A variety of interventions, from patient identification and record-keeping during clinical trials to drug discovery and reuse applications, will revolutionize drug discovery and development settings by gradually incorporating artificial intelligence.
The Global Artificial Intelligence in Medicines market is estimated to reach $18,119 million from $719 million in 2017 at a CAGR of 49.6% from 2018 to 2025. Artificial intelligence (AI) in medicine is used to analyze complex medical data by approximating human cognition with the help of algorithms and software. The development of novel pharmaceuticals and biologicals through clinical trials can take more than a decade and cost billions of dollars during that tenure period. AI technology can help to make this process faster and cheaper, which in turn is expected to help improve the efficiency of pharmaceutical and biotechnology companies.
The key players operating in the global artificial intelligence in medicine market include Welltok, Inc., Intel Corporation, Nvidia Corporation, Google Inc., IBM Corporation, Microsoft Corporation, General Vision, Inc., Enlitic, Inc., BioXcel Corporation, and Berg Health,The key players operating in the global artificial intelligence in medicine market include Welltok, Inc., Intel Corporation, Nvidia Corporation, Google Inc., IBM Corporation, Microsoft Corporation, General Vision, Inc., Enlitic, Inc., BioXcel Corporation, and Berg Health.
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In the field of medicine, artificial intelligence (AI) can be used for research studies and applications that support decision-based medical tasks using data-intensive computer-based solutions. For every drug that is launched in the market, the pharmaceutical industry spends on an average $2.7 billion, which amortizes the losses incurred from the aborted pipelines. Almost 90% of all drug programs that reach clinical trial stages are aborted. Thus, many large pharma and academic research institutions are focusing on use of AI to increase efficiencies and design better drugs.
The major factors that drive the growth of the AI in medicine market include shortage of skilled healthcare professionals, the growth in importance of precision medicine, and increase in the processing power of AI systems that is estimated to help improve the efficiency of drug discovery and management of clinical trials. However, limited acceptance from healthcare professionals and limitations of AI decision-making limit the market growth. Conversely, the untapped market potential in developing regions such as India and China is anticipated to provide numerous opportunities for the market growth during the forecast period.
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Based on region, North America occupied the largest share in 2017 owing to availability of capital investments for use of AI in drug discovery and increase in adoption of AI technology by the pharmaceutical and biotechnology companies. However, Asia-Pacific is expected to grow at the highest CAGR of 55.0% during the study period, due to growth in healthcare expenditure, rise in the R&D activities for advancement in AI technology, lack of healthcare professionals, and increase in need to reduce the healthcare costs.
Key Findings of the Artificial Intelligence in Medicine Market:
- The personalized medicine segment is projected to grow at the highest rate among the application segment during the analysis period.
- North America dominated global artificial intelligence in medicine market in 2017 and is projected to continue its dominance in future.
- China is expected to grow at the highest rate in Asia-Pacific during the forecast period.
- Software is the largest contributor among the product type segments in 2017.
- The natural language processing segment generated the highest revenue and is expected to continue its dominance in future.
Among the technology segments, the natural language processing segment accounted for the highest share in 2017 and is expected to dominate the market from 2017 to 2023. The deep learning segment is projected to grow at the highest CAGR during the forecast period, owing to increase in use of signal reduction, data mining, and image recognition, which are integral components of most AI protocols.
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