The Global Machine Learning in Pharmaceutical Industry Market size is expected to reach $11.4 billion by 2029, rising at a market growth of 34.4% CAGR during the forecast period.
The purpose of machine learning in the pharmaceutical industry is to advance medical knowledge, not to replace a doctor. A physician's whole body of knowledge, which includes everything they acquired in medical school and during their training, in addition to their experience treating patients, is scaled to unprecedented levels by artificial intelligence algorithms.
The ability to obtain and process the vast quantity of data available to doctors—information on new treatments, disease symptoms, drug interactions, and how different patients treated in the same way can have different outcomes—is quickly emerging as a crucial talent. And machine learning makes it possible for them to make inferences from that data and put them into action. For instance, machine learning systems may quickly identify a rare ailment, browse the available treatments, and prescribe by compiling data from many patient visits and thousands of doctors. As a result, time is saved, which leads to increased effectiveness and decreased expenses.
Machine learning can also prevent recidivism by helping to follow up on instances and providing extra recommendations. AI is integrated with electronic medical records. When a doctor uses them irregularly, a pop-up appears explaining how particular genetic features can affect the patient's condition or how a new medication could enhance their health. A doctor can better understand the illness and recommend the best course of treatment by clicking the pop-up.
Not only are these electronic records saving time and space, but they are also actively assisting doctors in formulating better treatment recommendations and educating them on the details in front of them. Some countries with a high lung cancer patient population are beginning to deploy AI programs to help doctors better diagnose lung cancer patients by analyzing X-rays and CT scans and spotting suspicious nodules and lesions.
Machine learning in pharmaceutical industry market, was positively affected by the COVID-19. The utilization of machine learning has been instrumental in the advancement of treatments and vaccines within the pharmaceutical sector. In addition, prospective COVID-19 drug candidates have been found due to the use of ML. Machine learning algorithms can sift through huge amounts of data from genetic databases and clinical trials to identify compounds potentially effective against the virus. This has contributed to speeding the drug discovery process, which ordinarily takes years, and has led to the quick development of many novel COVID-19 medications.
Businesses are utilizing AI and machine learning to provide users with the precise place and date of the upcoming outbreak, like a dengue outbreak, a few months in advance. This program also suggests anti-dengue measures a few hundred meters around the contaminated area. Thus, using machine learning, researchers can foresee the timing and location of impending epidemics, alert the relevant authorities, and inform the general public about it. This capability has the potential to save a significant number of lives, which is expected to increase machine learning's adoption and open up new growth opportunities for the market.
Patient treatment is made simpler and more productive using electronic summaries instead of paper. Future advances in genomes (and the enormous genomics of the symbiotic bacteria) and tailored therapy will greatly increase the amount of information available. As more patient data is gathered, more insights will become accessible. The increased use of machine learning in the pharmaceutical industry is anticipated to drive market growth due to its various benefits, including cost reduction, management, and the collection of massive patient data for future reference.
Harmonizing all the data and performing analytics over the data set is challenging when many data sources are used. Companies that choose a point solution or do not have a robust data analytics system must manually compile analytics reports and insights. Such a procedure takes a lot of time and might not produce any insights with practical business relevance. Thus, the issues associated with data are expected to hinder machine learning in pharmaceutical industry market's expansion.
Based on Component, the machine learning in pharmaceutical industry market is segmented into solution and services. The solution segment held the highest revenue share in the machine learning in pharmaceutical industry market in 2022. This is due to the fact that the pharmaceutical industry produces enormous amounts of data when creating and discovering new medicines. ML algorithms can process and analyze this data to find patterns, connections, and insights that can guide drug development decisions. The demand for machine learning solutions in the pharmaceutical industry is further increased by the desire for quicker and more affordable drug research and development processes.
On the basis Organization size, the machine learning in pharmaceutical industry market is divided into SMEs and large enterprises. The large enterprises segment witnessed the largest revenue share in the machine learning in pharmaceutical industry market in 2022. This is because large pharmaceutical corporations can use machine learning technology to evaluate enormous volumes of data from numerous sources, including electronic health records, clinical trials, and genetic information, to find prospective drug targets, forecast patient outcomes, and improve clinical trial design.
By deployment mode, the machine learning in pharmaceutical industry market is classified into cloud and on-premise. The on-premise segment garnered a prominent revenue share in the machine learning in pharmaceutical industry market in 2022. This is because on-premise services can save more capital than cloud services, as the use and distribution of data can be CPU/GPU intensive, making it expensive to maintain an ML process in a public cloud on a pay-as-you-go basis. The data set might need to be bigger to migrate to the public cloud, adding complexity and cost.
| Report Attribute | Details |
|---|---|
| Market size value in 2022 | USD 1.5 Billion |
| Market size forecast in 2029 | USD 11.4 Billion |
| Base Year | 2022 |
| Historical Period | 2019 to 2021 |
| Forecast Period | 2023 to 2029 |
| Revenue Growth Rate | CAGR of 34.4% from 2023 to 2029 |
| Number of Pages | 200 |
| Number of Table | 322 |
| Report coverage | Market Trends, Revenue Estimation and Forecast, Segmentation Analysis, Regional and Country Breakdown, Competitive Landscape, Companies Strategic Developments, Company Profiling |
| Segments covered | Offering, Organization size, Deployment Mode, Region |
| Country scope | US, Canada, Mexico, Germany, UK, France, Russia, Spain, Italy, China, Japan, India, South Korea, Singapore, Malaysia, Brazil, Argentina, UAE, Saudi Arabia, South Africa, Nigeria |
| Growth Drivers |
|
| Restraints |
|
Region-wise, the machine learning in pharmaceutical industry market is analyzed across North America, Europe, Asia Pacific, and LAMEA. The North America region led the machine learning in pharmaceutical industry market by generating the maximum revenue share in 2022. With a strong emphasis on R&D, the pharmaceutical business in North America makes a considerable contribution to the market. The market has adopted machine learning in recent years to spur innovation, boost productivity, and quicken medication discovery and development.
Free Valuable Insights: Global Machine Learning in Pharmaceutical Industry Market size to reach USD 11.4 Billion by 2029
The major strategies followed by the market participants are Partnerships. Based on the Analysis presented in the Cardinal matrix; Microsoft Corporation and Google LLC are the forerunners in the Machine Learning in Pharmaceutical Industry Market. Companies such as NVIDIA Corporation, IBM Corporation and Cyclica, Inc. are some of the key innovators in Machine Learning in Pharmaceutical Industry Market.
The market research report covers the analysis of key stake holders of the market. Key companies profiled in the report include Google LLC (Alphabet, Inc.), NVIDIA Corporation, IBM Corporation, Microsoft Corporation, Cyclica, Inc., BioSymetrics Inc., Cloud Pharmaceuticals, Inc., Deep Genomics Incorporated and Atomwise, Inc.
By Component
By Deployment Mode
By Organization size
By Geography
The Market size is projected to reach USD 11.4 billion by 2029.
Predicting epidemic beforehand are driving the Market in coming years, however, Inconsistency of data restraints the growth of the Market.
AGoogle LLC (Alphabet, Inc.), NVIDIA Corporation, IBM Corporation, Microsoft Corporation, Cyclica, Inc., BioSymetrics Inc., Cloud Pharmaceuticals, Inc., Deep Genomics Incorporated and Atomwise, Inc.
The expected CAGR of this Market is 34.4% from 2023 to 2029.
The Cloud segment acquired maximum revenue share in the Global Machine Learning in Pharmaceutical Industry Market by Deployment Mode in 2022 thereby, achieving a market value of $7.7 billion by 2029.
The North America market dominated the Market by Region in 2022, and would continue to be a dominant market till 2029; thereby, achieving a market value of $4.5 billion by 2029.
Our team of dedicated experts can provide you with attractive expansion opportunities for your business.
Unique Offerings 