ARTIFICIAL INTELLIGENCE (AI) IN BIOTECHNOLOGY MARKET SIZE AND SHARE ANALYSIS - GROWTH TRENDS AND FORECASTS (2024-2031)

Artificial Intelligence (AI) in Biotechnology Market Size and Trends

Global AI in biotechnology market is estimated to be valued at USD 2.10 Bn in 2024 and is expected to reach USD 7.11 Bn by 2031, exhibiting a compound annual growth rate (CAGR) of 19% from 2024 to 2031. AI has the potential to revolutionize various processes in biotechnology such as drug discovery and development. It is used across many biotechnology domains like agriculture, healthcare, forensics and environmental protection due to low-cost genome sequencing and biological research.

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Increasing funding from private and public organizations for AI in biotechnology can drive the market growth during the forecast period. Declining hardware and processing costs coupled with the development of more advanced algorithms can boost the adoption of AI technologies in biotechnology applications. Rising need to eliminate expensive and time-consuming lab tests and trials can also drive the market growth.

Rising Drug discovery and precision medicine

The application of artificial intelligence in drug discovery and precision medicine  accelerate the progress of biotechnology research and development. With AI, researchers can now screen millions of potential drug compounds in silico in a fraction of the time it would take humans. Powerful machine learning algorithms are being trained on vast datasets containing genetic information, molecular structures, electronic health records and clinical trial outcomes. This allows AI to identify novel drug targets, propose new molecule designs and predict how patients may respond to different therapies based on their unique biological profiles. AI technologies have a transformative impact across the entire drug development pipeline. Companies are using deep learning to analyze biomolecular datasets and discover insightful biomarkers or disease subtypes that would otherwise be impossible to detect manually. Startups like Benevolent AI have discovered new drug candidates for hard-to-treat diseases by systematically screening billions of potential molecules. Pharmaceutical giants are also investing heavily in AI to address bottlenecks in preclinical and clinical testing. For instance, in November 2022, XtalPi Inc.  partnered with CK Life Sciences. This partnership leveraged their respective areas of expertise to create a cutting-edge AI tumor vaccine research and development platform, which will improve the ability to discover and design tumor vaccines and expedite the development of new vaccine types. Amgen's R&D and The Scientist established a relationship in June 2022. The partnership looks at novel approaches to drug discovery and development that leverage AI and machine learning to generate novel protein treatments.

Clinical trial recruitment and retention

Increasing complexity of clinical trials can drive the AI in biotechnology market growth. Recruiting patients and retaining them through the entire clinical trial process is challenging for biotech companies. AI can help address these issues by identifying more eligible candidates through advanced analytics of patient datasets. It allows targeting personalized outreach by leveraging parameters like medical history, demographics, habits and proximity to the hospital. This focused recruitment using AI improves participant diversity and reduces screening failure rates. Once enrolled, keeping patients engaged throughout the trial requires significant manual effort. Non-compliance to treatment protocols or dropping out prematurely impacts trial timelines and results. Thus, AI continuously monitor participants through digital technologies. Technologies like mobile apps, wearables and remote monitoring devices provide vital signs, medication intake details, and others on a real-time basis. AI tools can identify early signs of disengagement or non-adherence by spotting anomalies in the data patterns. Timely interventions like counselling sessions or extra care can be initiated to boost retention. The use of AI for predictive modelling also helps estimate at-risk patients.