The artificial intelligence (AI) in genomics utilizes AI and deep learning in various genomics applications such as DNA/RNA sequencing, genetic diagnostics, and others. Advanced AI solutions have the potential to revolutionize precision medicine by enabling more accurate disease diagnosis and personalized treatment options. Some key use cases of AI in genomics include prediction of disease risks, drug discovery, diagnosing rare diseases, and accelerating genomic research.

The global artificial intelligence (AI) in genomics market is estimated to be valued at US$ 1,203 Mn in 2023 and is expected to exhibit a CAGR of 6.1% over the forecast period 2023 to 2030, as highlighted in a new report published by Coherent Market Insights.

Market Dynamics:

The Global AI in genomics market size is primarily driven by the rising demand for precise disease diagnosis. AI has the capability to analyze large and complex genomic datasets with high accuracy. This helps in detecting subtle patterns, correlations, and insights that humans may miss. For example, AI tools are being used for early detection of cancer by analyzing DNA mutations and protein expressions. Furthermore, growing genomic research activities are also fueling the adoption of AI technologies. Several biotech and pharmaceutical companies are investing in AI to discover new drug targets and accelerate drug development processes. Additionally, increasing collaboration between AI and genomics companies is leading to development of more advanced solutions, thereby supporting market growth over the forecast period. However, data privacy and security concerns pertaining to genomic data can hamper the market growth.

SWOT Analysis

Strength: AI in genomics market rely on predictive analytics and machine learning technologies which improves accuracy and speed of discovering genetic patterns, analyzing sequencing data and identifying disease-causing genetic variations. AI algorithms continuously learn and improve with massive datasets which further enhances precision and diagnostic capabilities of genetic tests. AI streamlines clinical trial recruitment process and drug discovery procedures based on genomic profiling.

Weakness: Lack of high computational infrastructure and skilled workforce limits widespread adoption of complex AI algorithms in underdeveloped regions. Ethical and legal concerns over data privacy, algorithmic bias and transparency of AI models act as a deterrent for some organizations.

Opportunity: Integration of AI with blockchain, cloud and edge computing opens new revenue streams. Personalized genomic tests and therapeutics based on individual genetic profiles present a lucrative opportunity. AI assists in disease monitoring, drug repurposing and developing personalized vaccines.

Threats: Stiff competition from niche genomic players and established life science giants poses pricing pressure. Vulnerabilities in AI systems can be exploited by bad actors for identity theft, insurance fraud or falsifying clinical results. Stringent regulations around use of genetic and health data increases compliance costs.

Key Takeaways

The global AI in genomics market is expected to witness high growth. The global Artificial Intelligence (AI) in Genomics Market is estimated to be valued at US$ 850 million in 2023 and is expected to exhibit a CAGR of 6.1% over the forecast period 2023 to 2030.

North America currently dominates the market due to presence of major genomic research centers, advanced genome sequencing infrastructure, and supportive funding landscape. The region is expected to continue its dominance in the forecast period on account rapid adoption of AI for precision medicine and clinical trials. Asia Pacific is projected to be the fastest growing region in the AI in genomics market driven by increasing healthcare investments, rising affluence, expanding biotech industry and large population.

Key players operating in the AI in genomics market are IBM, Intel, Nvidia, Google, Microsoft, Deep Genomics, BenevolentAI, and Data4Cure. IBM is a leading provider of AI solutions such as Watson for Genomics which researchers and clinicians deploy for discovery, diagnosis and treatment of various diseases. Intel focuses on high performance GPUs and FPGAs that expedite genomic analysis and machine learning workloads. Nvidia GPUs are used by genomic companies to train vast neural networks involved in drug development and clinical diagnostics processes.

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