Visiongain has published a new report on “AI in Pharmaceuticals Market Report 2021-2031”. Forecast by Application (Drug Discovery, Precision Medicine, Medical Imaging and Diagnostics, Research), Technology (Machine Learning, Other Technologies), Supply (Hardware, Software, Services), Deployment (Cloud, On-Premise), by Region (Americas North), Europe, Asia-Pacific, Latin America, Middle East and Africa), MORE COVID-19 Recovery Scenarios.
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Key questions answered by this report
What is the current size of the set Global AI in Pharmaceuticals Market? How much will this market be worth from 2021 to 2031?
What are the major drivers and restraints that will shape the global AI in Pharmaceuticals market over the next ten years?
What are the major segments of the overall AI in Pharmaceutical Market? How much will each of these segments be worth for the period 2021 to 2031? How will the composition of the market change during this period and why?
What factors will affect this industry and market over the next ten years?
What are the largest national markets for the AI in Pharmaceuticals world?
What is their current state and how will they evolve in the next ten years? What are their revenue potentials until 2031?
How will the market shares of major national markets evolve by 2031, and which geographical region will lead the market in 2031?
What are the leading companies and what are their activities, results, developments and prospects?
What are the main trends that will affect the world of AI in the Pharmaceutical market between 2021 and 2031?
What are the main strengths, weaknesses, opportunities and threats of the market?
How will the global AI in pharmaceuticals market develop during the forecast period, 2021 to 2031?
How will the market shares of major national markets evolve from 2021, and which countries will dominate the market in 2031, achieving the highest revenue and the fastest growth?
Embracing technology to revolutionize the pharmaceutical industry
There are other areas where the R&D process can be influenced by AI and machine learning. Better approaches to predict the properties of chemicals to reduce the number of substances that need to be synthesized is obviously an opportunity. This would make it possible to consider a broader chemical universe and to enrich the “chemical palette” open to medicinal chemists. Another area where researchers are beginning to use AI and machine learning is the mining of genomic, proteomic, and metabolic data to improve disease biomarkers and surrogate markers of drug efficacy.
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For example, the time required to review scans of cancerous tumors or brain scans of neurological disorders can be significantly reduced. Further downstream, AI and deep learning are also used to evaluate real-world outcome data, wearable device data, and other sensors. Predictive toxicology is already in its infancy and potentially companies will still be able to detect any potential safety risk with compounds even earlier by harnessing the true potential of AI. This advanced technology will also promote predictive medicine and better patient stratification for clinical trials, which can lead to improved performance based on greater efficiency in clinical trials since it is possible to better choose the pool of most desirable patients.
What are the opportunities for AI in drug discovery?
We see that the AI gives drug development a strategic advantage, so it is expected to be introduced easily and generally. The implications of the market adjusting to accept modern reality will eventually take place, and the pressure to embrace the new ways of working will be felt by late adopters for fear of dropping out of the race. They should adapt or perish if bigger, more developed players are already lagging behind.
What is the competitive landscape?
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