Visiongain has published a new report on “AI in Pharmaceuticals Market 2021-2031”. Forecasts by Application (Drug Discovery, Precision Medicine, Medical Imaging & Diagnostics, Research), Technology (Machine Learning, Other Technologies), Offering (Hardware, Software, Services), Deployment (Cloud, On-Premises), by Region (North America, Europe, Asia Pacific, Latin America, Middle East & Africa), PLUS COVID-19 Recovery Scenarios.
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Key Questions Answered by this Report
What is the current size of the overall global AI in Pharmaceuticals market? How much will this market be worth from 2021 to 2031?
What are the main drivers and restraints that will shape the overall AI in Pharmaceuticals market over the next ten years?
What are the main segments within the overall AI in Pharmaceuticals 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 that time, and why?
What factors will affect that industry and market over the next ten years?
What are the largest national markets for the world AI in Pharmaceuticals? What is their current status and how will they develop over the next ten years? What are their revenue potentials to 2031?
How will market shares of the leading national markets change by 2031, and which geographical region will lead the market in 2031?
Which are the leading companies and what are their activities, results, developments, and prospects?
What are the main trends that will affect the world AI in Pharmaceuticals market between 2021 and 2031?
What are the main strengths, weaknesses, opportunities, and threats for the market?
How will the global AI in Pharmaceuticals market evolve over the forecasted period, 2021 to 2031?
How will market shares of prominent national markets change from 2021, and which countries will lead the market in 2031, achieving highest revenues and fastest growth?
Embracing Technology to Revolutionize Pharmaceutical Industry
There are other fields where the R&D process can be influenced by AI and machine learning. Better approaches to predict chemicals' properties in order to reduce the number of substances that need to be synthesized is obviously an opportunity. This would allow for the consideration of a larger chemical universe and enrich the 'chemical palette' open to medicinal chemists. Another field where researchers are starting to use AI and machine learning is mining genomic, proteomic, and metabolic data for improved disease biomarkers and medication efficacy surrogate markers.
For example, the time taken to examine cancer tumour scans or neurological condition brain scans may be drastically reduced. Further downstream, AI and deep learning are also used to evaluate real-world result data, wearable device data and other sensors. Predictive toxicology is already in its infancy and, potentially, businesses will still be able to detect any possible safety hazards with compounds even sooner by harnessing the true potential of AI. This advanced technology will also promote predictive medicine and better stratification of patients for clinical trials, and this can lead to an improvement in performance based on greater effectiveness in clinical trials since it is possible to better choose the most desirable patient pool.
What are the Opportunities for AI in Drug Discovery?
AI is seen to give drug development a strategic edge, so it is expected to be introduced easily and generally. The implications of the market adjusting to accept the modern reality will eventually take place, and the strain to embrace the new ways of working will be felt by late adopters for fear of dropping out of the race. They would need to adapt or perish if any larger and more developed players are already lagging.
What is the Competitive Landscape?
Key market players featured in this report include:
Cloud Pharmaceuticals, Inc.
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