1.Manufacturing process improvement
In development and production, AI provides numerous opportunities to improve processes. AI can perform quality control, shorten design time, reduce materials waste, improve production reuse, perform predictive maintenance, and more.
AI can be used in many ways to make production more efficient with faster output and less waste. For example, a process that typically relies on human intervention to input or manage process data can be done using CNC (computer numerical control). The AI machine learning algorithms not only ensure tasks are performed very precisely, but also analyze the process to find areas where it can be streamlined. This results in less material waste, faster production, and more consistently meeting the product’s Critical Quality Attributes (CQAs).

2.Drug discovery and design
From designing new molecules to identifying novel biological targets, AI is playing a role in drug target identification and validation; target-based, phenotypic, as well as multi-target drug discoveries; drug repurposing; and biomarker identification. The key benefit for pharma companies is the potential for AI, especially when implemented during drug trials, to reduce the time it takes a drug to get approval and reach the market. This can result it great cost savings, which could mean lower cost drugs for patients, as well as more treatment choices.
For example, pharma researchers can identify and validate novel cancer drug targets using data such as longitudinal electronic medical records (EMR records), next generation sequencing, and other ‘omic data are used to create representative models of individual patients.
Read more: How to optimize cell culture media to speed biopharma development

3.Processing biomedical and clinical data
Perhaps the most developed use of AI so far is in algorithms designed to read, group and interpret large volumes of textual data. This can be a big time-saver for researchers in the life sciences industry, providing a more efficient way to examine the enormous amounts of data from the growing volume of research publications in order to validate or discard hypotheses. Furthermore, many clinical studies still rely on paper diaries in which patients log when they took a drug, what other medications they took, and any adverse reactions they had. Everything from handwritten notes and test results to environmental factors and imaging scans can be collected and interpreted by AI. The benefits of using AI in this way include faster research and cross-referencing of data, as well as combining and extracting data into usable formats for analysis. A Cognizant study showed that around 80% of clinical trials fail to meet enrolment timelines, and one-third of all Phase III clinical study terminations are due to enrolment difficulties.

4.Rare diseases and personalized medicine
Combing information from body scans, patient biology and analytics, AI is being used in various ways to detect diseases such as cancer, and even predict health issues people might face based on their genetics. One example is the IBM Watson for Oncology, which uses each patient’s medical information and history to recommend a personalized treatment plan.
AI is also being used to develop personalized drug treatments based on an individual’s test results, reactions to past drugs and historical patient data for drug reactions.

5.Identifying clinical trial candidates
Besides helping to make sense of clinical trial data, another use of artificial intelligence in the pharmaceutical industry is finding patients to participate in the trials. Using advanced predictive analytics, AI can analyze genetic information to identify the appropriate patient population for a trial, and determine the optimal sample size. Some AI technology can read free-form text that patients enter into clinical trial applications, as well as unstructured data such as doctor’s notes and intake documents.

6.Predicting treatment results
Among the more time- and cost-saving applications of artificial intelligence, is the ability to match drug interventions with individual patients, reducing work that previously involved trial and error. Machine learning models are capable of predicting a patient’s response to possible drug treatments by inferring potential relationships among factors that might be affecting the results, such as the body’s ability to absorb the compounds, the distribution of those compounds around the body, and a person’s metabolism.

7.Predictive biomarkers
Development of biomarkers is an important task not only in the context of medical diagnostics, but also for the process of drug discovery and development. For example, predictive biomarkers are used to identify potential responders to a molecular targeted therapy before the drug is tested in humans. In this process, AI uses biomarker models that are “trained” using large datasets.

8.Drug repurposing
For budget-pressed pharma companies, repurposing drugs promises to be one of the most immediate areas that AI-based technologies can deliver great value. Repurposing previously known drugs or late-stage drug candidates towards new therapeutic areas is a desired strategy for many biopharmaceutical companies as it presents less risk of unexpected toxicity or side effects in human trials, and, likely, less R&D spend.

9.Drug adherence and dosage
Ensuring compliance to a drug study protocol by voluntary participants in clinical studies is a huge problem for pharma companies. If patients in a drug study don’t follow the trial rules, they must either be removed from the study or risk corrupting the drug study results. One of the important factors of a successful drug trial is ensuring that participants take the required dosage of the studied drug at the prescribed times. That’s why having a way to ensure drug adherence is so important. Both through remote monitoring and algorithms for evaluating test results, AI can sort the good apples from the bad.

10. R&D
Pharma companies around the world are leveraging advanced ML algorithms and AIpowered tools to streamline the drug discovery process. These intelligent tools are designed to identify intricate patterns in large datasets, and hence, they can be used to solve challenges associated with complicated biological networks. This capability is excellent for studying the patterns of various diseases and recognizing which drug compositions would be best suited for treating specific traits of a particular disease. Pharma companies can accordingly invest in the R&D of such drugs that have the highest chances of successfully treating a disease or medical condition.

11. Drug Development
AI holds the potential to improve the R&D process. From designing and identifying new molecules to target-based drug validation and discoveries, AI can do it all. According to an MIT study, only 13.8% of drugs are successful in passing clinical trials. To top that, a pharma company has to pay anywhere between US$ 161 million to US$ 2 billion for a drug to get through the complete process of clinical trial and get FDA approval. These are the two main reasons why pharma companies are increasingly adopting AI to improve the success rates of new drugs, create more affordable drugs ad therapies, and, most importantly, reduce operational costs.

12. Diagnosis
Doctors can use advanced Machine Learning systems to collect, process, and analyze vast volumes of patients’ healthcare data. Healthcare providers around the world are using ML technology to store sensitive patient data securely in the cloud or a centralized storage system. This is known as electronic medical records (EMRs). Doctors can refer to these records as and when they need to understand the impact of a specific genetic trait on a patient’s health or how a particular drug can treat a health condition.
ML systems can use the data stored in EMRs to make real-time predictions for diagnosis purposes and suggest proper treatment to patients. Since ML technologies possess the ability to process and analyze massive amounts of data quickly, they can help quicken the diagnosis process, thereby helping save millions of lives. Recently, in mid-April, the marketing of a medical device named GI Genius that is based on Machine Learning and uses an AI algorithm was authorized by FDA. It is now being utilized by clinicians to detect signs of colon cancer. With the help of this device, you can easily detect portions of the colon, with potential lesions, during the time of colonoscopy.

13. Disease Prevention
Pharma companies can use AI to develop cures for both known diseases like Alzheimer’s and Parkinson’s and rare diseases. Generally, pharmaceutical companies do not spend their time and resources on finding treatments for rare diseases since the ROI is very low compared to the time and cost it takes to develop drugs for treating rare diseases. According to Global Genes, nearly 95% of rare diseases don’t have FDA approved treatments or cures. However, thanks to AI and ML’s innovative abilities, the scenario is rapidly changing for the better.

14. Epidemic prediction
AI and ML are already used by many pharma companies and healthcare providers to monitor and forecast epidemic outbreaks across the globe. These technologies feed on the data gathered from disparate sources in the Web, study the connection of various geological, environmental, and biological factors on the health of the population of different geographical locations, and try to connect the dots between these factors and previous epidemic outbreaks. Such AI/ML models become especially useful for underdeveloped economies that lack the medical infrastructure and financial framework to deal with an epidemic outbreak.
A good example of this AI application is the ML-based Malaria Outbreak Prediction Model that functions as a warning tool predicting any possible malaria outbreak and aid healthcare providers in taking the best course of action to combat it.

15. Remote Monitoring
Remote monitoring is a breakthrough in the pharma and healthcare sectors. Many pharma companies have already developed wearables powered by AI algorithms that can remotely monitor patients suffering from life-threatening diseases. For instance, Tencent Holdings has collaborated with Medopad to develop an AI technology that can remotely monitor patients with Parkinson’s disease and reducing the time taken to perform a motor function assessment from 30 minutes to three minutes. By integrating this AI technology with smartphone apps, it is possible to monitor the opening and closing motions of the hands of a patient from a remote location.
On detecting hand movement, the smartphone camera will capture it to determine the severity of the symptoms (Parkinson’s). The frequency and amplitude of the movement will determine the severity score of the patient’s condition, thereby allowing doctors to change the drugs as well as the drug doses remotely.
In case the conditions become worse demanding a treatment upgrade, the AI will send an alert to the doctor and arrange a checkup. Remote setups like these help eliminate the need to travel back and forth to the doctor’s clinic, saving patients the hassle of traveling and waiting.

16. Manufacturing
Pharma companies can implement AI in the manufacturing process for higher productivity, improved efficiency, and faster production of life-saving drugs. AI can be used to manage and improve all aspects of the manufacturing process, including:
• Quality control
• Predictive maintenance
• Waste reduction
• Design optimization
• Process automation
AI can replace the time-consuming conventional manufacturing techniques, thereby helping pharma companies to launch drugs in the market much faster and at cheaper rates as well. Apart from increasing their ROI substantially by limiting the human intervention in the manufacturing process, AI would also eliminate any scope for human error.

17. Marketing
Given the fact that the pharmaceutical industry is a sales-driven sector, AI can be a handy tool in pharma marketing. With AI, pharma companies can explore and develop unique marketing strategies that promise high revenues and brand awareness. AI can help to map the customer journey, thereby allowing companies to see which marketing technique led visitors to their site (lead conversion) and ultimately pushed the converted visitors to purchase from them. In this way, pharma companies can focus more on those marketing strategies that lead to most conversions and increase revenues.
AI tools can analyze past marketing campaigns and compare the results to identify which campaigns remained the most profitable. This allows companies to design the present marketing campaigns accordingly, while also reducing time and saving money. Furthermore, AI systems can even accurately predict the success or failure rate of marketing campaigns.
Although AI is rapidly finding applications in the pharma industry, the process of transformation is not without challenges. Usually, the current IT infrastructure of most pharma companies is based on legacy systems that aren’t optimized for AI.
Moreover, the integration and adoption of AI demand industry expertise and skills, something that is still not readily available. However, the process of AI adoption in the pharma sector can be made easy by taking these steps:
• Partnering and collaborating with academic institutions that specialize in AI R&D to guide pharma companies with AI adoption.
• Collaborate with companies that specialize in AI-driven medicine discovery to reap the benefits of expert assistance, advanced tools, and industry experience.
• Train R&D and manufacturing teams to use and implement AI tools and techniques in the proper way for optimal productivity.

18.Drug Adherence And Dosage
The use of artificial intelligence in pharmaceutical industry is growing at an unprecedented pace. AI in pharma is now being used to identify the right amounts of drug intake to ensure the safety of drug consumers. It not only helps to monitor the patients during clinical trials but also suggests the right amount of dosage at regular intervals.
Artificial intelligence in pharmaceuticals has led to faster automation in processes and is one of the key factors behind the increasing need for accuracy in this industry. The opportunities available for AI in pharma are unmeasurable and ensure both efficiency and compliance. Furthermore, AI in pharmaceuticals has also unlocked several potential AI jobs for people that come with lucrative salaries and benefits.
Future Of Artificial Intelligence In Pharmacy Researchers indicate that by the year 2025, almost 50% of global healthcare facilities will be implementing this technology in their business operations. Such is the growth of artificial intelligence in pharmacy. Drug developmental companies are expected to invest more in this technology for finding innovative solutions to chronic and oncology diseases. Some of the major chronic diseases that are expected to be tackled by Artificial Intelligence include diabetes, cancer, and chronic kidney diseases.
AI is also expected to improve the current candidate selection processes for clinical trials through faster assessments and identifications of the best patients for a given trial. Experts can harness the power of AI to provide more valuable information from the data provided for their patients. This includes MRI images, and mammograms as well. While all of these will surely revolutionize the whole industry of healthcare, there is an additional benefit of lots of AI jobs that will be available in the future, for people who specialize in this field. With the increasing adaptability of this technology, accessibility to the same will no longer be an issue, and it will slowly and steadily become a part of the natural process within pharmaceuticals and manufacturing.