The global artificial intelligence (AI) in life sciences market size was valued at USD 1,622 million in 2021. It is expected to reach USD 12,670 million by 2030, growing at a CAGR of 29.3% during the forecast period (2022-2030).
Artificial intelligence (AI) is identified as a highly data-driven technology. In the life sciences industry, it is generally employed in the R&D capabilities to provide meaningful insights from loosely coupled data. Although AI adoption in the life sciences sector is still in its infancy, early adopters are anticipated to benefit most from it as it will help them develop strategic technological skills for acquiring a competitive edge. Various life sciences companies are raising funds to invest in R&D capabilities and provide AI-integrated solutions and offerings in the market.
Furthermore, AI technology is increasingly making its way into the public domain through mobile healthcare applications. Mobile application, such as Sensely, stands to be an outstanding example of the commercial success of such applications. This scenario may lead to a new domain of artificial intelligence that is more suitable to be deployed on mobile platforms. Although the penetration of AI for voice recognition and image processing is considerably high on mobile platforms, the life sciences sector is expected to benefit immensely from the technology.
A transition is taking place in most industries, including the life sciences, due to rising cost pressure, a growing requirement for productivity, and disruption from new and inventive market competitors. Technology has emerged as the most significant enabler in adjusting the value chains of life sciences firms to match their current business requirements, whether performing cutting-edge research, identifying new pharmaceuticals, or staying ahead in the battle for the latest equipment. Awareness about artificial intelligence (AI), machine learning (ML), and neuro-linguistic programming (NLP) are ever-increasing, with AI being one of the most discussed technologies across the life science domain.
In the current scenario, AI has a little yet developing impression in this industry, with increasing drug discovery and development applications. However, firms are expanding AI applications across the product lifecycle. The life science industry involves a considerable amount of structured and unstructured data, and AI is acting as a resource to manage this data efficiently. Hence, increasing the adoption of AI for research and development contributes significantly to market growth.
AI has a positive impact on drug development and personalized medicine. With the ability to analyze small datasets that focus on the specific disease of interest, AI can aid in rationally designing optimal drug combinations that are effective and based on real experimental data, unlike mechanistic assumptions or predictive modeling. Additionally, research is underway regarding machine learning and predictive analytics in customizing treatment to a person's unique health history. At the moment, the emphasis is on supervised learning, in which clinicians can utilize genetic information and symptoms to reduce diagnostic options or make an informed judgment about a patient's risk, eventually leading to better preventive measures.
Many start-ups are also investing in the development of applications that take into account behavioral modification. For instance, Somatix, a gesture detection software, is used for wearable technology to aid in smoking cessation, while SkinVision is used to monitor skin cancer risk. Furthermore, a new strategic partnership between the University of Illinois Urbana-Champaign (UIUC) and Infosys would combine advanced machine learning tools with advanced biocomputing and genomic applications to enhance precision medicine and preventive care. The organizations would develop new technologies and systems for precision medicine, allowing caregivers to predict patients' diseases and control healthcare costs. The partnership aims to improve the predictability of treatment outcomes of potential illnesses. As a result, the ability to analyze large datasets of heterogeneous biological and clinical measures concurrently while incorporating critical clinical domain knowledge is critical to the future of precision medicine, tailoring diagnosis and treatment to each patient to optimize the outcome. With such rising needs, AI's function is projected to grow.
Massive cash injections are required to launch robust AI products off the ground, and it is not a sector that can be monetized either very quickly or easily. These products frequently require R&D teams and machine learning-focused engineers, both of which are expensive. Life science organizations not only have to support the initial outlay for software and costs for cloud support but also the ongoing costs for training the AI system when business processes change. Additionally, the inability to predict ROI, especially at the start of the project, also proves to be a source of friction.
Furthermore, while talent is undoubtedly a significant challenge, it is not as preeminent as structural challenges, which involve research and development to adapt it to an actual enterprise environment. Despite the significant opportunities to use AI to reduce healthcare costs and enhance patient outcomes, there are still substantial obstacles to success. The computer processing power and capacity need to increase even more before complex conditions can be mapped and addressed. A substantial amount of healthcare and medical research data is needed to be meticulously vetted before it is used to produce trustworthy findings since the quality of the data determines the quality of the results. This procedure alone takes a lot of time and money. These problems impact the economics of AI adoption in the life sciences sector.
Although using computer simulations for drug discovery—also known as in-silico screening, design, and testing—is not a novel approach, the development of modern predictive analytical tools has significantly increased the power of in-silico. Pharmaceutical companies are investing in artificial intelligence to enhance disease target identification, compound screening, new drug design, and potency/toxicity predictions. Deep learning lends itself exceptionally well to drug discovery due to its unprecedented ability to extract key features from unprocessed raw data, from large or small data sets. Therefore, this can be incredibly advantageous in identifying new disease targets, generating novel leads, and predicting drug outcomes.
AI can assist in advancing the science of drug discovery by providing predictions in various novel biological and chemical fields. AI can help identify pertinent information quicker and establish connections between biomedical entities, such as drugs and proteins, by extracting text from scientific papers. There are now just two licensed medications on the market that have only modestly improved patient outcomes in 50 clinical trials for amyotrophic lateral sclerosis (ALS) over the past 20 years. Hence, AI can significantly transform the approaches in drug discovery by saving considerable costs to organizations.
The global artificial intelligence (AI) in life sciences market is segmented by application.
Based on application, the market is segmented into drug discovery, biotechnology, clinical trials, medical diagnosis, precision and personalized medicine, and patient monitoring.
The drug discovery segment holds a significant market share and is estimated to grow at a CAGR of 30.2% during the forecast period. Drug discovery is the most significant life science application using artificial intelligence (AI) solutions. For instance, according to Taconic Biosciences' tally, an incredible amount of money and time goes into drug development and bringing a drug to market, costing about USD 2.8 billion over 12 years. AI solutions are increasingly becoming an essential component among several major pharmaceutical companies for creating a decision support system that analyzes various parameters and outcomes to help them decide to invest in particular drug discovery. Such a decision support system can help expedite the time taken to market the drug and give the end users an advantage in the highly competitive segment. Therefore, the factors mentioned above contribute to market growth.
One of the life sciences sector's most data-intensive responsibilities is conducting clinical studies. They collect enormous amounts of data daily while monitoring various patient characteristics. The researchers may find meaningful associations between loosely connected data by putting these data sets through intelligent AI systems. This motivates numerous pharmaceutical firms and clinical research institutions to invest in cutting-edge tools like artificial intelligence. The pharmaceutical business, which conducts more than 50% of all clinical trials annually worldwide, is closely monitoring the increasing uptake of AI in the current market context. Numerous well-known businesses, including GlaxoSmithKline, Sanofi, Pfizer Mitsubishi Tanabe Pharma, and Genentech, among others, are investing in start-ups and solutions for AI-based clinical trials to lower the costs of clinical trials. Consequently, it is anticipated that as the number of clinical trials in the field rises, so will the demand for solutions that may expedite the process and lessen its numerous difficulties.
Medical diagnosis is expected to be the fastest-growing segment. The high usage of AI-based solutions to create chatbots and mobile apps to diagnose several typical symptoms is the primary factor driving the immense growth of AI in medical diagnosis applications. Core applications needed for significant healthcare companies are also witnessing high demand in the global market. Solutions studying and identifying diseases from medical imaging and live patient symptoms are increasingly becoming popular in diagnosis applications, especially in regions with fewer medical professionals.
The global artificial intelligence (AI) in life sciences market is segmented into four regions, namely North America, Europe, Asia-Pacific, and the Rest of the World.
North America is the most significant shareholder in the global artificial intelligence (AI) in life sciences market and is estimated to grow at a CAGR of 26.6% during the forecast period. The United States is the largest market because of the substantial demand for AI solutions from virtually all life science applications. The country has a huge demand for AI solutions for drug discovery, precision medicine, and biotechnology applications. According to the FDA's Drug Trail Snapshot Report-2019, more than 27% of patients participating in drug trials were of Asian or Hispanic ethnicity, while only 40% of all clinical trial participants were Americans. This scenario is anticipated to significantly increase demand for applications of personalized medicine using AI technology during the forecast period.
Asia-Pacific is expected to grow at a CAGR of 30.2%, generating USD 3,775 million during the forecast period. China's biotechnology sector has experienced double-digit growth, moving from one of the nations with the slowest adoption rates to one of the fastest. Also, key players are focusing on establishing a drug research center in China that caters to market growth. The study published in the journal JAMA Cardiology showed that the Chinese population's annual deaths from cardiovascular disease increased from 2.51 million to 3.97 million in 2018. Thus it is anticipated that the demand for AI monitoring patients will support the market's growth during the forecast period.
The fastest-growing region is Europe, with Germany representing the biggest regional market for AI in the life sciences. The country has some of the best research facilities in the world, providing a suitable environment for the growth of clinical trials. According to Lymphoma Coalition, Germany recorded the highest number of clinical trials in 2018, at 120, closely followed by Italy, which conducted 119 trials. The German government is involved in the field as it provides country-wide financial support for AI, such as proposed investments amounting to EUR 3 billion by 2025. It also aids in applying technological progress in social and healthcare improvement. According to the Office for Life Sciences, Germany is the second-largest European country with the most productive pharmaceutical manufacturing sectors. Such factors are expected to drive more investment into the country's drug discovery applications, creating considerable demand for AI solutions over the forecast period.
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