According to a report from Grand View Research, the global AI in the life sciences market reached an impressive $1.3 billion in 2020, and it’s expected to grow at an annual rate of 41.2% from 2021 to 2028.
But how exactly is AI transforming life sciences? In this article, we’ll explore the crucial role that AI plays in life sciences and pharma, highlighting its benefits, addressing challenges, and presenting real-world examples of companies that effectively use AI to revolutionize their operations.
AI in Drug Discovery and Development
Traditionally, drug development is a highly tedious, time-consuming, and costly process that involves screening large volumes of data. However, with the capabilities of AI, life sciences companies can now analyze vast amounts of biological data and discover new drug targets more quickly, potentially saving millions of dollars and countless lives.
AI algorithms can also enable the optimization of chemical compounds by predicting the ones most likely to be effective, thereby reducing the number of compounds that require testing in the lab. This also results in a significant reduction in the time and money needed to produce new drugs.
Schrödinger is a company that actively utilizes AI to accelerate the drug discovery process. By combining physics-based methods with machine learning, it evaluates and optimizes chemical compounds before production, enabling pharmaceutical companies, biotech firms, and academic researchers to develop new drugs more efficiently and at a lower cost.
AI in Clinical Trials
AI plays a significant role in automating clinical trials, identifying patient populations most likely to benefit from a novel drug, monitoring patients’ health in real-time, and detecting adverse events or other issues that may affect trial outcomes. In addition, AI algorithms, when integrated with a suitable digital infrastructure, can clean, aggregate, code, store, and effectively manage ongoing streams of clinical trial data. This, in turn, reduces cycle times and costs while improving clinical trial outcomes.
Pfizer, one of the world’s largest pharmaceutical companies, uses machine learning algorithms to identify relevant patient populations for their clinical trials and monitor their health during the trial period. The result is a notable decrease in clinical trial cycle times and costs.
AI in Personalized Healthcare
While traditional medicines are designed to treat a broad range of patients with similar symptoms, personalized medicine and precision healthcare are tailored to the unique characteristics of each patient. By using AI to gain insights from genetic information, forecast disease progression, and spot biomarkers, medical professionals can provide customized treatment plans for individual patients. This approach not only improves patient outcomes but also reduces costs and minimizes adverse reactions associated with traditional medicine.
Deep Genomics employs artificial intelligence to analyze genomic data and create tailored treatment plans for genetic disorders. The utilization of AI is especially effective in this context, as it can quickly and accurately process large amounts of data to spot patterns that would be difficult for humans to detect.
AI in Medical Imaging and Diagnostics
Diagnosing medical conditions using medical imaging, such as MRI and CT scans, poses challenges, such as the potential for human error and subjective interpretation. By utilizing early disease detection, image-to-image translation, and case prioritization, AI-driven applications can provide doctors with more accurate diagnoses. This, in turn, not only improves patient care but also holds the potential to save lives by detecting life-threatening diseases at their earliest stages.
Aidoc, an Israeli medical technology company, uses the power of AI to analyze medical images obtained from radiology scans in real time to detect potential abnormalities. This helps radiologists prioritize the most critical cases and reduce the gap between diagnosis and treatment, leading to improved patient outcomes.
AI in Operations
Applying AI to manufacturing processes enables life sciences companies to forecast demands and scale production accordingly to meet these needs. In addition, it can help address specific challenges in the supply chain, such as identifying the most efficient shipping routes, reducing transportation costs, and detecting counterfeit drugs to prevent them from entering the supply chain.
The pharmaceutical company Novartis partnered with several tech companies, including Microsoft and Google, to build an AI-driven solution that uses machine learning algorithms to analyze data from various sources, such as sensors, production systems, and logistics networks. As a result, this solution has enabled the company to identify all bottlenecks and inefficiencies in its operations, enhance manufacturing processes, and improve supply chain management.
Risks and Challenges of AI in Life Sciences
Despite the undeniable promises of AI and ML in life sciences, there are certain risks and challenges associated with these technologies, such as performance failures, data privacy, and bias in AI algorithms. To gain a better understanding, let’s break down each of these aspects.
Technology and Performance Failures
One of the biggest risks associated with the integration of AI in life sciences is the potential for technology and performance failures. As AI systems become more complex and sophisticated, there is an inherent risk of malfunctions, system crashes, or errors in data processing. Such failures can have a number of causes, ranging from latent defects to incorrect use by the operator. However, even a small mistake can lead to severe consequences, resulting in incorrect diagnoses, flawed drug discoveries, or inaccurate clinical outcomes. Ensuring the reliability and robustness of AI technologies in life sciences is crucial to preventing setbacks that may compromise patient outcomes and the credibility of the entire healthcare ecosystem.
Data Quality and Bias
Another significant challenge facing the integration of AI in life sciences today is the issue of biases. Potential factors that may contribute to bias in AI include biased training data, biased assumptions in algorithm development, and subjective human decisions in both data selection and the application of algorithm results. For instance, a system designed to diagnose cancerous skin lesions, trained on data representing one skin color, may produce inaccurate results for patients with a different skin color, thereby increasing the potential risks to their health. Therefore, it is imperative to address concerns related to data quality and actively reduce biases to ensure the fairness and equity of AI applications in life sciences.
Cyber Security
With the extensive use of AI in life sciences, the collection and analysis of large volumes of patient data have become commonplace. This surge in data utilization raises concerns about patient privacy and security. As AI algorithms process sensitive medical information, there is a need for stringent measures to safeguard patient confidentiality. Unauthorized access, data breaches, or misuse of personal health information pose significant threats to individuals’ privacy and can undermine public trust in AI-driven healthcare solutions. Striking a balance between harnessing the power of AI for medical advancements and upholding patient privacy is an ongoing challenge that requires continual attention.
Regulatory Compliance
As AI becomes increasingly prevalent in life sciences, ensuring that AI applications adhere to established regulations and standards becomes more complex. Regulators face the challenge of keeping pace with technological advancements while maintaining a balance between fostering innovation and safeguarding public health. Striking the right regulatory framework that addresses the unique aspects of AI in life sciences without stifling progress is essential. Failure to establish clear guidelines may lead to ethical concerns, compromised patient safety, and potential legal repercussions.
Skills Gaps and Access to Data
Skills gaps and challenges in accessing data are additional obstacles to the progress of AI initiatives. The AI Skills Gap study reveals that 93% of organizations in the US and UK prioritize AI in planned projects. However, according to the same study, the most significant barriers to the adoption of AI and machine learning (ML) are still the lack of skills (44%) and access to data (52%).
What's Next for AI in Life Sciences?
The future of AI in life sciences holds great promise, offering advancements such as accelerated drug discovery, improved clinical trials, and more efficient personalized healthcare, among others. However, this transformative journey comes with its own set of challenges and potential risks that require a collaborative approach to implementing ethical frameworks, robust regulatory guidelines, and transparent practices. This way, we can effectively unlock the full potential of AI, revolutionizing life sciences and improving the well-being of individuals on a global scale.
Embrace AI Technology in the Life Science Verticals with Coherent Solutions
At Coherent Solutions, we offer a comprehensive range of life science software development services, spanning biotechnology, pharmaceuticals, and medical devices. With over 25 years of expertise, we assist our clients in enhancing their AI capabilities and crafting high-quality AI-driven solutions that revolutionize research, development, and patient care in the life sciences industry. Reach out to our AI team today to explore how Coherent Solutions can support your technology initiatives.