How the Pharma Industry is Adapting with AI and Machine Learning

on November 23 | in Pharmaceuticals | by | with No Comments

Artificial intelligence is also known as AI and machine learning have evolved to the point where people are considering the benefits of their real-life applications. Due to the ability to work accurately and reduce errors and mistakes, the pharma industry is learning to make use of and adapt AI and machine learning for a wide array of functions.

The following are some major ways that the pharma industry is doing so:

Diagnosis and Disease Identification

One of the top areas where machine learning is being applied to is disease identification. Proper diagnosis of ailments is necessary and the major step in the treatment of any ailment. It is important to note that improper diagnosis in ailments can be costly for the pharma industry and the patients.

Moreover, for ailments such as aggressive cancer, timely diagnosis can make a huge difference in the recovery time and treatment of the person. In 2016, IBM Watson Genomics was launched for this very reason. It paved the way for other AI and machine learning other programs, including Google’s DeepMind Health to diagnose macular degeneration in eyes and Oxford’s P1vital® Predicting Response to Depression Treatment (PReDicT).

Personalized Treatments

Individual health requirements of a person can make treatments complicated and different for each person, even when they have the same ailment. Factors such as age, weight, gender and even the genetic disposition of a person can require a treatment plan that is modified in accordance with their needs.

Another program, IBM Watson Oncology makes use of AI and machine learning in order to facilitate treatment decisions, based on history, medical information and other factors. This removes the difficulties faced in switching treatment plans and also ensures the accuracy to a higher degree.

Discovery of Drugs and Manufacturing

Machine learning and AI are proving to be invaluable for drug discovery, particularly during the early stages. Precision medicine also relies on this aspect, eliminating much of the guesswork that is involved during this time. It also enhances the ability to find and use alternative forms of therapy to pick and choose the best one.

The major programs focusing on this are MIT Clinical Machine Learning Group, who is focusing on the use of efficient algorithms that enhance disease processing and effective plans for treatment of diseases such as Diabetes (Type2). Similarly, Microsoft’s Project Hanover collaborates with other institutions to apply machine learning for precision treatment of cancer, including AML – Acute Myeloid Leukemia.

Clinical Trial and Research

Clinical trial research is invaluable for new treatments, including medication but the process is time extensive and it can require a lot of trial and error. With machine learning and AI, pharma companies can rely on predictive analysis that can help them find the perfect candidates focusing on core components such as age, gender, medical history, and genetic disposition. It allows for smaller focus groups but that can garner better results.

This factor also cuts down on expenses associated with clinical trial and research. Machine learning is also being utilized for monitoring the real-time effects of medication on participants, reducing accidents occurring during a clinical trial. This also gives vital information in identifying problems that can be improved upon to produce a better medical product.

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