AI appears set to be the thing that separates the next generation of business success stories and market dropouts. It has revolutionized the transportation industry by bringing the science fiction dream of autonomous cars into reality, as driverless taxis have already been tested and deployed in the U.S.

Further indicators of its imports come from finance companies like Goldman Sachs, JPMorgan, and Morgan Stanley — all of which have aggressively expanded their data and tech teams over the past year — looking to deploy AI projects that will give them the competitive edge against their rivals. The application of this technology ranges from the mundane to the absurd, seemingly with no sector able to escape its influence — and the pharma industry is no different.

From personal experience, having spent nearly three decades working in the technology industry, and from many conversations with my better half, a longtime pharmaceutical research professional in the therapeutics and drug discovery sector, there’s no question the opportunity for AI in pharma is immense. Some of the industry’s giants have already started to take the plunge and implement AI strategies for an array of different purposes, setting the stage for industry transformation.

Clinical Trials Selection

Delivering a drug to market is a costly process. In 2017, research from the Tufts Center for the Study of Drug Development stated that the average cost was $2.7 billion, and this total has only increased since. Clinical trials account for a large part of this development cost, but despite the high levels of investment, there are often delays and high failure rates. A study from CB Insights suggests that the highest cause of delay in clinical trials comes from the recruitment process, with 80% of trials failing to meet enrollment deadlines.

The use of AI technology such as IBM’s Watson has enabled clinicians to find suitable patients for clinical trials faster and more effectively than using conventional techniques. In this instance, AI can not only save pharma companies time by expediting the selection process but also money by ensuring the right candidates are selected, thus reducing the number of potentially failed trials.

Drug Adherence

An important part of clinical trials is protocol compliance. In short, if the volunteers fail to follow the rules of the trial, then their data will have to be removed from the set; otherwise, if left undetected, it can incorrectly skew the results. Ensuring that participants are taking the right drugs at the right time is essential in safeguarding the accuracy of the results.

AI-powered facial recognition technology has been used for a range of different applications, from providing entertaining filters on the messaging app Snapchat to new security possibilities by giving people unique biometric “keys.”

SaaS platforms such as AiCure have taken this technology and applied it to the problem of drug adherence. Through the use of facial recognition, the platform can tell if someone has taken a pill and if it is the right person. By using the platform, participants demonstrated 90% adherence.

Rare Diseases

The rules of the free market push large pharma companies to develop drugs that will serve the largest groups of people, and as a result, developing treatments for rare diseases has long been a lower priority, as it is not as cost-effective.

The Food and Drug Administration in the U.S. and the European Medicines Agency (EMA) in Europe offer incentives for this development, but AI is also helping to combat this threat. Startups in the U.S. are already applying machine learning algorithms that integrate massive amounts of data from a range of data sources, including clinical trials, patent logs, and other scientific data and literature to repurpose existing drugs and apply them to tackle lesser-known diseases.

In a recent study by the University of Bonn, scientists discovered that neural networks can automatically compare portrait photos to diagnose rare diseases more efficiently and reliably. The software can detect distinct facial characteristics associated with certain diseases from a photo, combine this information with other genetic and patient data, and zero in on the disease most likely to be involved.

Corporate Espionage And Hacktivism

With the cost of drug development so high, the intellectual property that this industry produces is incredibly valuable. So it is no surprise that according to research from NTT Security earlier this year, the healthcare sector moved into the top five industries experiencing the most cyberattacks.

For the first time, the study found the most common attack type was a reconnaissance-based attack, accounting for 44% of total attacks against the health industry. In fact, following EpiPen’s price hike in 2016, a group of hackers stole patent data, enabling them to create cheaper homemade versions of the device, and shared “how-to” videos online.

Cybersecurity is a growing issue for all industries, with the threat being further compounded in the health industry as hackers’ attention is drawn toward the valuable patents and designs that they can monetize.

In response, AI has been deployed across the cybersecurity industry to help combat this impending threat. The aforementioned biometric security such as facial recognition can be used to help secure sensitive data in the pharma industry, while AI-powered algorithms are enabling security researchers to detect threats from anywhere in the world in real-time by integrating massive amounts of security data.

Conclusion

The pharma industry has begun its first steps toward the implementation of large-scale AI solutions, and with an industry as large as pharma, the range of potential applications is immense.

To enable pharma to make this massive change, the industry needs to adopt the right technologies and tools that will let it effectively (and in a timely fashion) capture, integrate, analyze, and interpret the many and ever-growing datasets. Everything from developing and trialing new drugs and therapies to keeping these patents and designs secure can help to transform this long-established and ever-more-critical industry.

Source :  https://www.forbes.com/sites/forbestechcouncil/2020/01/27/ai-pharmas-perfect-medicine/#4f9335c217ad