The pharmaceutical industry has long been dependent on cutting-edge technologies for delivering reliable and safe drugs to the market. The Pharma industry is now using the latest technology of artificial intelligence.

AI has been heavily used by marketing teams across the world. The area of machine learning and artificial intelligence is nothing but spectacular. Some solutions are based on artificial intelligence algorithms and these are finding applications in different areas of the pharma industry.

AI use in the pharma industry has just started gaining strong momentum. There are a growing number of companies that are already using AI-based solutions in the discovery, research, and manufacturing process. Nevertheless, the benefits AI brings to this industry are still limited.

How is Artificial Intelligence Being Applied in the Pharmaceutical Industry?

Artificial industry and machine learning have been critical in the consumer healthcare business and pharmaceutical industry. They have played a crucial role during the pandemic, driven by the race to discover efficacious vaccines for COVID-19.

But do you know at which levels you can apply AI in the pharma industry? Here are the best uses of AI in the Consumer Healthcare and Pharma arena.

1. Disease Identification/Diagnosis:

It ranges from degeneration in the eyes to COVID-19.

2. Personalized Treatment/ Behavioral Modification/ Digital Therapeutics:

This can be used for assisting and identifying individuals for providing an early insight into the condition, like classifying cutaneous skin disorders, gum conditions, and serve as an ancillary tool for improving the diagnostic accuracy of clinicians or improving clinical decisions made by medical doctors or mental health professional.

3. Drug Discovery and Manufacturing:

This helps with the initial screening of drug compounds for predicting the success rate based on biological factors. AI makes it possible to measure DNA and RNA quickly. Next-generation sequencing and precision medicine help with faster recovery of drugs and customized medication for patients.

4. Clinical Trials:

Identifying the right person for the trial based on disease condition and history, and added attributed overlaying with demographics, infection rates, and ethnicity for presenting the maximum impact.

5. Predictive Forecasting:

Predicting an epidemic is one of the primary examples of this. AI and ML technology are being applied for monitoring and predicting a seasonal illness or epidemic outbreak across the world. A predictive forecast is good for planning a supply chain for getting the inventory at the right quantity at the right time on predicted intensity.

Implementing AI in the above-mentioned areas is not an easy task. It needs proper planning, the right set of resources, and a robust approach to implement things step by step. It doesn’t happen overnight and companies spend years to leverage AI in applications of the Pharma industry. Let’s find out the companies which are already leveraging AI in pharma.

Who is Leveraging AI in Pharma?

It shouldn’t come as a surprise that the technological arms race leaders are the top players in the pharmaceutical industry that have the budget to invest money in machine learning and artificial intelligence solutions.

Almost every pharma company uses some form of AI or big data solutions for promoting the R&D arena. AI in the pharma industry can be noticed in the following companies.

1. Pfizer:

It uses IBM Watson. This is the system using big data analysis and AI to power its new drugs for immuno-oncology with a drug discovery platform.

2. Sanofi:

The French pharmaceutical company leverages AI to facilitate its research into metabolic-disease therapies.

3. GlaxoSmithKline:

It is a British pharma giant that invests in AI and machine learning to automate drug discovery.

4. Genentech:

The Company is leveraging the AI system offered by GNS Healthcare, the data analytics company to research and create new cancer treatments.

5. F. Hoffmann-La Roche AG:

It has developed a data-driven medical research platform to leverage deep learning.

6. BenevolentBio:

This is a startup based in London using data from sources, like patents, research papers, patient data, and clinical trials into its AI big data platform for gaining actionable insights for the pharmaceutical industry. The company builds AI tools for pinpointing the relationship between symptoms, genes, tissues, protein, drugs, and species.

However, AI isn’t the domain of the leading pharmaceutical corporations of the world. This technology can help smaller pharmaceutical companies to level the playing field and get an edge in this race. To do so, they need to consult a company specialized in handling AI operations for the Pharma industry.

Let’s try to understand which areas can have promising results after implementing AI in their operations.

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Areas Where AI is Bringing Value to the Pharma Industry

Drug development and discovery is one of the core areas in the pharma industry operation. The most promising results of leveraging AI have been achieved in the following areas.

Drug research is a large business involving large sums of money. It also costs a significant amount of money to develop a medicine that will work. Companies can spend a large amount of money on different candidate therapies that eventually fail along the way or might get stuck somewhere in regulatory approval or trial procedures. AI is the way to develop new and affordable drugs. It further solves the problem and optimizes new processes for developing new drugs.

AI can solve many big pharma problems by offering cheaper, quicker, or more effective drug development.

Unique Cases of AI in the Pharmaceutical Industry

DeepMind has come up with a solution to a critical scientific problem that has stumped researchers for a long time. AlphaFold, a research laboratory, and company used the AI program and showed that it can predict how proteins can fold into 3D shapes. With this discovery, it is now possible for researchers to discover the mechanisms driving a few diseases and paving the way for customized medicines.

Challenges to AI Adoption at Larger Organizations

The primary challenges to AI adoption at bigger organizations are as follows.

Securing the right resource with the right background is quite challenging. For an AI model to work efficiently, a training data set of 2-3 years of historical data is crucial. It is one of the most critical challenges in a big organization due to the acquisitions and merger data not available easily.

Bigger companies are grappling to prove the business value for AI projects. For instance, chatbots can be deployed for more cognitive services. Yet, data adaptability isn’t significant and results in problems proving the value of such an attempt.

Finding the right resource with the appropriate background can be quite challenging. There is a limited data science skilled pool to choose from. Moreover delays in hiring and scaling multiple AI projects are quite a big challenge.

This shows that operationalizing AI in the pharmaceutical industry is not easy as it sounds.

What’s Ahead?

Without data, AI can’t do anything. In the Pharma industry, people have a predefined mindset that data will only be useful if it is at least 90% complete and accurate. Well, this mindset needs to be changed because AI works best when you have lots of data, the accuracy and the depth are not as important. Being quick and dirty will serve your purpose instead of being very very precise with the data. This is because when the data is a little off, AI smart algorithms will compensate for that part by acting a little smarter in itself.

Also, forget that you need at least 20 people and a $10 million funding to start. You can start with small use cases too which are very specific. Start with a few use cases to cover outpatient services, sales, or manufacturing. Pick those use cases, have a brainstorm session, check the feasible studies, and consult company expertise in implementing AI for the healthcare/pharma industry. RESOLVE can help you to actualize your data so that you get improved business outcomes.