The global artificial intelligence (AI) in biotechnology market report provides a detailed analysis of the market dynamic and the report sheds light on the current situation of the market size, share, and forecast 2023 to 2032.
Market Overview:
The discovery of pharmacological targets, image screening, drug screening, and predictive modeling are all examples of AI uses in the biotechnology industry. Clinical trial data is also managed, and the scientific literature is searched using AI. Artificial intelligence is a hot topic because of its widespread popularity (such as ChatGPT). When biotechnology and AI evolve together, new unprecedented possibilities become accessible. This can aid in solving several global issues and advance significant Sustainable Development Goals. Some examples of current goals include ensuring food security, access to clean water, well-being and health, responsible consumption, clean energy and production, combating climate change, protecting life below the ocean, restoring and promoting sustainable use of terrestrial ecosystems, managing forests sustainably, reversing and halting land degradation, preventing desertification, and halting biodiversity loss.
Regional Snapshot:
North America is observed to sustain its leading position in the market throughout the forecast period. Due to the increasing incidence of chronic illnesses among the population, North America is anticipated to hold a significant portion of the worldwide market for AI in Biotechnology. Additionally, a strong healthcare infrastructure, expanding research efforts, and the concentration of considerable industry participants are important factors in the region's pharmaceutical industries' use of artificial intelligence.
Asia Pacific is expected to witness significant growth during the forecast period during the forecast period. In the upcoming years, biotech spending in Asia Pacific will increase the most due to various variables, such as a growing population, rising healthcare requirements, and advantageous governmental regulations. The accessibility of financing is one of the major factors influencing biotech investments in the Asia Pacific. Private investors and governments in the area have recently made significant investments in biotech due to realizing the potential for the sector to expand and innovate.
Report Highlights:
Artificial Intelligence (AI) in Biotechnology Market Report Scope:
| Report Coverage | Details |
| Fastest Growing Market | Asia Pacific |
| Largest Market | North America |
| Base Year | 2022 |
| Forecast Period | 2023 To 2032 |
| Regions Covered | North America, Europe, Asia-Pacific, Latin America, and Middle East & Africa |
Market Dynamics:
Driver:
Rising research activities in molecular breeding
Molecular breeding refers to the use of molecular biology methods, particularly the genetic modification of DNA, to enhance the qualities of animals or plants. Genomic selection, molecular marker assistance, and gene modification or genetic engineering are some of the available techniques. The practice of plant tissue culture is beneficial for commercial plant multiplication. It has been used recently for various purposes, including genetic modification, disease resistance in plants to protect species on the brink of extinction, and quick manufacturing of plants irrespective of season. These applications use a variety of approaches, including tissue, organ, and in-vitro plant regeneration, aseptic cell growth, molecular genetics, genome analysis, gene transfer, and recombinant technologies to enhance crop performance in agriculture. Applying AI technologies and algorithms for molecular breeding in agriculture has several advantages, including year-round output regardless of season, consistent plant growth, genetic improvement for improved plant efficiency, and genetic preservation of favorable genetics. Thus, the rising research in the molecular breeding field drives the growth of the market.
Restraint:
Hesitance from the biotechnology companies
Initially, due to the lack of awareness and acceptance from industries, the deployment rate of artificial intelligence solution platforms was low. The major obstacle in the adoption and acceptance of AI in biotechnology is the high investment cost required in the initial set-up. Biotechnology companies require a specific infrastructure for the deployment, this transformation adds up to the cost of initial set-up. The acceptance of AI platforms is again affected by hesitance in conversation. Biotechnology companies are prone to have human intervention in every operation, the penetration of AI may affect the human touch by creating a reliability challenge for the operator. All these factors hamper the growth of the market.
Opportunity:
Advances in medical biotechnology
Medicinal biotechnology creates medications and antibiotics from live cells to improve human health. It also uses DNA research and genetically modifies cells to produce more vital and useful traits. In the process of finding new drugs, the sector of medical biotechnology has recently started employing machine learning and artificial intelligence. Machine learning aids in the discovery of tiny compounds that, depending on known target structures, may have therapeutic advantages. Machine learning is commonly utilized in illness diagnosis since it uses the actual findings of diagnostic tests to improve them, meaning that the more tests that are performed, the more accurate the results that may be obtained. AI is also assisting in speeding up the planning stage of radiation therapy, which saves time and enhances patient care. Enhancing electronic health records with evidence-based medications and systems that support clinical decisions is another field where machine learning and artificial intelligence are shown promise. These technologies are widely employed in fields such as radiography, gene editing, personalized medicine, medication management, and others, in addition to the ones mentioned above.
Challenge:
Machine learning limitations in environmental sciences
There are still difficulties in environmental sciences even though machine learning (ML) is already well-established in medical research that integrates multi-omic techniques for system biology. As the quantity of general databases grows substantially, a few issues arise that create a significant challenge for the penetration of machine learning in environmental science. Issues such as utilization of soil metaproteomic, connecting it with omic data, absence of this information and consumed power for the same create a certain limitation for the machine learning to be implemented in the environmental sciences while creating a challenge for the market.
Recent Developments:
Major Key Players:
Market Segmentation:
By Offering
By Applications
By Usage
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