Global Machine Learning in Pharmaceutical Industry Market Report 2023: Increasing use of Technologies in the Medical Industry Drives Growth

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Dublin, June 06, 2023 (GLOBE NEWSWIRE) -- The "Global Machine Learning in Pharmaceutical Industry Market Size, Share & Industry Trends Analysis Report By Component (Solution and Services), By Deployment Mode (Cloud and On-premise), By Organization size, By Regional Outlook and Forecast, 2023-2029" report has been added to ResearchAndMarkets.com's offering.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.

Key Market Players

Google LLC (Alphabet, Inc.)

NVIDIA Corporation

IBM Corporation

Microsoft Corporation

Cyclica, Inc.

BioSymetrics Inc.

Cloud Pharmaceuticals, Inc.

Deep Genomics Incorporated

Atomwise, Inc.

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.Market Growth Factors

Predicting epidemic beforehandBusinesses 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.Increasing use of technologies in the medical industryPatient 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.Market Restraining Factors

Inconsistency of dataHarmonizing 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.

Scope of the Study

By Component

Solution

Services

By Deployment Mode

Cloud

On premise

By Organization size

Large Enterprises

SMEs

For more information about this report visit https://www.researchandmarkets.com/r/7daeu5

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