AI in Pharma Statistics 2024 By Market Size, AI Usage and Facts

Ketaki Joshi
Written by
Ketaki Joshi

Updated · May 28, 2024

Rohan Jambhale
Edited by
Rohan Jambhale

Editor

AI in Pharma Statistics 2024 By Market Size, AI Usage and Facts

Introduction

AI in Pharma Statistics: The pharmaceutical market is rapidly evolving, with the integration of Artificial Intelligence (AI) applications transforming every aspect of pharma processes. From drug discovery to supply chain management, AI is significantly reducing the time required to develop new medicines, shrinking it from 5-6 years to just one year. These advancements not only enhance accuracy and efficiency but also create new opportunities for personalized medicine and innovative therapeutic solutions.

AI-driven predictive modeling and advanced algorithms are improving trial designs, accelerating the trial process, increasing precision and effectiveness, and reducing costs.

Editor’s Choice

  • 60% of professionals in these industries say that using AI helps improve quality control. AI can quickly and accurately analyze large amounts of data, which helps find errors and ensure high standards.
  • Artificial Intelligence in medical care is estimated to save the United States healthcare economy $150 billion annually by 2026.
  • In 2020, the COVID-19 pandemic has increased the worldwide pharmacy market by 17.5%.
  • Pharmaceutical companies also face classic barriers to implementing AI, such as the need for flexible infrastructure to collect, verify, and govern data and run applications at scale.
  • A significant part of this expense is due to the high failure rate of trials—roughly 87.5% of compounds that enter clinical testing never make it to the market.
  • According to estimations between 2019 and 2029, the Market size of AI in the Pharmaceutical Market in 2024 was $3,05 billion, and it is predicted to be $18.06 billion in 2029.
  • Artificial Intelligence is estimated to boost the revenue of the pharmaceutical and medical instruments market by $100 billion by 2030.
  • Between 2020 and 2027, the use of machine learning in drug innovation is estimated to increase at a rate of 52.6%. Current Usage: 80% of professionals in the pharmaceutical and life sciences fields are currently using AI to discover new drugs.
  • 95% of pharmaceutical companies are putting money into building their AI capabilities.
  • AI technology is drastically reducing the time needed for drug discovery. What used to take 5-6 years can now be done in just one year with the help of AI.
  • AI applications have the potential to generate between $350 billion and $410 billion annually for pharmaceutical companies by 2025.
  • Using AI in clinical trials can lead to cost reductions of 70% per trial.
  • AI can shorten the duration of clinical trials by 80%, making the process much quicker.
  • A study found that AI could cut the time needed to develop a new drug by four years.
  • This reduction in development time could save the pharmaceutical industry $26 billion.
  • The use of AI in cancer diagnosis, where pharmaceuticals play a crucial role, is expected to grow rapidly, with a compound annual growth rate (CAGR) of 40.1% from 2021 to 2028.
  • According to a survey, 65% of respondents believe that AI will have the most significant impact on manufacturing and supply chain management within the pharmaceutical industry.
  • The AI market in genomics, which is vital to pharmaceuticals, is projected to grow at an annual rate of 52.7% from 2021 to 2028.

What is AI in the Pharmaceutical Industry?

Generally, technology specialty states three directions:

  • Data science algorithms are automatic algorithms designed to verify past activities and make choices. Based on the patient’s clinical history and medical data, they can provide a more efficient therapy plan or a drug merger.
  • Machine learning algorithms are a very difficult way of making decisions based on impartial network analytics. They work with the available datasets to estimate decision issues and categorize and classify data.
  • Deep learning is related to the more difficult form of the natural language learning process; it is used for more detailed diagnosis.

artificial-intelligence-in-pharma-industry

(Source: viseven.com)

General AI in Pharma Statistics

  • Artificial Intelligence has had a market size of almost $692.0 million in the pharma industry in recent years.
  • It is estimated that Artificial Intelligence will lessen the drug discovery timeline by four years.
  • Almost 95% of the pharma industries have invested in Artificial Intelligence technology to some extent.
  • The COVID-19 pandemic has hastened the adoption of Artificial intelligence in the pharmaceutical market, with almost 54% of healthcare companies fast-tracking their digital transformation plans.
  • The use of AI for early prognosis and diagnosis estimation could reduce mortality by almost 30% to 40%.
  • It is predicted that Artificial Intelligence can save the pharmaceutical market up to $100 billion annually.
  • The use of machine learning in drug innovation is estimated to increase by 52.6% between 2020 and 2027.

Artificial-Intelligence-AI-in-Pharmaceutical-Market

(Source: scilife.io)

  • The above chart shows the size of the AI market in the Pharmaceutical Industry at a 42.68% CAGR.
  • According to estimations between 2019 and 2029, the Market size of AI in the Pharmaceutical Market in 2024 was $3,05 billion, and it is predicted to be $18.06 billion in 2029.
  • By 2027, the worldwide Artificial Intelligence in the pharmaceutical industry size is estimated to reach almost $5.2 billion.
  • Artificial Intelligence has resulted in finding 25 new medicines as of November 2020.
  • According to AI in Pharma Statistics, around 40% of the employees in the pharmaceutical market think that Artificial Intelligence will be ordinary in research and development in two years.
  • Artificial Intelligence in medical care is estimated to save the United States healthcare economy $150 billion annually by 2026.
  • Artificial Intelligence has been successful in associating new drugs almost 25% of the time, compared to the present market rate of 0.01%.
  • Around 84% of professionals think investing in Artificial Intelligence will result in greater competitive benefits.
  • Artificial Intelligence is estimated to boost the revenue of the pharmaceutical and medical instruments market by $100 billion by 2030.
  • The TechEmergence survey states that almost 35% of pharmaceutical organizations are already using machine learning technology.
  • By 2026, the size of artificial Intelligence in the pharma industry is predicted to exceed $4.3 billion.
  • By 2024, Artificial Intelligence in drug innovation will be worth more than $20 billion.
  • By 2024, the AI industry in drug innovation is projected to achieve a 40.8% CAGR.

Top Pharmaceutical AI Companies

Several organizations worldwide are working on Artificial Intelligence in pharma, from drug innovation and pre-clinical to clinic and even after-market solutions.

Pharma AI Company Location Summary
Recursion United States of America Recursion takes the in-silico AI projection from AlphaFold and examines it experimentally.
Google DeepMind United Kingdom DeepMind first made headlines when AlphsGo defeated the world’s top human Go player, Lee Sedol of South Korea. After AlphaGo’s success, DeepMind’s founder, Demis Hassabis, turned his focus to 3D protein folding and the production of the pharmaceutical AI process AlphaFold, which currently predicts the shape of billions of proteins.
Genesis Therapeutics United States of America Genesis Therapeutics has innovated a generative Artificial Intelligence drug innovation platform that helps pharmaceutical organizations find new medicines against focused targets that were previously difficult for them to target.
Insilico Medicines Hong Kong Insilico uses pharmaceutical AI models to find targets and drug candidates for diseases, such as the drug candidate INS018_055, which can be used against pulmonary fibrosis and is now in Phase 2 trials.
Intro United States of America Insitro trains models on gene sequences, clinical outcomes, and pathology slides. These models can estimate how cancer patients are prone to respond to a specific treatment.
Camino United Kingdom The Camino uses AI generative in med comms, sifting through Pubmed abstracts and papers to find the data about the pharmaceutical industry as competitors for the pharma clients.

Challenges Faced by the Pharma

  • The cost of developing new drugs is incredibly high, with a 2020 study showing that the average research and development (R&D) cost for a new treatment is about $985 million.
  • A significant part of this expense is due to the high failure rate of trials—roughly 87.5% of compounds that enter clinical testing never make it to the market.
  • The Role of AI in Drug Development—AI can significantly speed up the process of gathering and accessing information, drastically reducing the time it takes to develop new drugs and keeping their prices lower.
  • This is increasingly important as the cost of discovering and developing new drugs continues to rise.
  • For instance, AstraZeneca uses machine learning to identify which genes might cause resistance to cancer treatments quickly. Samsung has created an app that uses AI to detect early COVID-19 infection.
  • Smaller Datasets: AI algorithms generally need large datasets to learn effectively. However, the pharma industry deals with numerous diseases, many of which have relatively few cases, making it difficult to gather large datasets. Additionally, mergers and acquisitions can complicate data collection since the original data sources may no longer be accessible.
  • Complex Data: Patient data is highly complex, including past and current health information, treatment histories, lifestyle choices, and genetic data. It also includes biometric data from sensors or wearable devices. This complexity makes it challenging for AI systems to process and analyze the data effectively.

technologies-that-will-have-the-greatest-impact-on-the-pharmaceutical-industry-in-2021

(Reference:europeanpharmaceuticalreview.com)

  • Artificial Intelligence has the highest 36% impact on the pharmaceutical market.
  • Complicated Data Labeling: Labeling data for AI requires highly specialized knowledge, such as identifying abnormalities in X-ray images. This process can be tedious and time-consuming, as each scan may need to be reviewed by multiple doctors, each taking 5 to 15 minutes.
  • Data Bias: Historically, certain population groups have been underrepresented or misrepresented in medical datasets. This can lead to misdiagnoses and poor outcomes. To ensure ethical AI use, machine learning operations (MLOps) processes must monitor and detect biases, and diverse teams should test the models continuously.
  • Lack of Data Standards: There is no universal standard for what constitutes a good dataset in the industry. Different organizations may collect and code data differently, use placeholder data when information is missing, and inconsistently document demographic data. Clear parameters are needed to make datasets more useful for advancing research.
  • Lack of Biological Data: Biological databases are crucial for bioinformatics, offering access to genomic sequences and other biological data. This information helps scientists understand how patients respond to treatments. AI relies on this data to make discoveries and improve drug development.
  • Regulation: The pharma industry is heavily regulated, requiring full transparency at every stage of drug development. These regulations can make AI development in pharma more lengthy and costly. Collaboration with regulators can streamline this process, benefiting both parties.
  • Pharmaceutical companies also face classic barriers to implementing AI, such as the need for flexible infrastructure to collect, verify, and govern data and run applications at scale.
  • Innovations like transfer learning and MLOps platforms can help train models and streamline the process of moving them into production.
  • The growing competition to discover treatments faster will drive the demand for AI to accelerate new drug discoveries.
  • While the challenges of analyzing medical data are becoming more complex, the pace of innovation in AI tools and technologies is increasing, enabling pharma companies to gain insights more quickly and efficiently.

E-Pharma Industry Statistics

  • On average, Chinese e-pharmaceutical consumers visit the websites two times a week and spend almost 6 minutes on every visit.
  • According to the AI in Pharma Statistics, 48% of German online shoppers bought pharmaceutical products online in 2020.
  • Worldwide, almost 55% of internet users have placed an order for pharma products online.

global-epharmacy-market

(Source: sphericalinsights.com)

  • In the above chart, we can see the Global ePharmacy Market prediction from 2023 to 2033.
  • The market size in 2023 is $68.8 billion, and it is estimated to grow to $349.6 billion.
  • The e-pharmacy industry in Brazil is estimated to grow at a 3.8% CAGR from 2020 to 2027.
  • Almost 38% of the users choose e-pharma because of the convenience of delivery at home.
  • In 2020, the COVID-19 pandemic has increased the worldwide pharmacy market by 17.5%.
  • The retail e-commerce sales of counter pharma in the United States were valued at $17.3 billion.
  • The e-commerce sales of pharmaceutical goods in Germany were valued at almost 2 billion euros.
  • The pharmacy method in Australia is predicted to be worth $600 million of the $12.5 billion total prescription industry in 2021.
  • The pharmacy industry’s growth in Asia Pacific is estimated to rise at a 15.6% CAGR between 2019 and 2026, the highest CAGR in the region.
  • In India, almost 96% of the e-pharmacy structures are made illegally.
  • The Chinese online pharmacy industry is estimated to reach 158.4 billion yuan, which is equal to $24.6 billion, in 2025.
  • By 2026, the pharmacy industry is estimated to cross $70 billion in Europe.
  • The Indian pharmacy industry is projected to grow at a 63% CAGR and reach $3.6 billion by 2022.
  • The United States dominated the North American pharmacy industry and was valued at $33,507.5 million.
  • More than half of the ePharmacy value is created from selling OTC drugs.
  • Between 2021 and 2026, the worldwide e-pharmacy industry was projected to increase at a 14.8% CAGR.
  • By 2026, the pharmacy industry is estimated to reach $177,794.9 million.
  • The worldwide ePharmacy industry size was valued at $49,727.7 million.

ai-use-cases-in-the-pharma-and-healthcare-industry-as-of-2020

(Reference: statista.com)

  • According to recent study, Artificial Intelligence (AI) is having a major impact on the pharmaceutical and healthcare industries.
  • Quality Control: 60% of professionals in these industries say that using AI helps improve quality control. AI can quickly and accurately analyze large amounts of data, which helps find errors and ensure high standards.
  • Monitoring and Diagnosis: 42% of respondents highlighted that AI is crucial for monitoring and diagnosing health conditions. AI can assist in diagnosing diseases and choosing the best treatments based on the latest data.
  • Personalized Risk Screenings: AI is also expected to soon provide personalized preventive risk screenings for patients. This means AI could help doctors identify the best options for preventing illnesses based on individual patient data.

In summary, AI is enhancing quality control, aiding in diagnosis, and offering personalized health screenings, making it a valuable tool in the pharmaceutical and healthcare industries.

Conclusion

In the end, AI is reconstructing the pharmaceutical market, which enables development and efficiency. Even though the journey will be difficult because of data privacy and ethical concerns, in addition to the need for specific skills, the potential advantages could be tremendous. The pharma market can reap the full advantages of artificial Intelligence by addressing any difficulties that may develop along the way and harnessing its incredible potential.

In the coming years, this will transform drug development, improve patient outcomes, and lead to a more efficient and innovative healthcare system.

Ketaki Joshi
Ketaki Joshi

Ketaki Joshi is a professional medical writer with extensive experience in scientific research on illness, health, and healthcare. Her work includes creating feature articles for newsletters and websites, as well as research news stories for doctors and researchers. With a lifelong passion for reading, Ketaki transitioned from a career at a French multinational company to pursue writing professionally. Her dedication to the craft has culminated in the recent release of her first Amazon-published short story, "The Envelope That Changed Our Lives."

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