Sciences: Exploring Innovation and Opportunities

PUBLISHED

January 15, 2025

Evolution of AI Models in Life Sciences

The 1950s marked the decade when artificial intelligence was officially “born,” with Alan Turing introducing this concept in his research paper “Computing Machine and Intelligence.” Five years later, researchers coined the term “artificial intelligence” during the Dartmouth College conference. Since that time, scientists from all around the world have diligently researched artificial intelligence, its benefits, and applications.

Despite being closely studied for years, it wasn’t until the 1970s that AI was introduced to the healthcare industry. MYCIN, an early expert system, was the first application of AI in life sciences, used to identify bacteria, diagnose some blood clotting diseases, and recommend treatments based on patient data.

Since the first application of artificial intelligence in healthcare and life sciences, both industries have experienced serious technological advancements and continue to find new ways to use AI for the greater good. The global market for AI in life sciences is projected to reach $33.5b in 2029, with a compound annual growth rate of 27.9%. The life sciences industry now uses AI to identify potential health risks, accelerate the drug discovery process, predict disease spread, facilitate the rapid development of medicine, and provide more accurate diagnoses.

Role of Artificial Intelligence in Life Sciences

When a new technology is implemented, its role is hard to determine at first. When the Internet first appeared, many were not sure how it would be useful in the life sciences sector. Some experts and companies were so reserved about going online that it took decades for many to finally move some operations to the World Wide Web.

To avoid the same thing from happening to the adoption of AI, it’s important to understand why it can and will, play a crucial role in our lives:

Artificial intelligence can create new opportunities. It’s not just about optimizing processes; it’s also about making discoveries, speeding up research, and augmenting the work of healthcare experts and scientists to achieve better healthcare outcomes faster.
AI can analyze patient data to identify patterns and changes, enabling more accurate predictions and disease diagnosis. This, in turn, can help discover the appropriate drug or treatment more quickly.
AI systems can contribute to maintaining sustainable practices in the pharmaceutical industry by analyzing energy consumption, optimizing drug distribution, and minimizing waste. All of that is possible thanks to its prediction capabilities, as artificial intelligence can predict how much energy and resources might be required and what can be done to cut consumption and reduce waste.
Artificial intelligence can assist governments and healthcare institutions in combating pandemics, including monitoring and predicting outbreaks, speeding up diagnostics and research, repurposing existing drugs, and even accelerating vaccine design and development.

AI in life sciences is a lot more than just a tool for optimization. It can aid scientists in breaking new ground and assist healthcare professionals in achieving improved patient outcomes. The success of AI implementation depends solely on the people responsible for its adoption, which is why it’s so important to finally learn more about artificial intelligence and its capabilities instead of avoiding it.

How Artificial Intelligence is Advancing Life Sciences

How is AI reshaping the life sciences industry today? Here is how artificial intelligence and machine learning are revolutionizing pharma:

Clinical trials

Right now, researchers are actively looking for ways to implement AI into clinical trials that would help scientific research and speed up the matching of patients to trials. One example of how AI is already used is an algorithm developed by the National Institutes of Health, TrialGPT, that can match people who meet the enrollment criteria to the relevant clinical trials.

Drug discovery

Over the past five years, the presence of AI in the drug discovery field has grown significantly. In 2024, the top 20 pharmaceutical companies have already announced that they are actively researching uses for AI in R&D. Life sciences companies can use AI to analyze vast amounts of data and identify drug targets, design small molecules, generate synthesis pathways for chemical compounds, and predict drug properties. The drug discovery process can take up to 15-20 years, and with the help of AI, it’s possible to shorten it, accelerate the search for potential drug candidates, and increase the success rate.

Chatbots and virtual assistants

AI-powered chatbots are continuously improving, with some bots even being capable of using real-time data to generate detailed responses and answer complex questions. In life sciences, chatbots and virtual assistants can be used by pharmaceutical companies to improve communication with patients.

Drug repurposing

AI can help pharmaceutical companies produce multi-purpose drugs without having to create new ones. AI-based algorithms can analyze already existing drugs and their chemical components to devise new ways to treat different diseases with the same pharmaceuticals.

Genomics

Genetics is a fascinating field of study that allows us to better understand many diseases and conditions. With AI, it’s possible to deepen this knowledge and learn more about our DNA and how to prevent some mutations from happening. Artificial intelligence can identify patterns and genetic variants, analyze genomic data, and even predict gene interactions.

AI in Life Sciences: TOP Use Cases

AI has a broad range of applications in the pharmaceutical industry; many companies have already started to implement artificial intelligence into their daily operations. Here are the top 3 use cases of AI in life sciences right now:

Atomwise. This San Francisco-based company focuses on using AI to accelerate scientific research and drug discovery by developing 3D models of molecules and employing AI to predict the bidden affinity of those molecules.
Google Health. Google has launched numerous healthcare projects leveraging AI to improve patient care and assist people in remote areas. One of its most significant AI research initiatives is an AI-powered screening tool capable of detecting tuberculosis in its early stages.
Johnson & Johnson. In collaboration with Rest Devices, the pharmaceutical company created an app that tracks babies’ sleep and gives advice based on the baby’s sleep routines. Combined with a wearable device that additionally tracks the infant’s body position, activity changes, breathing, and body temperature, parents can get insights into their child’s sleeping patterns.
Paige.ai & Microsoft. Paige, an organization that primarily focuses on building AI-driven solutions for healthcare organizations, has teamed up with Microsoft to create new AI models that provide an even deeper understanding of cells and tissue. Trained on data from over 225,000 patients, these models will aid healthcare experts in identifying and diagnosing cancer, deepening understanding of the disease and potentially even speeding up its treatment.
Illumina and AstraZeneca. This collaboration focuses on research on AI-driven drug discovery, looking for more ways to use genomics in combination with artificial intelligence to develop new drugs faster.

There are many great examples of how pharmaceutical companies and healthcare organizations use AI to drive discoveries and innovations. Even though there is still a long journey ahead, a single company can achieve remarkable outcomes by utilizing AI-powered tools even now.

Current Trends in AI for Life Sciences

What is the state of AI in the life sciences industry right now? Let’s take a look at current trends to see how most companies leverage artificial intelligence nowadays:

Generative AI

GenAI is the subfield of artificial intelligence capable of creating new content, such as texts, images, videos, and others. In life sciences, generative artificial intelligence can assist with medical imaging, creating simulations, scientific research, hypothesis generation, drug discovery and design, and other areas.

AI-powered patient care

According to the statistics provided by The Health Foundation in the UK, 54% of respondents support the use of AI for patient care, and an even bigger percentage, 61%, support AI integration for administrative purposes. Many pharma companies and healthcare institutions are already implementing AI to automate various operations and improve the quality of care; for example, some organizations have created virtual assistants that can answer patients’ questions at any time.

Personalization

Precision, or personalized medicine, shifts from the “one-size-fits-all” solution when it comes to treating diseases and concentrates on tailoring the healthcare to each patient instead. Thanks to AI, it’s possible to do research on the right medicine faster, allowing healthcare providers to customize treatment plans on the go.

Remember that many of the trends might not live up to expectations, and this is totally normal. Not everything is supposed to last long, including even the most incredible trends. It’s okay to experiment and search for new, better ways to implement AI so that the technology provides you with the most value.

Future Prospects and Challenges

Every innovation comes with a set of demands and rules. As great as it is, AI still has a lot of unfulfilled potential, exploring which might lead to both positive and negative consequences. Here is what the future might look like for AI in life sciences.

Challenges

There are many possible challenges lying ahead, and here are some of them:

Data safety

No matter how noble the cause is, sharing your data with a third party and allowing AI to use it can be a little worrying. Moreover, many are worried that the data AI-powered software uses can be easily breached and stolen.

Ethical issues

One of the biggest questions of the modern day is, “How ethical is it to use AI?”. Some oppose its implementation because of the ways it’s created and trained, as companies often use data from open sources to do so, which sometimes involves materials produced by people who did not consent to that. Another concern is the possibility of AI taking over certain fields, potentially replacing human roles, particularly in creative domains.

A lack of expertise

AI in life sciences requires immense levels of expertise and knowledge, arguably more so than any other industry. Finding the best uses for AI and later interpreting the results it produces is the responsibility of properly trained experts. Without experts leading the innovation and making sure that integrating AI brings as many benefits as possible, it can be hard to realize the technology’s full potential.

Poor governance practices

During the early days of the Internet, nobody knew how to use it correctly. This quickly changed with new policies and rulesets, which allowed companies to regulate what their employees are permitted to do and share on the Internet and what they are not. The same level of responsibility should apply when companies introduce AI; however, currently, only 55% of businesses enforce formal policies and procedures regarding the use of artificial intelligence.

Future Prospects

It is fair to say that we stand at the dawn of the AI revolution. There are many opportunities and challenges ahead, making our AI journey all the more thrilling. Let’s discuss what the future might hold for this technology:

Speech AI

AI voiceovers and voice-controlled systems have gained popularity in the past few years; it’s now used to improve business performance, science research, and even entertainment. This technology will likely continue to evolve, with more features tailored to the needs of life sciences coming out in the future.

Remote monitoring

AI-powered devices and systems continue to improve, with many of them already capable of tracking patients’ states at long distances. As remote monitoring apps and platforms continue to advance, more medical professionals will be able to treat patients regardless of their location.

Quick disease diagnosis

With AI’s ability to quickly analyze massive data sets, it will become possible to diagnose patients much faster, even those who have symptoms similar to multiple conditions and illnesses. It might take a few weeks or months to get the right results, but with AI, this process might take just a few days, if not hours.

Final Remarks

In the end, as much as any technology artificial intelligence is all about how it is utilized. It can be used to develop life-saving therapies, build new business models, and devise personalized treatment plans, but it can also be misused for activities that don’t bring any value, such as deepfakes or low-quality art. AI is our canvas, and it’s up to our society to use it for something positive and transformative. Fearing and avoiding the technology will only delay its inevitable impact, so it’s best to start using it to build a better and more inclusive future.

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