AI in Life Sciences Market
Number of pages: 100 | Report Format: PDF | Published date: June 08, 2023
Historical Years – 2021 | Base Year – 2022 | Forecasted Years – 2023-2031
|
Report Attribute |
Details |
|
Market Size Value in 2022 |
US$ 1.54 billion |
|
Revenue Forecast in 2031 |
US$ 7.79 billion |
|
CAGR |
19.70% |
|
Base Year for Estimation |
2022 |
|
Forecast Period |
2023 to 2031 |
|
Historical Year |
2021 |
|
Segments Covered |
Offering, Deployment, Application, and Region |
|
Regional Scope |
North America, Europe, Asia Pacific, Latin America, and the Middle East & Africa |
According to the deep-dive market assessment study by Growth Plus Reports, the global AI in life sciences market was valued at US$ 1.54 billion in 2022 and is expected to register a revenue CAGR of 19.70% to reach US$ 7.79 billion by 2031.
AI in Life Sciences Market Fundamentals
AI is accelerating the drug discovery process by assisting in identifying potential drug candidates. Machine learning algorithms can analyze large databases of chemical compounds and predict their effectiveness in targeting specific diseases. This helps in narrowing down the search for potential drugs, reducing the costs and time required for traditional trial-and-error methods. AI enables personalized medicine by analyzing large-scale patient data, including genetic information, medical records, and lifestyle data. Deep learning algorithms can analyze medical images, such as X-rays, MRIs, and CT scans, to detect abnormalities, tumors, and other diseases. Additionally, AI algorithms can analyze complex biological data, such as proteomics and metabolomics data, to identify biomarkers—indicators of diseases or treatment responses. Furthermore, NLP techniques enable extracting and analyzing information from medical literature, clinical notes, and patient records. AI-powered NLP systems can assist in literature reviews, drug interactions analysis, adverse event monitoring, and clinical decision support.
AI in Life Sciences Market Dynamics
The life sciences industry generates vast amounts of data from various sources, including genomics, clinical trials, electronic health records, and medical imaging. The shift towards precision medicine, which aims to tailor treatments to individual patients based on their genetic makeup and other factors, has created a demand for advanced data analytics and AI-driven solutions. AI-powered technologies, such as machine learning and predictive modeling, are being utilized to expedite the drug discovery process by identifying potential drug candidates, predicting their efficacy, and optimizing clinical trial designs. This can potentially reduce costs, improve success rates, and accelerate the availability of new therapies. AI has shown great promise in medical imaging analysis and diagnostics. Deep learning algorithms can analyze large volumes of medical images, detect anomalies, and assist radiologists in making more accurate diagnoses, thus, driving the development and adoption of AI tools for image-based diagnostics, such as computer-aided detection systems and automated image interpretation. Additionally, growing strategic collaborations or partnerships further helps the overall market growth. For instance, Amgen's R&D announced a partnership with The Scientist in June 2022; this collaboration investigates cutting-edge drug discovery, and development approaches that use artificial intelligence (AI) and machine learning to build novel protein therapeutics. Regulatory bodies recognize AI's potential in the life sciences industry and are working to establish guidelines and frameworks to ensure its safe and effective use. Regulatory support, such as the FDA's Digital Health Software Precertification (Pre-Cert) Program, provides a pathway for AI-driven medical software and devices to gain approval and enter the market. AI can play a crucial role in public health initiatives, disease surveillance, and outbreak management. By analyzing large-scale data, AI algorithms can identify patterns, detect emerging diseases, and monitor public health trends, enabling proactive measures to control the spread of diseases and improve population health.
AI algorithms require large volumes of high-quality data to train and make accurate predictions. However, in the life sciences field, data quality can vary, and access to comprehensive and diverse datasets can be challenging. Issues such as data bias, incomplete data, and lack of standardized formats can affect the performance and reliability of AI models. Integrating AI technologies with existing healthcare systems and workflows can be challenging. Compatibility issues, technical infrastructure requirements, and interoperability concerns need to be addressed for the seamless integration of AI solutions into healthcare environments. The transition from legacy systems to AI-driven platforms may require significant investments in infrastructure and training. AI in life sciences requires collaboration between domain experts, data scientists, and AI researchers. However, there can be a shortage of AI expertise and a lack of understanding of AI among healthcare professionals and researchers. Bridging the gap between AI and life sciences domains and fostering interdisciplinary collaborations is essential for successfully implementing and adopting AI in the industry. Implementing AI solutions in the life sciences industry can involve significant upfront costs, including infrastructure, data management, and talent acquisition. Organizations need to carefully assess the cost-benefit ratio and demonstrate the return on investment (ROI) of AI implementations to justify the expenses and ensure long-term sustainability.
AI in Life Sciences Market Ecosystem
The global AI in life sciences market has been analyzed from the following perspectives: by offering, deployment, application, and region.
AI in Life Sciences Market by Offering
Based on the offering, the global AI in life sciences market is segmented into services, software, and hardware.
The software segment dominates the global market. Software plays a critical role in data analytics and machine learning, which are the foundation of AI applications in life sciences. Software tools and platforms enable the processing, analyzing, and interpreting of large volumes of complex biological data, such as genomics, proteomics, and medical imaging. Machine learning algorithms implemented in software can discover patterns, make predictions, and assist in decision-making processes. Software solutions offer flexibility and scalability, allowing users to adapt and customize AI applications to specific research or healthcare needs. Software-based AI systems can be easily updated, integrated with existing infrastructure, and scaled up to handle large datasets and complex algorithms. This flexibility and scalability make software preferred for implementing AI solutions in diverse life sciences applications. The software industry has witnessed significant advancements in AI frameworks and libraries, such as TensorFlow, PyTorch, and sci-kit-learn. These frameworks provide developers with the tools and resources to efficiently build and deploy AI models. The availability of robust and user-friendly software development tools has accelerated the adoption of AI in life sciences. Integration with these systems enables seamless data flow, interoperability, and enhances the overall efficiency of AI applications in life sciences. Software-based solutions offer the flexibility to connect with diverse data sources and leverage existing IT infrastructure. Software solutions are generally more accessible and user-friendly compared to hardware alternatives. With the increasing demand for AI in life sciences, software providers focus on creating intuitive interfaces, user-friendly workflows, and visualization tools that cater to the needs of researchers, healthcare professionals, and data scientists. The ease of use and accessibility of software solutions make them more widely adopted across the industry.
AI in Life Sciences Market by Deployment
Based on the deployment, the global AI in life sciences market is segmented into on-premise and cloud-based.
The cloud-based segment dominates the global market. Cloud-based AI solutions offer scalability and flexibility, allowing organizations to easily scale their computing resources based on the needs of their AI applications. Cloud platforms provide the ability to rapidly scale up or down computing resources, enabling efficient processing of big data and complex AI algorithms. AI applications in life sciences often require significant computational power and resources. Cloud providers offer specialized high-performance computing (HPC) instances and GPU acceleration options that are optimized for AI workloads. These capabilities allow life sciences organizations to perform complex AI tasks efficiently and at scale without investing in and maintaining dedicated HPC infrastructure. Cloud platforms provide robust data storage and management solutions that can handle the large and diverse datasets encountered in life sciences. The on-cloud segment offers data replication, backup, version control, and security measures to ensure data integrity and compliance with regulatory requirements. Cloud-based data management facilitates easy access, sharing, and analysis of data, supporting AI applications in life sciences.
AI in Life Sciences Market by End User
Based on the end user, the global AI in life sciences market is segmented into biotechnology, medical diagnosis, drug discovery, clinical trials, and others.
The process of discovering and developing new drugs is complex, time-consuming, and expensiveThe application of AI in drug discovery holds the promise of reducing costs, shortening timelines, and increasing the success rate of bringing new drugs to market. AI plays a crucial role in target identification and validation, which are early stages of the drug discovery process. AI algorithms can analyze biological data, including genetic information and molecular interactions, to identify potential drug targets and validate their relevance in disease pathways. By narrowing down the search for viable drug targets, AI helps researchers focus their efforts and resources on the most promising candidates, saving time and reducing the risk of failure. AI algorithms can perform virtual screening of large chemical libraries to identify molecules that can potentially interact with specific drug targets. Virtual screening using AI techniques significantly accelerates the identification of lead compounds for further development. AI can also optimize lead compounds by predicting their efficacy, toxicity, and pharmacokinetic properties, leading to more efficient and targeted drug development processes. AI algorithms can sift through large datasets, such as electronic health records and biomedical literature, to identify potential candidates for repurposing, offering cost and time advantages. The drug discovery segment is driven by addressing unmet medical needs and developing new disease treatments. AI's ability to expedite the drug discovery process and uncover novel therapeutic targets provides hope for addressing these unmet needs effectively. The potential of AI in identifying breakthrough drug candidates and accelerating the development of innovative therapies makes it a dominant force in the drug discovery segment.
AI in Life Sciences Market by Region
Based on the region, the global AI in life sciences market is segmented into North America, Europe, Asia Pacific, Latin America, and Middle East & Africa.
North America, particularly the United States, has a well-established infrastructure for research and development in the life sciences industry. It houses renowned universities, research institutions, and pharmaceutical companies actively engaging in AI-driven research and innovation. The presence of robust R&D infrastructure fosters a conducive environment for developing and adopting AI technologies in life sciences. North America is home to several leading AI companies specializing in developing AI solutions for the life sciences industry. These companies have the expertise, resources, and capabilities to develop advanced AI algorithms, platforms, and tools tailored specifically for life sciences applications. These companies' presence contributes to North America's dominance in the AI in life sciences market. North America has strong market demand for AI-driven solutions in the life sciences industry. The region has a large healthcare market, including pharmaceutical companies, research organizations, and healthcare providers actively seeking innovative technologies to enhance patient care and outcomes. The high demand for AI technologies drives the adoption and deployment of AI solutions in the region, further solidifying North America's dominance in the global market. North America has been at the forefront of technological advancements, including AI, in various industries. The region has a strong ecosystem of technology companies, start-ups, and research institutions that continuously push the boundaries of AI in life sciences. The advancements in AI algorithms, machine learning techniques, and computational infrastructure originating from North America contribute to its dominant position in the global AI in life sciences market.
Competitive Landscape
The prominent players operating in the global AI in life sciences market are:
Strategic Developments
Artificial Intelligence (AI) has significantly impacted the field of life sciences, revolutionizing various aspects of research, drug discovery, diagnostics, and personalized medicine.
IBM Corporation, Nuance Communications Inc., and APIXIO Inc. are some of the major market players in the global AI in life sciences market.
The high cost of the AI implementation and the lack of skilled professionals are some of the restraints obstructing the growth of the global market.
On-cloud segment dominates the global AI in life sciences market.
Growing technological advancement and the growing need for precision medicine are some of the major factors driving the global market.
*Insights on financial performance are subject to the availability of information in the public domain
Recommended Reports