Artificial intelligence (AI) is essentially everywhere. While it is most evident in the technology sector, the main advances of AI in pharma and in life sciences are now being felt, reshaping the industry from R&D to commercial. Although still in a nascent stage, early adopters are leveraging AI to accelerate products to market, drive cost efficiencies, and improve patient outcomes and care.
AI isn’t meant to compete with human endeavors. Instead, it enhances human capabilities by using various complex tools and networks to mimic the cognitive functions of a human, building adaptable rules on the fly. The software and systems involved in AI can interpret data (visual image, sound, or speech) and learn from it to make independent decisions to reach a given goal.
While there are numerous areas where life sciences and pharma use AI effectively today, there are other opportunities that AI can unlock for industry players to improve the likelihood of success.
Time and Cost Efficiencies in R&D
Life science and pharma companies are facing increasing R&D costs and lead-time challenges. In fact, only around 10% of drug development projects make it from Phase I to approval, and it takes 10-15 years and about $1 billion to develop one successful drug. The process requires a lot of complex analysis based on multiple data points and is highly dependent on the abilities of individual research analysts.
To reshape the value chain and bring life-saving therapies to market faster, AI in pharma can be used in lab equipment to automate portions of experiments by identifying vision-based characteristics and suggesting next steps, auto-completing findings, and suggesting conclusions. Meanwhile, voice-based AI, such as Alexa, can be trained with specific data scraped from various sources on the internet to assist in labs. Further, collecting and analyzing data and using machine learning to identify patterns will help reveal insights from lab experiments and aid research and new product development.
The payback: accelerated R&D, resulting in reduced costs, higher accuracy, and better products, as well as identification of new patterns and insights that humans may miss.
Patient Selection in Clinical Trials
Clinical trials are time-consuming, expensive, and prone to attrition. AI in pharma has the potential to disrupt clinical trials — from patient recruitment to adherence monitoring and data collection — as it complements big data very well, helping analyze and synthesize massive datasets.
For instance, combining unanalyzed historical structured and unstructured clinical trial data into advanced AI models can improve and accelerate patient selection decisions by highlighting high-probability targets. These targets can either be patients who are more likely to have a measurable clinical endpoint or a population that is more capable of responding to treatment. This can be further extended by proactively searching electronic health records to scout eligible patients.
Further, during active clinical programs, continuing to use advanced AI models enables real-time adjustment and course corrections. Researchers can monitor patient behavior and detect signs of patient dropout, allowing corrective measures that increase the likelihood of meeting set timelines. On the other hand, patients can better adhere to trial protocols with the help of a voice-enabled AI health assistant connected to IoT devices, such as mobile phones, smartwatches, and other wearables.
Quality Control and Health and Safety
Quality control and health and safety are critical elements in life sciences and pharma. Due to the high costs of quality testing, most companies today use manual supervision and random product sampling techniques to monitor product quality and ensure health and safety. However, the price of any error is exceptionally high and could be catastrophic for a company in this industry.
By using spectrogram analysis and computer vision checks at every stage of the manufacturing process, we can ensure ingredient quality is checked at every step, correct manufacturing procedures are followed, and all health and safety standards for both machinery and workers are met. This AI-powered process can enable pharmaceutical manufacturers to reduce reliance on manual supervision and random sampling, assuring quality with a higher degree of confidence.
Thanks to AI, customers are more satisfied, health and safety risks are lowered, labor costs are reduced, and the high cost of product recalls or damage caused to the brand equity is eliminated.
AI in life sciences and pharma is set to become widespread within the next decade, leading to a marked shift in the operating approach of those who wish to ride the wave. Click here to discover how Teleperformance can help your company develop an appropriate AI strategy and build your capabilities as well as create the best public sector customer service.