Artificial Intelligence (AI) is revolutionising businesses in all industries. Some commentators even expect the adoption of neural networks will prove more momentous than the introduction of the internet and transform every sector, impact every business, and catalyse every innovation platform. The exponential rate at which AI is now developing is as scary as it is exciting. Leading experts have about how lives will be affected and the impact of AI on human workers. But there is little doubt that in the life sciences industry, AI will help to save many lives and reduce patient suffering.

Many life science companies are already leveraging the power of AI to make medical advances, with each use case being more exciting than the next. It has not gone unnoticed here at ������Ƶ that our partners are increasingly looking to hire people with experience in AI to aid their innovation efforts, putting those skilled individuals in high demand. In this article, we have listed the top 20 ways artificial intelligence is advancing life sciences and some of the companies who are already using it effectively.

1. Drug discovery

Drug discovery is vital because it allows us to develop new medications and treatments that can improve and save the lives of people suffering from various illnesses and diseases. AI can be used to help identify new drug candidates and predict their efficacy and safety. Compared to traditional discovery methods, this process is far more streamlined, meaning companies can potentially bring drugs to market quicker and more cost-effectively. This involves using AI algorithms to analyse large amounts of data to identify compounds that have the potential to be developed into drugs. This can involve screening databases of existing compounds or natural products, as well as using AI to design and synthesize new compounds.

Insilico Medicine has achieved notable success in drug discovery by identifying potential treatments for cancer and age-related diseases. The company was recently granted the FDA’s for a drug discovered and designed using artificial intelligence (AI) – a small molecule inhibitor treatment for idiopathic pulmonary fibrosis (IPF). Insilico is collaborating with pharmaceutical companies, including Pfizer, Novartis, and GSK, to advance its compounds towards clinical trials, demonstrating the potential of AI-driven drug discovery to accelerate the drug development process.

2. Clinical trials

Identifying the right patients for a clinical trial can be difficult and monitoring their response to treatment and any potential side effects is time-consuming and expensive. AI can help identify patient populations that are most likely to benefit from a new drug, monitor them in real-time, and detect adverse events or other issues that may affect trial outcomes.

Pfizer, one of the world's largest pharmaceutical companies, has been utilising artificial intelligence (AI) to optimize clinical trials. With the help of machine learning algorithms, for their clinical trials and monitor the patients' health during the trial.

3. Medical device design

Designing effective medical devices is a complex and challenging process that requires careful consideration of various factors, such as materials, shapes, and sizes. Traditionally, this process involved a significant amount of trial and error, resulting in a long and costly development cycle. However, AI can help to address these challenges by optimizing the design of medical devices. By using machine learning algorithms, AI can analyse large volumes of data to identify the most effective materials, shapes, and sizes for a given device, allowing developers to make more informed decisions. Additionally, AI can assist in the virtual prototyping of medical devices, allowing developers to test and refine designs in a digital environment, reducing the cost and time required for physical prototyping. By leveraging AI to optimize the product design, companies can accelerate the medical device development process, reduce costs, and bring better products to market faster.

The Johnson & Johnson Center for Device Innovation, located in Houston, is at the forefront of creating medical devices that utilize AI to enhance patient outcomes. Examples of include the Acuvue Oasys contact lenses, which are optimised for each wearer.

4. Drug design

Drug design is the process of creating new drugs or optimizsing existing ones to improve their therapeutic properties. AI is used to predict how a newly designed compound will interact with biological targets, such as proteins or enzymes, and optimize its properties to increase its efficacy and reduce side effects. This can involve using AI to predict the 3D structure of the target molecule and design a molecule that will fit into it, as well as predicting how the molecule will behave in the human body.

is a company that uses AI to speed up the process of discovering new drugs. They combine physics-based methods with machine learning techniques to evaluate and optimize chemical compounds before making them. This helps pharmaceutical companies, biotech firms, and academic researchers design and develop new drugs more efficiently and at a lower cost.

5. Personalised medicine

Traditional medicines are often designed to treat a broad range of patients with similar symptoms, while personalised medicine is tailored to the specific genetic makeup and characteristics of each patient, considering individual variability in genes, environment, and lifestyle. AI can generate insights from genetic information, biomarkers, and other physiological data to tailor treatment plans to individual patients by predicting how they will respond to different treatment options, which may help avoid adverse reactions, reduce the use of expensive or unnecessary treatments on patients that are unlikely to respond, and reduce hospitalisation and outpatient costs.

Deep Genomics is a company that is using AI to analyse genomic data and develop precision therapies for genetic diseases. AI is particularly well-suited for this type of work because it can quickly and accurately process large data sets to identify patterns and relationships that would be difficult for humans to detect. Deep Genomics CEO, Brenden Frey, , “We have built a system that within two hours can scan over 200,000 pathogenic patient mutations and automatically identify potential drug targets.”

6. Drug repurposing

Drug repurposing is the process of identifying new uses for existing drugs that were originally developed for different medical conditions. This is done to potentially save time and money in the drug development process and to bring effective treatments to patients more quickly. AI can help identify existing drugs that may be effective in treating other diseases or conditions. The process involves using machine learning algorithms to analyse the chemical structures and properties of drugs and then comparing them to information about diseases and biological pathways.

London-based BenevolentAI is one such company using AI to identify new uses for existing drugs. GlobalData reports that BenevolentAI has , including drugs for Parkinson's disease and COVID-19.

7. Biomarker identification

Biomarker identification is the process of finding a measurable biological indicator that can help diagnose, predict, or monitor a disease or treatment in medicine. AI can help to identify biomarkers by analysing biological and clinical data from patients, identifying patterns and correlations that may be too complex for humans to detect. Neural networks can be trained on this data to recognize specific biomarkers that are associated with a particular disease or condition, which can then be used to develop more accurate diagnostic tests or personalised treatments.

is a company that uses advanced genomic testing to analyse the DNA of cancer patients and identify potential biomarkers that can be used to personalise treatment plans. They employ artificial intelligence and machine learning algorithms to analyse large datasets of genomic data from tumour samples, searching for patterns and correlations that can help predict treatment response and disease progression. By identifying these biomarkers, Foundation Medicine is working towards improving cancer treatment outcomes by developing more targeted and effective therapies tailored to individual patients.

8. Chatbots and virtual assistants

AI-powered chatbots have multiple potential uses for life science companies, including providing customer support, streamlining the recruitment process for clinical trials, serving as virtual assistants for medical professionals, generating leads and providing personalized product recommendations, and assisting with data analysis to inform business decisions. These chatbots can help improve customer engagement and satisfaction, increase efficiency, and cost savings, and provide valuable insights to inform overall business performance.

Pharmacovigilance management company, MyMeds&Me, in partnership with , has developed a chatbot, called Phoebe, that helps patients and healthcare professionals report adverse drug reactions in a conversational manner. MyMeds&Me is using Phoebe to streamline their reporting process, reduce the time and effort required to complete a report, and increase the accuracy and completeness of the data collected.

9. Medical imaging analysis

The challenges of diagnosis from medical imaging, such as MRI and CT scans, include the potential for human error, the subjectivity of interpretation, and the need for specialised training to accurately interpret complex images. AI can help analyse medical images to detect early signs of diseases and empower doctors to make more accurate diagnoses. This can potentially avoid the need for invasive diagnostics, such as biopsies, that would normally be required to confirm a diagnosis. This can lead to better patient outcomes and a more efficient healthcare system.

Israeli medical technology company is one such example that uses AI to analyse medical images from radiology scans in real time to flag potential abnormalities. This helps radiologists to identify and prioritize cases that require immediate attention, allowing them to focus on the most critical cases first. This helps to reduce the time between diagnosis and treatment, improving patient outcomes.

10. Electronic health record (EHR) analysis

Electronic health records (EHRs) are digital versions of a patient's medical history that allow healthcare providers to access a patient's health information easily and enable patients to become more involved in their healthcare. AI can assist in analysing EHR data by using algorithms to identify patterns and trends that may not be immediately apparent to humans.

These insights can be used to improve patient care and outcomes by enabling healthcare providers to make more informed decisions about treatment options and personalized care plans and help identify potential health risks before they become serious, allowing for earlier intervention and preventive care. Additionally, companies such as to help to identify patients who are eligible for clinical trials and to track patient outcomes.

11. Supply chain optimisation

The current challenges of pharmaceutical supply chains include issues such as a lack of transparency, inefficient inventory management, and a fragmented distribution network. These challenges can lead to drug shortages, delayed shipments, and increased costs, which can impact patient care. AI can help optimize the pharmaceutical supply chain by predicting demand, managing inventory, and reducing waste. This helps to ensure excess inventory does not go unused and minimises stockouts that can lead to delays in patient care.

In addition, AI can help address specific challenges in the pharmaceutical supply chain, such as identifying the most efficient shipping routes to minimize transportation costs and detecting counterfeit drugs to prevent them from entering the supply chain. By leveraging AI technologies, pharmaceutical companies can improve the efficiency, accuracy, and safety of their supply chain operations, delivering better outcomes for patients.

Novartis and ensure that its products are available when and where they are needed. The pharmaceutical company has partnered with several tech companies, including Microsoft and Google, to develop AI-driven solutions that can enhance its manufacturing processes and supply chain management. Novartis has leveraged machine learning algorithms to analyse data from various sources, including sensors, production systems, and logistics networks. This has enabled them to identify bottlenecks and inefficiencies in its supply chain, allowing the company to make real-time adjustments to improve productivity and reduce costs.

12. Drug pricing

Pharmaceutical companies face several challenges in setting a drug pricing strategy, including the need to balance profitability with affordability for patients and healthcare systems. The field of health economics is complex, and companies need to navigate regulations and pricing negotiations with payers and government agencies, while considering factors such as research and development costs, production expenses, and market demand. AI can predict the value of a drug and determine the optimal pricing strategy by analysing vast amounts of data from various sources, including clinical trials, real-world evidence, and market trends. Machine learning algorithms can identify patterns and relationships in this data, allowing pharmaceutical companies to estimate the potential value of a drug and its impact on patient outcomes. AI can also simulate different pricing scenarios and predict their financial implications, enabling companies to make data-driven decisions on pricing strategy. By leveraging AI, pharmaceutical companies can optimize drug pricing to balance profitability with affordability, improving patient access to life-saving treatments.

#Leading pharma information provider, Elsevier, is promoting a new medication pricing standard called Predictive Acquisition Cost (PAC), developed by Glass Box Analytics, that asserts to track actual drug acquisition prices more precisely. According to the company, PAC uses predictive analytics to consider a variety of factors when estimating the acquisition cost of a drug, including maximum allowable cost benchmarks, published price lists, existing price benchmarks, drug dispensation metrics, supply-demand measures, and survey-based acquisition costs.

13. Drug dosage optimisation

Prescribing the right dosage of a drug can be difficult because everyone's body processes drugs differently. Doctors must balance how well the drug works with how safe it is as incorrect dosage can lead to negative side effects. AI can be used to help determine the optimal dosage of a drug for individual patients based on factors such as age, weight, and medical history.

AiCure uses AI to according to patient input during clinical trials. They use machine learning to predict how patients will respond to medication and track changes in patient health and response. This enables them to optimize dosage to enhance efficacy while reducing undesirable side effects, resulting in a more comprehensive understanding of the patient's treatment experience.

14. Disease diagnosis

Diagnosing some illnesses can be challenging because they often present with symptoms that are similar to other conditions or have subtle or nonspecific symptoms that can be difficult to detect. In some instances, symptoms may even not show up at all until the disease has progressed to an advanced stage. Artificial Intelligence (AI) can assist in diagnosing difficult cases by analysing substantial amounts of medical data, such as patient records and lab results, to identify patterns and detect anomalies that may not be immediately apparent to human doctors. AI algorithms can also learn from these data sets and improve their accuracy over time, potentially leading to earlier and more accurate diagnoses.

Babylon Health uses AI to analyse symptoms and medical history to provide diagnoses and treatment recommendations. According to a , Babylon's diagnostic accuracy was comparable to that of primary care physicians for a range of medical conditions, including respiratory infections, skin problems, and urinary tract infections. Additionally, the app also demonstrated the ability to triage patients for urgent care.

15. Genomics research

Genomics research is the study of an organism's complete set of DNA and involves analysing the DNA sequence to understand how genes function and how they interact with each other. This helps scientists make medical advancements by identifying genetic variations that contribute to diseases and developing targeted therapies based on an individual's unique genetic makeup. The use of AI can expedite the analysis of genomic data to identify disease-causing mutations and potential drug targets much faster.

Illumina is a biotechnology company that specializes in genetic sequencing and analysis and uses artificial intelligence (AI) to help identify patterns and correlations that may be difficult for humans to spot. to use artificial intelligence (AI) to help identify potential drug targets and accelerate the development of new treatments.

16. Predictive analytics

AI can predict a range of factors related to disease progression and patient outcomes. For instance, AI can help identify patients who are at risk of developing a particular disease, or who may experience certain complications or adverse events during treatment. AI can also predict the effectiveness of specific treatments for individual patients, based on factors such as their medical history, genetics, and lifestyle. Additionally, AI can help forecast disease progression over time, which can inform treatment planning and allow for earlier intervention in some cases. Overall, AI has the potential to improve healthcare outcomes by enabling more accurate and personalized treatment decisions based on individual patient data.

is a healthcare technology company that uses predictive analytics and artificial intelligence to forecast the progression of cancer. Their platform can analyse various types of medical data, including medical images, pathology slides, and genomics data, to identify patterns and make accurate predictions. The predictive analytics technology can assist healthcare providers in identifying the likelihood of disease progression and the potential effectiveness of different treatments. This can help improve patient outcomes and potentially save lives. Paige.ai's forecasting capabilities have the potential to transform the field of healthcare by enabling earlier interventions and more personalized treatment plans for patients.

17. Patient monitoring

AI can help monitor patient data from medical devices, such as glucose monitors and pacemakers, to improve patient outcomes and alert healthcare providers to potential issues.

There are several companies using AI in medical devices for patient monitoring, including Medtronic, which analyses data from its . The company's AI algorithms are designed to detect changes in a patient's condition and provide timely alerts to healthcare providers, allowing them to take necessary action before a serious issue arises. This proactive approach helps healthcare providers to intervene in a timely and efficient manner, improving patient outcomes and potentially saving lives.

18. Natural language processing

AI-powered natural language processing (NLP) is a powerful tool that can help extract useful information from medical texts, such as research papers and clinical trial data. With the sheer volume of medical data available, NLP can quickly analyse and extract relevant information, saving time and resources that would otherwise be spent manually analysing data. This can lead to faster and more accurate diagnoses, more effective treatments, and improved patient outcomes. Additionally, NLP can help researchers identify patterns and insights that may not have been apparent before, leading to new discoveries and breakthroughs in medical science.

In late 2019, Canadian-based AI-platform, BlueDot, identified a cluster of pneumonia-like cases in Wuhan, noticing similarities with the SARS virus. BlueDot uses NLP to cull data from thousands of disparate sources before alerting physicians to anomalies. That was the that has come to be known as COVID-19. It would be another nine days before the World Health Organization released its statement alerting people to the emergence of a novel coronavirus.

19. Fraud detection

Prescription drug fraud can lead to serious health consequences for those who take the medications without proper medical supervision and can contribute to the larger issue of drug abuse and addiction. AI can help prevent this by using machine learning algorithms to detect patterns and anomalies in prescription data, such as excessive quantities or dosages of certain drugs, or suspicious prescribing patterns by healthcare providers. AI-powered systems can also compare prescription records with other data sources, such as criminal records and insurance claims, to identify potential instances of fraud or abuse. By detecting and preventing prescription drug fraud, AI can help promote patient safety and reduce the overall impact of drug abuse on society.

One of the leading players in this space, , uses AI-powered analytics to monitor electronic health record (EHR) data and detect patterns that may indicate prescription fraud or abuse. The platform flags potential instances of fraud or abuse for further investigation by healthcare providers, allowing them to take timely action to prevent harm to patients.

20. Regulatory compliance

AI can help pharma and medical device companies with regulatory compliance by automating the process of monitoring and analysing data related to regulatory requirements. AI-powered systems can flag potential compliance issues, such as incomplete or inaccurate documentation, and provide alerts to compliance teams for further investigation. This can help companies to proactively identify and address compliance issues, reducing the risk of fines, legal action, and damage to the company's reputation.

Veeva Systems offers an AI-powered platform called that helps pharma and medical device companies with compliance and quality management. The platform uses AI to automate compliance processes, identify potential issues, and streamline workflows, helping companies to maintain compliance with regulations and standards. Other notable companies in this space include ComplianceQuest, Sparta Systems, and MasterControl.

What is next for AI in life sciences?

As companies in the life sciences sector continue to innovate and experiment, it is certain that even more new use cases for AI will come into existence, transforming the industry, and improving patient outcomes in ways that we are only just beginning to understand. It is an exciting time for life sciences, and AI will undoubtedly play a significant role in shaping its future. However, life science businesses that fail to adopt AI run the risk of falling behind their competitors in terms of efficiency, innovation, and success.

If your life sciences business is looking to hire skilled professionals in the field of Artificial Intelligence, .

Do you have experience working within AI in life sciences? so our specialist recruitment team can contact you about exciting new career opportunities.