Original article was published by Life Sciences Review on Artificial Intelligence on Medium
The Potential For AI In Life Sciences
Life sciences organizations can leverage AI in several ways to drive insightful decisions on all aspects of their business.
FREMONT, CA: The pharma and life sciences industry is faced with mounting regulatory oversight, decreasing R&D productivity, and hurdles to growth and profitability. The regulations are forcing the pharma and life sciences industry to change its status quo. As a result, the life sciences industry is compelled to focus on relatively nascent and evolving technologies like Artificial Intelligence (AI). Infusion of AI in the life sciences industry allows firms to rationalize internal costs and focus on better profiling and targeting clients and medical practitioners. Here are some AI opportunities in the life sciences industry.
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• Supporting Innovation for New Products
The biggest goal of the life sciences industry is to advance research and innovation for new drugs. AI has opened up new ways to help in the innovation of new products. The most widely used method in the industry is to develop sophisticated algorithms that can identify and analyze the patterns and causes of diseases. The data mined by the AI system can be used to predict the reactions of the body in response to the drug.
This can be used to make any necessary changes in the existing medicine to offer better results.
• Improving Diagnostics
AI’s first application was in improving diagnostic techniques. Diagnosis is the most fundamental step in the treatment. If the diagnosis is wrong, then the whole treatment will be wrong, which can be fatal for the patients. AI-based automated diagnosis is faster and better as compared to traditional diagnosis methods. AI systems can employ microscopic magnifications to find disease patterns and cross-reference the gathered data with the patient’s medical history.
• Optimizing Safety Information
The expiry dates and safety periods for medicine are generally decided considering the chemical components, but these analyses are not always very efficient and accurate. The software can effectively analyze the pieces of medicines keeping in mind the several constraints not limited to the chemical factors but also other usage and environmental factors. The machine learning systems can be leveraged to record past medicines usage patterns to decide the future usage patterns of the medicine.