How AI is reshaping the pharmacy industry | Tangled AI
#AI has been powering tremendous changes in the business world for quite some time now. It is common to see #artificialintelligence in data and research-driven sectors. This is true especially in an industry like pharmaceuticals, where AI has made an astounding impact right from performing clinical trials to accelerating the development of drugs.
The power of data has always ruled the pharmacy industry from day one, and the blending of AI into this sector thus didn’t come out as a great surprise for people. A study points out that #ml models reduce incorrect drug intake by about 50%. This clearly shows the potential of AI and its sub-sectors to make the pharma sector better and safer for the people.
Another survey reveals that 61% of companies adhering to innovations are using AI to identify opportunities that they would have missed otherwise. This is an important statistic to understand, especially for upcoming pharmaceutical companies planning to thrive on innovation and new technologies.
By 2021, companies are expected to be investing about $6.6 billion in adopting AI, with healthcare being the biggest contributor among them. A subset of AI, #deeplearning, is also experiencing an accelerating growth due to its potential for diagnostic uses, for accurately analyzing images using pathological data and historical treatment outcomes.
Identification of new drug candidates
AI is actively used in pharmaceutical industries to identify potential drug candidates and new drug combinations to treat rare diseases. Computational drug discovery platforms constructed using AI and its subsidiaries are not a new thing for most people. It helps to analyze, screen, and discover the drug molecules that serve as a cure for potential diseases.
For instance, twoXAR is an AI-driven pharmaceutical industry that uses AI drug discovery platforms to screen and prioritize potential novel drug candidates that can be used to treat ocular defects. twoXAR has constructed its computational drug discovery platform through the applications of AI, #bigdata, and #cloudcomputing, and it has proved to be faster, reliable, and more effective than conventional methodologies.
Improvement in clinical trials
Data-driven protocols backed by advanced AI algorithms are used to garner information from patients, administration records, and other sources to drastically reduce trial costs and time. AI also utilizes remote-connected technologies by collecting patient data from mobile sensors and applications for clinical trials to prevent patients from traveling long distances. This helps in improving the compliance and retention rate of patients in clinical trials.
An excellent example of AI usage in clinical trials is IBM Watson. Watson uses structured and unstructured information from patient records and medical records, checking patients’ health status and eligibility criteria, thereby narrowing down the process of clinical trials by a considerable margin. Watson uses advanced #NLP and reasoning algorithms to select candidates based on their symptoms and health status.
In 2018, it was reported by Mayo clinic that IBM Watson had assisted them in performing better clinical trials in the right enrollment of eligible candidates and improving the efficacy of clinical trials by 80%.
Predictive analytics in the pharma industry
Gone are the days where the outcomes of the drug administration remain unknown. With the incorporation of predictive analytics in the pharma industry, drugs’ effects are known beforehand and clearly indicate what is likely to happen.
Pharma companies also use predictive analytics to know how well they are doing in comparison to other companies, both commercially and scientifically. This helps them bridge gaps, identify new trends and shifts in the marketplace, and establish new supply chain methods.
Predictive modeling is a part of predictive analytics, and it is still in the experimental stages. Predictive modeling can be incorporated to have a visual understanding of the action of the drug inside the human body. It opens up a pathway to understanding medicine and its action on a particular organ better.
Improved drug adherence
Traditional medical methods are not suitable to build drug adherence in patients. But thanks to AI, drug adherence has been brought to a whole new level. AiCure, a mobile SaaS platform, uses image recognition to keep tabs on patients by monitoring them swallowing the pills. This helps them identify that they have consumed the right prescribed pill to avoid discrepancies and side effects. It’s common knowledge that ingesting the wrong combination of medicines can result in dangerous repercussions and take a toll on the patient’s health. Therefore, AI comes as a savior for hospitals and people by monitoring patients’ drug consuming patterns.
Optimal Marketing
AI analytics also helps to identify best practices before the launch of a new drug. New strategic approaches can be followed to ensure that the drug has a wider reach through the coordination of sales and marketing teams. Big data analytics is used to segment sales teams and analyze sales rates across geographical territories. Automated marketing methods can be streamlined by resorting to smarter email marketing, interactive AI chatbots to attract people, and closing deals with hospitals. All these can significantly improve the marketing standards and enhance the competitiveness in the pharmaceutical industry.
Johnson and Johnson is a renowned pharmaceutical company that uses AI for its marketing activities by automating processes, thereby saving time for mainstream activities.
Final thoughts
AI may be difficult to master or expensive to implement for most companies, but it is necessary for revolutionizing today’s pharmaceutical industry. With the onset of the digital revolution, pharma industries are in constant pursuit of manufacturing innovative products with disruptive technologies, AI being the predominant one. Pharma companies have to invest in AI to be more competitive and avoid any potential problems with their drug usage. From discovering new drugs to monitoring the intake of drugs, pharma industries have to use AI to make the world a better place to live.
