Artificial intelligence (AI) is used to describe the development of ‘intelligent machines’ that can mimic, and in some cases, surpass human intelligence. The development of AI systems and models has been one of the most profound technological advancements in recent decades. Within the life science industry, AI has had a profound impact, from accelerating the drug discovery process to enhancing patient care, and its influence is only set to increase. 

This article provides a comprehensive into the role of AI in life sciences. We’ll run through a brief history of artificial intelligence, discuss its applications within life science, and outline some of the best practices when utilizing the potential of AI. 

This article covers:

  • Historical context of artificial intelligence
  • Current trends of AI in life science 
  • Benefits of AI in life science 
  • Ethical considerations 
  • Challenges and limitations of AI 
  • The future of AI in life science 
  • Best practices for integrating AI in life science 
  • Use AI to transform your organization with Sapio Jarvis

Historical context

A brief history of AI 

The concept of artificial intelligence, or AI as it is commonly referred to, can be traced back to the mid-20th century and is used to describe the pursuit of creating ‘intelligent machines’ (machines capable of human intelligence). 

It was in the 1950s when the field of AI emerged and by 1956 it became formally recognized as a field of study with researchers starting to explore the potential of machine intelligence. By the 1960s and 1970s, early AI systems had been developed to solve specific problems and by the 1980s and 1990s, the focus of AI had shifted towards neural networks and machine learning. Today, in the 21st century, artificial intelligence is undergoing exponential growth and development, with breakthroughs such as deep learning and speech recognition being made.

Early applications of AI in life science 

Some of the earliest examples of artificial intelligence being used in life sciences date back to the 1960s and 1970s. A couple of the most notable early applications of AI in the industry include:

  • Dendral: Dendral was one of the first AI life science systems, developed in 1965 at Stanford University. It was designed to analyze mass spectrometry data and infer the possible chemical structures of organic compounds. Dendral paved the way for future application of AI in chemistry and bioinformatics.
  • MYCIN: MYCIN was a later AI system, developed at Stanford University in 1976. It focused on diagnosing bacterial infections and subsequently recommended antibiotic treatments. 

These early examples marked the beginning of AI’s integration into the life sciences, setting the stage for the significant advancements and innovations we see today.

The integration of artificial intelligence in the life sciences industry has evolved and progressed since its early applications in the 1960s. Here are just some of the ways AI is currently used in the field of life science.

Machine learning and deep learning in genomics

Machine learning and deep learning are two subfields of AI that are being used to transform genomics.

Machine learning refers to machines having the ability to learn about a dataset without being explicitly programmed on what to learn. Deep learning, on the other hand, is a technique used to implement machine learning. When combined, machine learning and deep learning are being used within life science to improve our understanding of hidden patterns in vast and complex genomic datasets. 

One way in which machine learning and deep learning is being used in genomics is in the study of oncology. Here, machine learning is being used to analyze liquid biopsies in order to identify the primary kind of cancer. Other applications of machine learning and deep learning in genomics include using AI technologies to analyze human faces to detect and understand genetic disorders.

AI-driven drug discovery and development

AI is playing a pivotal role in the drug discovery and development process. Through the utilization of machine learning and deep learning, AI systems and models can identify patterns within drug discovery datasets. They can not only confirm whether a particular target is suitable for drug development, but it can also assist in refining the design of drug molecules for greater efficacy.

By having these capabilities, AI can be used to propose the creation of new molecules, enhance the properties of existing drugs, and even uncover entirely new drug candidates. 

AI in medical imaging and diagnostics

AI-powered medical imaging enables doctors to not only provide more accurate diagnoses but also offer treatment options that are more personalized. 

Through deep learning, medical imaging allows medical practitioners to identify anomalies and diseases with a high level of precision. This means that patients can benefit from earlier and more accurate diagnoses, resulting in timely interventions and improved healthcare outcomes. Moreover, the use of AI in medical imaging and diagnostics can be used to guide treatments that are tailored to the patient, enhancing both the efficacy and overall patient experience.

AI-powered wearable devices and their impact on healthcare

Al-powered wearable devices generate and harness a wealth of health data. These devices, when coupled with Al algorithms, enable in-depth analysis that can identify potential health risks and provide individuals with valuable insights that can allow them to proactively manage their health. 

One example of how Al-powered wearable devices are impacting healthcare is in the management of chronic diseases. Here, individuals suffering from a chronic disease can wear Al-powered devices so that insights such as the progression of the disease and the treatment efficacy can be captured. With this information, individuals and health professionals are better able to treat and monitor the disease. 

Aside from helping those with pre-existing health conditions, Al-powered wearable devices can play an instrumental role in the early detection of diseases. Through the continuous generation of data, individuals can monitor their health enabling timely interventions and ultimately improving overall healthcare outcomes.

Benefits of AI in Life Sciences

Utilizing artificial intelligence in the life science industry holds a vast range of opportunities and advantages. Just some of the benefits of AI in life sciences include:

Speeding up research and reducing costs

One of the primary benefits of incorporating Al in life sciences is its ability to process and analyze a vast amount of data at a much faster rate than healthcare professionals and researchers. This acceleration in data processing enables scientists and researchers to generate insights and breakthroughs at a quicker rate, speeding up the research process.

Furthermore, the use of artificial intelligence in the life science industry can be used to automate tasks. This automation can streamline workflows and reduce the time spent on routine tasks. Beyond optimizing workflows and enhancing time-effectiveness, this automation can reduce the costs associated with research in the life sciences as time and resources are utilized more efficiently.

Enhancing accuracy in diagnostics

Al models and systems are able to detect patterns, trends, and anomalies that a medical professional may not have. Due to its attention to detail, the usage of Al in life science can enhance accuracy in diagnostics. 

For instance, Al can identify early signs of diseases, often before they manifest noticeable symptoms. This early detection is instrumental in treating diseases in their initial stages, which can significantly improve patient outcomes and reduce treatment costs. Furthermore, as AI systems are not prone to fatigue or distractions the diagnostic results they produce are reliable. 

Personalized medicine and treatment recommendations

A further benefit of using artificial intelligence in life science is its ability to create personalized medicine and treatment recommendations by analyzing patient data. 

Not only can AI models assess a patient’s risk of developing specific diseases, but they can be used to identify trends and anomalies in data to predict what drugs or therapies may be most appropriate. With this data, healthcare professionals are able to tailor treatments and interventions to the specific needs of each patient, maximizing the effectiveness of treatment.

Predictive analytics for patient outcomes

By analyzing patient data such as medical history, lifestyle habits, genetic information and environmental factors, Al can help identify patients at risk of developing specific conditions or diseases before any symptoms appear. With these insights, healthcare professionals are able to introduce early intervention or preventative strategies such as lifestyle changes, targeted screenings or medication to improve patient outcomes.

Ethical considerations

Integrating AI into the life sciences industry requires ethical considerations to ensure that it reaches its full potential while mitigating potential risks. By addressing ethical considerations, the use of AI in life science has the ability to transform the industry.

Data privacy in AI-driven medical research

Al models are trained using data from medical research. Subsequently as the demand for Al grows, so does the need for data. This, in turn, creates concerns about patient privacy as the relationship between health systems and Al developers grows closer. These concerns can create questions about the need to gain patient consent when it comes to the sharing of data with Al developers. 

Moreover, as Al models become more developed concerns arise about breaching data privacy in another way. Machine learning and algorithms are used to detect and identify trends, which may result in Al predicting private patient information despite that information never being disclosed to them. For instance, an Al system may detect that someone has Parkinson’s disease based on subtle tremors, despite not receiving this information. This could be perceived as a breach of privacy, particularly if these findings of are shared with third parties.

Bias and fairness in AI algorithms

A further ethical consideration about artificial intelligence in life science surrounds bias. Al systems reflect the data that was used to train them, this can inadvertently mean that artificial intelligence can inherit biases if they were present in the training data. For instance, an Al model trained on data that was generated within an academic center will be less relevant to patients whose demographics were not represented in these settings. This lack of diverse training data can reduce accuracy when applying insights to underrepresented segments.

Possibility over-relying on AI in medical decision-making

There is a concern that the over-reliance on artificial intelligence to analyze vast amounts of data within life science could inadvertently decrease human knowledge and capacity over time. 

One possibility is that as Al takes on an increasingly central role in medical diagnoses and treatment, healthcare providers may become reliant on the suggestions raised by these systems. Over time, this over-reliance may result in a gradual decline in the ability of medical professionals to independently interpret medical information and produce recommendations based on this. Likewise, an over-reliance on artificial intelligence could result in a lessened ability to identify and rectify any mistakes generated by AI.

Challenges and limitations of AI

Despite its strengths and potential, there are several challenges and limitations of AI that need to be addressed. These include the need to balance data quality with quantity, optimize AI’s ability to interpret results in a biological context and maximise interdisciplinary expertise.

Data quality and quantity

As discussed, the quality of artificial intelligence is dependent on the training data. Therefore, if the data used to train these systems contains bias, the Al model will inevitably also display signs of bias. Any presence of bias within AI systems has the ability to create discriminatory outcomes.

Another limitation of AI in the context of data quality and quantity surrounds its processing. While one of the key strengths of artificial intelligence in life sciences is its ability to analyze and sift through a vast amount of data, this does not necessarily equate to high-quality data processing. For this reason, it is crucial that any errors or inconsistencies present in the data are addressed, however, this adds an extra layer of complexity and time to those projects utilizing artificial intelligence.

Interpreting AI results in a biological context

There is no doubt that Al models are extremely powerful in their ability to interpret complex data. However, in a biological context, many interrelated variables are involved. Therefore, while Al may be able to identify patterns between these variables, they may be unable to explain the underlying cause.

Need for interdisciplinary expertise

For the reasons addressed above, interdisciplinary expertise is essential when using artificial intelligence in life science. 

By nature biological systems are incredibly complex. However, through utilizing interdisciplinary expertise a comprehensive view of biological systems can be harnessed. By having a comprehensive view of life science, AI models and systems can be developed that better capture the complexities of biological systems. 

Additionally, taking advantage of interdisciplinary expertise can enhance the training of AI and its subsequent analysis. This is because, in the field of life science, a vast range of datasets are used. Therefore by leveraging the knowledge of various experts, these datasets can be integrated to improve the quality of training data.

Future of AI in life sciences

Today, AI has a significant impact on the life sciences industry, yet its influence is only set to grow as artificial intelligence continues to advance. Here are just a few of the future implications of AI in the field of life sciences. 

Integrating quantum computing with AI

The integration of quantum computing with Al in life sciences has the potential to transform the industry. 

For instance, in the context of drug discovery and development, quantum computing has the potential to accelerate the process of replicating complex simulations of molecular structures. In doing so, quantum computing can analyze vast datasets to identify potential drug candidates more efficiently.

The role of AI in understanding and treating complex diseases (like Alzheimer’s)

Al’s ability to spot patterns and trends can be used to better understand and treat complex diseases. 

Take Alzheimer’s disease for instance, through using Al systems researchers can identify patterns and changes in memory by drawing on insights generated from a vast amount of data. With this data, artificial intelligence can be used to predict who may have an increased risk of getting the disease, how it may impact the brain over time, and how the symptoms may evolve, enabling health professionals to take the appropriate action.

Aside from benefiting the diagnosis and treatment of complex diseases, artificial intelligence can be developed to enhance patient care. For instance, in the context of Alzheimer’s disease, developments are underway to introduce sensors into households. These sensors will be able to monitor an individual’s behavior and detect signs of deteriorating health, allowing for medical intervention.

The role of AI in precision medicine

Precision medicine is an approach to healthcare whereby individuals receive tailored treatments based on a range of factors, such as their genetic information or lifestyle. 

Al has the ability to play a pivotal role in advancing precision medicine as it can be used to analyze vast datasets to guide personalized treatment. For instance, artificial intelligence systems can be used to predict the likelihood of an individual developing a specific disease based on their genetic profile. Through this trend identification, Al models can be used in precision medicine to guide early intervention and prevention strategies.

Best practices for integrating AI in life sciences

Artificial intelligence has the potential to transform the life science industry, but its improper use can lead to significant problems. To maximize the benefits and avoid potential issues, it’s crucial to understand the best practices for integrating AI in life sciences.

Continuous learning and model validation

In the field of life sciences, data is constantly evolving, influenced by factors such as new research and demographic changes. To keep pace with these changes, continuous learning is crucial to ensure that AI models are up-to-date and reflective of wider societal shifts and trends.

However, as discussed, AI models can inherit biases from the data used during their training. For this reason, the adoption of continuous learning serves to not only identify and correct these biases but to also ensure that the AI systems remain fair and reflective of the wider population.

Another crucial aspect to consider to ensure the effectiveness of AI models in life science is the incorporation of feedback from healthcare professionals and researchers. By acting on regular feedback, AI models can be developed to better align with the needs and requirements of clinicians and researchers. 

Ethical AI use and adhering to regulations

When using Al models and systems within life sciences it is crucial to not only use these systems ethically but to also adhere to regulations. 

Compliance with data protection regulations ensures that sensitive health and medical data remains secure and confidential. This prevents data breaches that have the potential to compromise research and patient information. However, like everything in the life sciences industry, regulations are subject to change. For this reason, continuous learning can be adopted to ensure that artificial intelligence systems remain compliant in the face of evolving regulations. 

In addition to adhering to regulatory requirements, it is good practice to utilize artificial intelligence in an ethical way. This means gaining informed consent from patients and research participants if you intend to use their information in AI processes. Through gaining this consent trust is built and the ethical use and collection of data is supported.

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Resources and further reading

Further reading

“Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again” by Eric Topol. This book explores the impact of AI on healthcare.

“Artificial Intelligence in Medicine” by Anthony C. Chang. A comprehensive talk about AI applications in medical practice.

“Beyond the hype: ‘acceptable futures’ for AI and robotic technologies in healthcare” – A review article on the current state of AI in healthcare.

“The Promise and Peril of AI in Healthcare” – An article on the ethical considerations of AI in healthcare.

Courses

Stanford University offers a course titled “AI in Healthcare” that covers the application of AI in medical settings.

Coursera offers “AI in Healthcare Specialization” a course also provided by Stanford University, focusing on AI applications in the healthcare industry.

Organizations:

The Deloitte AI Institute is a notable organization that focuses on AI insights and innovation, bringing together experts in AI.

AI for Good is a global initiative by the United Nations, promoting the ethical use of AI, including in life sciences.

Thought Leaders

Eric Topol, a prominent cardiologist and author, is known for his work on AI’s impact on medicine and healthcare.

Anthony C. Chang, a pediatric cardiologist, is an expert in AI applications in healthcare and medicine.