GLOBAL ARTIFICIAL INTELLIGENCE (AI) IN PHARMACEUTICAL MARKET SIZE AND SHARE ANALYSIS - GROWTH TRENDS AND FORECASTS (2024-2031)
Global artificial intelligence (AI) in pharmaceutical market is estimated to be valued at USD 1,108.1 Mn in 2024 and is expected to reach USD 7,776.2 Mn by 2031, exhibiting a compound annual growth rate (CAGR) of 32.1% from 2024 to 2031.
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The market is witnessing growth due to rising investment by key pharmaceutical players in AI technologies. AI help pharmaceutical companies in accelerating drug discovery process and precision medicine. Machine learning and deep learning algorithms also aid in analysis of large healthcare and clinical datasets for better understanding of diseases. Furthermore, rising chronic diseases due to changing lifestyle and increasing focus on developing targeted therapies can boosts demand for AI in pharmaceutical industry. Personalization of treatment based on patients' genetic makeup using AI can offer new opportunities for the market players in the near future.
Accelerating Drug Discovery Timeline with AI
The pharmaceutical industry has always been under immense pressure to bring new drugs to the market at a faster pace to cater to growing needs of patients worldwide. However, traditional drug discovery methods, which rely solely on human intellect and experimentation, have proven inefficient to keep up with this demand. Shifting through petabytes of scientific literature and clinical data to identify new drug targets and designing novel molecules often takes years of laborious research. Thus, AI plays a transformational role by augmenting human capabilities with its advanced computational powers and ability to analyze massive volumes of unstructured data. Machine learning and deep learning algorithms are being used to perform in-silico screening of millions of potential drug candidates against known drug targets within hours. Natural language processing models analyze literature to find associations and extract never explored insights, saving significant time spent on manual data scrutiny. AI tools are also assisting in hit-to-lead optimization processes by accurately predicting drug properties and side effects at early stages itself. Pharmaceutical giants have started leveraging these capabilities offered by AI. For example, Bayer partnered with an AI startup to apply machine learning on protein structures for speeding up drug discovery against cancer and cardiovascular diseases. Pfizer collaborated with IBM's Watson to enhance its R&D productivity using cognitive computing. Such strategic AI integrations are demonstrating the potential to slash years off traditional discovery timelines. If this trend continues, AI could become fully embedded in pharma workflows to accelerate every step from target identification to clinical trials.
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Advancements in Specialized Biologics and Targeted Therapies
Rising investments in artificial intelligence by pharmaceutical companies can drive the artificial intelligence (AI) in pharmaceutical market growth. Pharmaceutical giants are increasingly leveraging AI systems to accelerate drug discovery processes and make them more efficient. AI has the potential to analyze huge tranches of medical data more quickly and uncover novel insights that humans may miss. This helps pharmaceutical companies reduce drug discovery costs and time as AI supplements human efforts. For example, many top pharmaceutical companies like Pfizer, Merck, GSK and AstraZeneca have ramped up their AI investments in recent years. As per the data published by the United Nations World Intellectual Property Organization in 2022, pharma patent filings related to AI had increased by over 30% between 2020 to 2021 due to applications in precision medicine and clinical trials. Drug makers are utilizing AI for tasks like analyzing genetic data to develop personalized treatments, improving clinical trial recruitment and monitoring drug safety. AI algorithms can also predict potential side effects of new molecules early in the drug discovery process from huge chemical and biological databases which often saves millions of dollars and years of research if red flagged early. The applications of AI are projected to transform drug discovery, disease screening, treatment recommendation and remote patient monitoring in the next 5 years, according to a 2021 report by World Health Organization. This will likely accelerate the discovery of new cures and boost treatment effectiveness. AI also poses important challenges around data privacy, bias and regulatory compliance that needs careful consideration for realizing its full benefits. Rising investments in this transformational technology can offer immense opportunities for innovation and efficiency gains in pharmaceutical industry with implications for better health outcomes around the world.
Key Takeaways from Analyst:
As drug discovery and clinical trials increasingly rely on analyzing large and complex datasets, AI tools that can shift through molecular libraries and medical records faster than humans are gaining popularity. Pharma companies have started adopting AI/machine learning to streamline drug discovery processes and maximize productivity. The ability of AI to crunch massive amounts of data and identify subtle patterns that humans may miss can revolutionize how new drugs are developed.
North America currently dominates the market due to heavy investments by major market players in the region to develop AI-powered drug discovery platforms. However, Asia Pacific region is expected to witness fastest adoption of AI tools in the pharmaceutical industry due to Chinese and Indian markets. These nations are witnessing rapidly growing R&D expenditure and burgeoning healthcare sectors.
Data security and lack of expertise can hamper the market growth. As AI models are only as good as the data they learn on, ensuring patient privacy and data protection will be paramount for gaining user trust. Pharma firms will also need to invest in reskilling existing labor pools to bridge the skills gap when it comes to training AI models and interpreting results.
Market Challenges: Lack of curated pharma data sets for machine learning
The lack of curated pharma data sets can hamper the artificial intelligence (AI) in the pharmaceutical market growth. Pharmaceutical companies collect vast amounts of data from various stages of drug discovery, clinical trials and post-marketing. However, most of this data resides in silos and is neither interoperable nor standardized. Curating this disorganized data into unified, well-structured formats specifically designed for machine learning applications is extremely challenging. Without comprehensive, high-quality labeled datasets, AI algorithms have limited training data to develop advanced models that can accelerate drug discovery and precision medicine efforts. AI has the power to shift through petabytes of unstructured data to reveal novel insights about diseases, drug targets and therapies. It can also glean subtle patterns that human analysts may miss. but the lack of interoperable, tagged datasets limits the ability of AI models to learn from real-world evidence at scale. As a result, promising AI applications like predictive toxicology, cancer subtyping and personalized treatment recommendations are difficult to implement at an industrial level. This challenge slows the integration of AI into mainstream drug development workstreams. AI-based approaches were only able to cut preclinical testing times by around 10% due to insufficient pharma data access for training. More comprehensive datasets sharing information from diverse sources could help algorithms achieve much larger efficiencies.
Market Opportunities: Adoption of artificial intelligence for target identification and validation
Adoption of artificial intelligence for target identification and validation presents a great opportunity in the global artificial intelligence in pharmaceutical market. AI has the potential to revolutionize drug discovery by helping pharmaceutical companies identify and validate new drug targets more efficiently. Target identification and validation is a crucial but lengthy process that often takes years using traditional research methods. AI technologies like machine learning can help analyze vast amounts of biological and chemical data to pinpoint potential drug targets and their properties much faster. This can significantly accelerate early drug discovery efforts and bring new therapies to patients more quickly. Several pharmaceutical companies have already started exploring how AI can transform target identification. For example, Bristol Myers Squibb partnered with Anthropic to apply self-supervised learning models to biological datasets to propose new targets for diseases like cancer. Many other big pharma companies like AstraZeneca, Pfizer and Janssen have also initiated collaborations applying machine learning to genome sequencing and protein structure data to generate novel target hypotheses. As the use of real-world health data increases, AI is also being used to discover associations and identify potential drug targets based on disease outcomes in patient datasets. Widespread adoption of AI for target identification has the potential to vastly improve drug discovery success rates in the coming years. According to a 2021 report by the United Nations Inter-Agency Task Force on Financing for Development, traditional drug discovery methods currently have a low success rate of around 5%, resulting in high costs for pharmaceutical companies.
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By Deployment Mode - Affordability and Scalability Fuel Growth of Cloud Deployment in Pharmaceutical AI
In terms of deployment mode, cloud segment is estimated to contribute the highest market share of 58.1% in 2024, owing to its affordability and scalability. Pharmaceutical companies, especially startups and small to medium enterprises, are under constant pressure to control costs and maximize returns on investment. Deploying AI solutions on-premise requires large upfront capital for hardware procurement, maintenance of infrastructure, and hiring of IT staff for administration. The cloud model eliminates these expenses by offering AI services on a pay-per-use subscription basis. Companies can rapidly scale up capabilities as their needs evolve without having to make heavy infrastructure investments. Cloud ensures constant updates and upgrades to stay on top of latest developments in AI technology. These advantages have made cloud the preferred choice for deployment of pharmaceutical AI applications across small molecule drug discovery, biologics development, clinical trials, and personalized medicine.
By Technology- Deep Learning Dominates the AI Technology
In terms of technology, deep learning segment is estimated to contribute the highest market share of 42.12% in 2024, owing to its ability to learn directly from large and complex unstructured datasets. Pharmaceutical R&D relies heavily on massive genomic, imaging, chemical, and patient data to drive precision targeting of drug mechanisms and diseases. Traditional AI techniques struggle to extract meaningful insights from such gigantic and unorganized pools of information. Deep learning algorithms uniquely facilitate automated feature engineering to recognize complex patterns in molecular, biological and clinical data directly without human intervention. This self-learning capability makes deep learning extremely well-suited for applications across target identification, compound screening, biomarkers detection, and clinical trial participant recruitment in the pharmaceutical industry. Its dominance will continue as biomedical datasets expand in size and scope with emerging omics technologies and digitization of healthcare.
By Offering - Software Dominates as Pharma AI Moves to Commercialization
In terms of offering, software segment is estimated to contribute the highest market share of 54% in 2024, owing to maturation of pharmaceutical AI into commercially deployed solutions. Early experimental implementations relied more on specialized AI hardware. However, as core algorithms stabilized and regulatory confidence in AI grew, pharma firms favored standalone software tools that can be seamlessly integrated into existing IT infrastructure and regulatory compliance workflows. Software programs offer a more cost-effective option than hardware to scale AI technologies across the clinical and commercial lifecycle. These present a configurable interface for various user functions while seamlessly handling underlying machine learning and data processing tasks. This standalone yet interoperable nature of AI software allows pharma companies to take full control of AI outputs as per their validation and documentation needs. The shift to commercial software is catalyzing wider deployment of AI beyond R&D labs into real-world decision-making in areas like pharmacovigilance and medical affairs.
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North America dominates the the artificial intelligence (AI) in the pharmaceutical market with an estimated market share of 41.2% in 2024. The U.S. is home to many large pharmaceutical companies that have significantly invested in AI technologies. Companies view AI as critical for drug discovery, reducing costs and time to market. Large corporations like Pfizer, Johnson & Johnson, Merck and others have set up dedicated AI divisions and labs with a focus on automating drug discovery processes. Local startups in this field also receive strong funding support, allowing them to contribute innovative solutions. The region has a highly skilled workforce with expertise in domains like computer science, data science and healthcare. This talent pool helps address technical challenges and efficiently deploy AI-powered tools.
Asia Pacific has emerged as the fastest growing market for artificial intelligence (AI) in the pharmaceutical. Countries like China and India offer a low-cost base for global pharmaceutical companies to establish AI research centers. Both nations also have a burgeoning domestic market needing cost-effective drug development capabilities. China's government actively promotes this sector as part of its "Made in China 2025" campaign. Significant financial incentives are provided to attract foreign direct investment. Multiple Sino-American joint ventures have come up for applying deep learning to complex healthcare issues prevalent in Asia. In India, the government aims to increase generic drug manufacturing and digital healthcare through public-private partnerships applying AI.
Global Artificial Intelligence (AI) In Pharmaceutical Market Report Coverage
| Report Coverage | Details | ||
|---|---|---|---|
| Base Year: | 2023 | Market Size in 2024: | US$ 1,108.1 Mn |
| Historical Data for: | 2019 to 2023 | Forecast Period: | 2024 to 2031 |
| Forecast Period 2024 to 2031 CAGR: | 32.1% | 2031 Value Projection: | US$ 7,776.2 Mn |
| Geographies covered: |
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| Companies covered: |
NVIDIA Corporation, IBM Corporation, Exscientia, Insilico Medicine, Atomwise, Inc., Cloud Pharmaceuticals, Inc., Cyclica Inc., Envisagenics, Inc., Numerate, Inc., Schrödinger, Inc., Standigm, Turbine.ai, BenevolentAI, Recursion Pharmaceuticals, Owkin, Inc., XtalPi Inc., Valo Health, Absci |
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| Restraints & Challenges: |
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*Definition: Artificial Intelligence (AI) in the pharmaceutical market refers to the use of advanced machine learning and cognitive technologies to discover new drug candidates, personalize treatment plans and accelerate clinical trials. AI is helping pharmaceutical companies analyze vast amounts of data from research, clinical trials, electronic health records and scientific literature to better understand disease mechanisms and develop more effective and targeted drugs faster. AI has the potential to significantly advance drug discovery and development processes by automating repetitive tasks and revealing insights that may have been difficult for humans to see alone. This can help pharmaceutical companies reduce costs and bring innovative new treatments to patients more quickly.
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About Author
Ghanshyam Shrivastava - With over 20 years of experience in the management consulting and research, Ghanshyam Shrivastava serves as a Principal Consultant, bringing extensive expertise in biologics and biosimilars. His primary expertise lies in areas such as market entry and expansion strategy, competitive intelligence, and strategic transformation across diversified portfolio of various drugs used for different therapeutic category and APIs. He excels at identifying key challenges faced by clients and providing robust solutions to enhance their strategic decision-making capabilities. His comprehensive understanding of the market ensures valuable contributions to research reports and business decisions.
Ghanshyam is a sought-after speaker at industry conferences and contributes to various publications on pharma industry.
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