Rasi Bhadramani/istock via Getty
AI in the Pharma Industry: Current Uses, Best Cases, Digital Future
Nearly 50% of global healthcare companies will implement artificial intelligence strategies and by 2025 and some experts believe it is crucial for how businesses operate down the line.
Pharmaceutical executives are looking for ways to leverage artificial intelligence and machine learning within the healthcare and the biotech industry. Reports show an increasing number of entities are realizing current use cases, driving the digital future of the tech in the industry.
Top pharmaceutical companies are collaborating with AI vendors and leveraging AI technology in their manufacturing processes for research and development and overall drug discovery.
In fact, reports show nearly 62 percent of healthcare organizations are thinking of investing in AI in the near future, and 72 percent of companies believe AI will be crucial to how they do business in the future.
To get a better sense of the future of AI in the sector,, PharmaNewsIntelligence dives into current AI use cases, the best uses for the technology, and the future of AI and machine learning.
How AI is Currently Being Used in Pharma
The McKinsey Global Institute estimates that AI and machine learning in the pharmaceutical industry could generate nearly $100B annually across the US healthcare system.
According to researchers, the use of these technologies improves decision-making, optimizes innovation, improves efficiency of research/clinical trials, and creates beneficial new tools for physicians, consumers, insurers, and regulators.
Top pharmaceutical companies, including Roche, Pfizer, Merck, AstraZeneca, GSK, Sanofi, AbbVie, Bristol-Myers Squibb, and Johnson & Johnson have already collaborated with or acquired AI technologies.
In 2018, Massachusetts Institute of Technology (MIT) partnered with Novartis and Pfizer to transform the process of drug design and manufacturing with its Machine Learning for Pharmaceutical Discovery and Synthesis consortium.
The consortium aims to break down the divide between machine learning research at MIT and drug discovery research by bringing researchers and industry together to identify and address the most significant problems.
GSK also entered into a collaboration with Cloud Pharmaceuticals to accelerate the discovery of novel drug candidates. And in April 2020, GSK and Vir Biotechnology partnered to enhance COVID-19 drug discovery through CRISPR and AI.
Two months later, Roche and Owkin, a machine learning platform for medical research, partnered to speed up drug discovery, development, and clinical trials.
And most recently, Abbott launched a coronary imaging platform powered by artificial intelligence. The platform can detect the severity of calcium-based blockages and measure vessel diameter to boost the precision of decision-making during coronary stenting procedures.
“There’s so much unmet need out there when it comes to medications. We’re witnessing a revolution in healthcare. Artificial intelligence is giving us the ability to discover new treatments and techniques faster than we would’ve thought possible just a decade ago,” a Johnson & Johnson spokesperson said in a statement.
“It’s an exciting time to be in this field, too. The healthcare AI market is growing rapidly, and it's creating rewarding and lucrative careers,” they continued.
Best Use Cases for AI
AI and machine learning play a critical role in the pharmaceutical industry. But the best use cases for these technologies are drug discovery, drug manufacturing, diagnostic assistance, and optimizing medical treatment processes, according to industry stakeholders.
Over the years, drug discovery has become increasingly competitive and expensive, which has driven pharmaceutical companies to look into AI as a new method to reduce research and development costs, while avoiding costly errors.
AI has a great potential to transform drug discovery by accelerating the research and development timeline, in an effort to make drugs more affordable and improve the probability of FDA approval.
The tech can also help with the repurposing of new drugs, especially during the COVID-19 pandemic. AI and machine learning algorithms are able to identify molecules that may have failed in clinical trials and predict how the same compounds could be applied to target other diseases.
In drug development and production, AI provides various opportunities to improve processes.
For example, AI can perform quality control, reduce materials waste, improve production reuse, and perform predictive maintenance. Machine learning can help forecast and prevent over-demand and under-demand, as well as fix supply chain problems and failures in the production line.
When a patient is diagnosed, physicians look at their symptoms, diagnostic tests, historic data, and other factors. Based on this information, the physician will provide the patient with personalized treatment options.
AI and machine learning can significantly help with diagnostic assistance by providing a more data-driven approach to patient categorization.
Over the years, FDA has approved dozens of AI platforms for personalized patient care. Some of the platforms were used for remote patient monitoring, while others identified brain bleeding on a CT scan or recognized abnormal heart rhythms on an Apple Watch.
During a medical treatment process, it’s easier to predict an outcome than to suggest a solution to change that outcome.
AI can help optimize the medical treatment process through mobile apps with health measurement and remote monitoring capabilities. The personalized data from the apps can help to improve research and development, as well as treatment efficacy.
Most notably, AI tools significantly help to accelerate cancer diagnosis and treatment.
In mid-April, FDA authorized marketing of the GI Genius, a medical device that uses AI to assist clinicians in detecting signs of colon cancer.
GI Genius is based on machine learning and uses an AI algorithm to highlight portions of the colon where there may be a potential lesion, including polyps or suspected tumors, in real time during a colonoscopy.
The Future of AI in the Pharma Industry
The recent surge in activity in deploying AI capabilities in the pharmaceutical industry shows no sign of slowing down. According to recent research, about 50 percent of global healthcare companies plan to implement AI strategies and broadly adopt the technology by 2025.
Specifically, global pharmaceutical and drug development companies will invest more in discovering new drugs for chronic and oncology diseases.
Chronic diseases are the leading causes of death in the US. Therefore, organizations are increasingly leveraging AI to improve chronic disease management, drive down costs, and enhance patient health.
Some of the major chronic diseases that AI will tackle in the future include chronic kidney disease, diabetes, cancer, and idiopathic pulmonary fibrosis.
AI will also shape the future of pharmaceuticals by improving candidate selection processes for clinical trials. By quickly analyzing patients and identifying the best patients for a given trial, AI helps ensure uptake by providing trial opportunities to the most suitable candidates.
The tech also helps to remove elements that may hinder clinical trials, reducing the need to compensate for those factors with a large trial group.
Organizations will also continue to leverage AI to better screen and diagnose patients. Experts can use AI to extract more valuable information from data that already exists, including in MRI images and mammograms.
AI and machine learning will continue to help further drug discovery and manufacturing. And as AI tools become more accessible over the years, they will become part of the natural process within pharmaceutical and manufacturing. The future will be AI-enabled.
