Exploring the Opportunities and Challenges of AI in Life Sciences

AI (Artificial Intelligence) is no longer a theory in our textbooks, it is now involved in our day-to-day lives. Health care companies also started to adopt AI as it is quick to analyze data and perform activities which otherwise took days to complete.

The explosion of data in healthcare data and the need to make pharma and healthcare processes efficient to cope up with the growing demand for faster drug development and patient care. As the need and willingness to adopt AI is increasing it is time to analyze the benefits of AI, and the extent of its impact on the present healthcare industry.

In this article, we will tell you potential benefits of AI in, why the adoption of AI is in a nascent stage in the life sciences industry.

Before jumping to the benefits of AI in the life sciences industry, here are a few real-time examples of AI applications in action.

Real-time Applications of AI in Life Sciences Industry

AI is flexible and plays a significant role in all stages of the life sciences industry.

• Analyze the huge previous drug development data to predict the viability of future drug molecules.
• Scan numerous patient medical records in minutes to pick the right candidates to recruit for the clinical trial.
• Predict the success rates of the clinical trial protocols to ensure the clinical trials are a success.
• Streamline and automate data gathering for clinical site investigations.
• Intelligently mine data from various sources to understand the stakeholder’s voice.

These are just a few examples of what AI applications can accomplish in the drug development process. Now, let us look at the benefits companies can reap using AI.

Benefits of Artificial Intelligence and Machine Learning in Life Sciences Industry

AI is a broad term for human-like intelligence provided by computers, Machine Language (ML) and Natural Language Processing (NLP) are subsets under AI. The use of AI in patient care and drug development is already bringing revolutionary changes in the industry.

1. Reduce overall cost and saves time

Owing to the complexity of the drug development process, each drug will take at least a decade to complete, even then 9 out of 10 drugs fail during the clinical trial stage. AI applications can speed up certain processes in drug development, for example, AI algorithms can analyze previous drug development data and predict the success rates of certain drug molecules. This not only saves time, but also reduces the cost of experimenting with various drug molecules.

2. Efficient processes with reduced errors

Manual processes are time taking and are prone to human errors, this is where AI applications come into play. Using ML algorithms, one can train them to find patterns and make accurate predictions. On example could be, the occurrence of quality events within a clinical trial. With the help of AI, one can analyze previous clinical trials data to know the pattern in the occurrence of quality events within the various clinical trial sites, this can help in taking measures to prevent their recurrence in the future.

3. Business intelligence for better decision making

Data driven decisions are more accurate than gut instincts; however, previously there was no easier way to analyze the vast market data to extract insights. But, now with the help of AI, one can analyze humungous amounts of information and present the necessary data helpful in making the right decisions.

For instance, clinical trials are decentralized and happen across the globe, making them vulnerable to the various events happening across the world. Using AI, one can monitor these events and trigger an automatic location-based email to the business owners.

4. Effective compliance and regulations

The Life sciences industry is highly regulated hence a significant time is spent in being complaint with the regulations. AI can help reduce the time and money spent on regulatory submissions. Using AI one can automate some repetitive tasks such as generating regulatory documents or extracting data from clinical trials.

These are just some of the applications of AI in life sciences industry, the power of AI is so versatile and immense that it can be used at various levels in the whole drug and patient care processes. The future of AI looks bright, and it is just about time when the life sciences industry starts to embrace it full potential. But why is the adoption of AI happening at a snail’s pace? Read on to find out.

Challenges in Adopting AI for the Life Sciences Industry

Though AI makes life easy, the use of AI in pharmaceutical industry is still not up to the expected levels, let us explore the reasons for it.

• One major roadblock is the data disparity in the processes, the complex healthcare data is highly dispersed, heterogenous and incomplete. This makes adopting AI (which largely depends on data) a difficult task.
• The life sciences industry is highly regulated, with strict rules around data privacy and patient confidentiality. This makes it difficult to collect and use data for AI applications, as there are legal and ethical concerns around the use of personal health information.
• Building and deploying AI systems can be expensive, and many companies in the life sciences industry may not have the resources or infrastructure to support these efforts.
• Knowledge gaps between science and technology are also making it difficult to adopt AI in the life sciences industry.

Addressing these challenges will be crucial to unlocking the full potential of AI in the life sciences industry. It will require collaboration between industry, academia, and regulatory bodies to develop standards and guidelines for data collection, sharing, and analysis, as well as the development of new tools and technologies to support AI-driven insights.