Applications of Artificial Intelligence in Pharma Industry

ai in pharma

Artificial Intelligence is a rapidly growing technology that finds applications and uses cases in all aspects of industry and life as well: within smart factories that use AI technology to enhance their capabilities and in smart assistants found in smart phones.

Similarly, the pharmaceutical industry is finding innovative and smart ways to use this modern technology to resolve some of the significant issues facing pharma sector today. Along with AI-powered analytics, big data has brought a radical shift in the paradigm of the pharma.

Artificial intelligence has the potential to promote innovation, while at the same time increasing productivity and providing better results. In addition, Artificial Intelligence develops the value proposition of pharmaceutical companies by creating new and latest business models.

You can observe AI implementation in almost every aspect of the pharmaceutical field, from drug discovery and development to drug manufacturing, supply chain and marketing. By implementing and leveraging AI systems in the workflow, pharma companies can perform all business operations cost-effectively, efficiently, and hassle-free.

Here, I’ll be giving an overview of the top 10 AI applications in the pharmaceutical sectors. So, let’s look at the

The Best Applications of Artificial Intelligence in Pharmaceutical Industry

#1 Drug Discovery Process and Design

From making small molecules to determining novel biological targets, AI plays a prominent role in drug target identification and validation; phenotypic, target oriented, and as multi-target drug innovation; biomarker identification; and rep Shadha restart.

A major benefit of the pharma industry is that when AI is administered during drug testing, it minimizes the time it takes for a drug to be approved and reach the global market to purchase. This leads to cost-cutting, which means lower cost medications for patients care without side effects and more treatment options.

For example, researchers in pharmaceutical can identify and verify novel cancer drug targets using data such as longitudinal EMR records (Electronic Medical Records) and other ‘omic data’.

#2 R&D

Pharma companies across the globe are developing advanced AI-powered tools and ML algorithms to smoothen drug innovation process. These technology tools are designed to detect complex patterns in large datasets and, therefore, can be used to resolve problems associated with complex biological networks.

This ability to study patterns of various diseases and to determine which composite formulations are best suited for the treatment of specific symptoms of a particular disease is excellent. Pharma industries can invest in the R&D of such drugs that are more likely to treat a disease or medical condition successfully.

#3 Disease Prevention

Pharmaceutical organizations can use Artificial intelligence to develop cures for both Parkinson’s and Alzheimer’s and for very rare diseases. In general, pharmacy companies don’t spend resources and time to find medicine for an early stage of rare diseases because ROI (rate of interest) is less compared to the cost and time it takes to develop a drug for rare diseases.

It is a fact that almost 95% of rare diseases do not have FDA approved cures or treatments, as per Global Genes. However, thanks to the innovative capabilities of AI and ML, the scenario is changing rapidly for the better.

#4 Diagnosis 

Physicians can use advanced machine learning systems to gather, process, and analyze patient health care data. Healthcare professionals across the globe are using deep learning and ML to securely store patient data in the centralized storage system or cloud. This is called Electronic Medical Records (EMR).

Physicians may refer to these health records when they need to understand the effect of a specific genetic trait on a patient’s health or how medicine treats. Machine Learning systems can use data stored in EMRs to generate real-time estimates for diagnostic purposes and to indicate appropriate treatment for the patient.

As ML technologies are capable of processing and analyzing large amounts of data quickly, they can help speed up the diagnostic process, thereby saving millions of lives.

#5 Epidemic Prediction

Pharma companies and healthcare industries are using ML and AI technologies to monitor and assess the spread of infections worldwide. These modern technologies consume data collected from unequal resources on the web, study the connection of several environmental, biological, and geographical factors on the population health of diverse geographical regions and attempt to connect the dots between these factors and the prevalence of previous epidemics.

Such Artificial intelligence and machine learning models are particularly beneficial for underdeveloped economies that lack medical infrastructure and financial framework to combat the spread of infection.

A good example of this is the ML-based malaria outbreak prediction model, which serves as a warning tool for malaria outbreaks and helps health care providers take the best action to combat it.

#6 Identifying Clinical Trial Candidates    

AI not helps understand clinical trial data, but also helps the pharmaceutical industry to find patients to participate in clinical trials. AI can analyze genetic data to determine the appropriate patient population for a clinical trial and identify the appropriate sample size.

Some AI technology allows patients to read the free-form text as they enter structured data and clinical trial applications like intake documents and doctor notes.

#7 Drug Adherences and Dosage

It is a big problem for pharma companies to ensure that volunteers in clinical studies comply with the drug study protocol. Patients in a study should be excluded from the study if they do not follow the rules of the study, or there is a risk that the study drug will distort the results.

One of the critical factors in a drug trial is to ensure that participants take the required dose of the drug studied at regular intervals. Hence, it is so important to have a way of sticking to drugs. Through remote monitoring and algorithms to predict test results, AI technology can sort out good apples from bad.

Conclusion

To wrap up, the scope of Artificial intelligence and machine learning in the Pharma industry looks very promising. As growing pharma companies adopt AI and ML, this definitely leads to the democratization of these modern technologies, making it more accessible to small as well as medium-sized businesses.

AI is not only transforming the way the pharma industry is evolving; it’s also offering drug manufacturing companies innovative ways to enhance the brand value.

If you are interested in taking advantage of cutting-edge technologies like Artificial Intelligence, please contact us today.

We will guide you on the right path!

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