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Machine Learning in Pharmaceutical Industry Market Expected to Reach $26.2 Billion by 2031—Allied Market Research

 
2022
Machine Learning in Pharmaceutical Industry Market

Report Code : A74504

quote Growing demand for Machine Learning can help optimize the pharmaceutical supply chain by predicting demand, identifying potential disruptions, and optimizing inventory levels. By using predictive models to forecast demand and supply, pharmaceutical companies can reduce waste and ensure that drugs are available when and where they are needed, contributing to the machine learning in pharmaceutical industry market growth in the upcoming years. quote

Vitika Verma - Manager
ICT and Media at Allied Market Research

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According to a new report published by Allied Market Research, titled, “Machine Learning in Pharmaceutical Industry Market," The machine learning in pharmaceutical industry market size was valued at $1.2 billion in 2021, and is estimated to reach $26.2 billion by 2031, growing at a CAGR of 37.9% from 2022 to 2031.

Machine learning is a subset of artificial intelligence that involves the use of algorithms and statistical models to enable computer systems to learn from data and make predictions or decisions without being explicitly programmed.

Machine learning is a driving force in the pharmaceutical industry's pursuit of personalized medicine. Personalized medicine involves tailoring treatments to individual patients based on their unique genetic makeup, health history, and other personal factors. Machine learning algorithms can analyze vast amounts of patient data, including genetic data, medical records, and lifestyle factors, to identify patterns and predict how different patients will respond to different treatments. This can help healthcare professionals develop personalized treatment plans that are more effective and have fewer side effects. In addition to improving treatment outcomes for individual patients, machine learning can also help pharmaceutical companies develop more targeted therapies that are more likely to be effective in specific patient populations.

However, the high cost of data collection can be a significant constraint in the use of machine learning in the pharmaceutical industry. The cost of collecting, cleaning, and preparing data can be challenging, and it can be a significant barrier to entry for smaller pharmaceutical companies with limited resources. In addition to the cost, data collection in the pharmaceutical industry can also be complex, as it involves dealing with sensitive patient data, regulatory compliance, and data security. Collecting data from different sources, such as clinical trials, electronic health records, and other sources, can also be challenging.

The machine learning has significant opportunities in the pharmaceutical industry market, especially in the area of predictive analytics. With the large amounts of data available in healthcare, machine learning algorithms can be trained to identify patterns and make predictions about disease outbreaks and individual patient risks. Machine learning can assist in both the identification of patients who are more likely to respond to a particular treatment and the design of clinical trials that are more effective. In addition, using machine learning to forecast drug safety and effectiveness can lower the likelihood of unfavorable events and guarantee patient safety. Owing to these factors, the use of machine learning services and solutions are expected to increase at a rapid pace and offer ample opportunities to the market players.

The machine learning in pharmaceutical industry market share is segmented on the basis of component, enterprise size, deployment, and region. By component, the market is divided into solution and services. By enterprise size, the market is classified into SMEs and large enterprises. By deployment, the market is classified into cloud and on-premise. By region, the market is analyzed across North America, Europe, Asia-Pacific, and LAMEA.

The key players profiled in the machine learning in pharmaceutical industry market report include Cyclica Inc., BioSymetrics Inc., Cloud Pharmaceuticals, Inc., Deep Genomics, Atomwise Inc., Alphabet Inc., NVIDIA Corporation, International Business Machines Corporation, Microsoft Corporation, and IBM.   

The report offers a comprehensive analysis of the global ML in pharmaceutical industry market trends by thoroughly studying different aspects of the market including major segments, market statistics, market dynamics, regional market outlook, investment opportunities, and top players working towards the growth of the market. The report also highlights the present scenario and upcoming trends & developments that are contributing toward the growth of the market. Moreover, restraints and challenges that hold power to obstruct the market growth are also profiled in the report along with the Porter’s five forces analysis of the market to elucidate factors such as competitive landscape, bargaining power of buyers and suppliers, threats of new players, and emergence of substitutes in the market.

Key Findings of the Study

  • Based on component, the solution sub-segment emerged as the global leader in 2021 and the services sub-segment is anticipated to be the fastest growing during the machine learning in pharmaceutical industry market Forecast period
  • Based on enterprise size, the large enterprises sub-segment emerged as the global leader in 2021 and the SMEs sub-segment is predicted to show the fastest growth in the upcoming years
  • Based on deployment, the cloud sub-segment emerged as the global leader in 2021 and is predicted to show the fastest growth in the ML in pharmaceutical industry market upcoming years
  • Based on region, the North America machine learning in pharmaceutical industry market analysis registered the highest market share in 2021 and is projected to maintain its position during the forecast period

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quote Machine Learning in Pharmaceutical Industry Market By Component (Solution, Services), By Enterprise Size (SMEs, Large Enterprises), By Deployment (Cloud, On-premise): Global Opportunity Analysis and Industry Forecast, 2021-2031 quote

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