The AI Revolution in Biotechnology: A New Turning Point in Drug Development and Precision Medicine by 2025
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As of the end of 2025, the biotechnology industry is experiencing an unprecedented wave of change through its integration with artificial intelligence. The global AI biotech market size is expected to grow from $92 billion in 2024 to $124 billion in 2025, a sharp increase of 34.8%, paving the way for innovative breakthroughs in traditional drug development and precision medicine. Particularly, the practical application of technologies such as large language models (LLMs) and deep learning for protein structure prediction, molecular design, and clinical trial optimization is becoming a reality, potentially reducing the traditional drug development timeline from 10-15 years to 5-7 years.
At the heart of this transformation are AI models like Google DeepMind’s AlphaFold 3.0 and Meta’s ESM-3. Since its release in the first half of 2025, AlphaFold 3.0 has predicted over 200 million protein structures with 99.5% accuracy, demonstrating a time-saving effect of over 70% in the drug target discovery process compared to traditional methods. Meanwhile, Meta’s ESM-3 has shown the capability to simultaneously predict structure and function from protein sequences, achieving a success rate of over 95% in antibody design. The commercialization of these AI tools is fundamentally reshaping R&D investment strategies in the global pharmaceutical industry, with the average AI investment share of the top 20 pharmaceutical companies reaching 28% of their total R&D budget by 2025.
South Korea’s biotech ecosystem is also rapidly growing in line with these global trends. According to the Korea Biotechnology Industry Organization, the domestic AI biotech market size increased by 48.3% from 890 billion won in 2024 to 1.32 trillion won in 2025, significantly surpassing the global average growth rate. Notably, Samsung Biologics (Incheon, Gyeonggi-do) achieved a 15% improvement in production yield by introducing an AI-based biopharmaceutical production optimization system in the second half of 2025, while Celltrion (Incheon) announced that it had reduced the development period of biosimilars from five years to three years by establishing an AI-driven development platform. These achievements suggest that South Korea is solidifying its position as a global biotech manufacturing hub while emerging as a leader in next-generation biotech innovation based on AI technology.
AI Innovation in Drug Development Paradigm
The most significant change in the traditional drug development process is occurring in the early target discovery and lead compound optimization stages. Previously, the mainstream approach involved random screening of thousands of compounds, but now the paradigm is shifting to AI algorithms learning complex correlations between molecular structures and biological activity to predict optimal candidate substances. UK-based Exscientia announced that its AI-designed drug DSP-1181 showed 40% higher efficacy compared to existing treatments in Phase 2 clinical trials in 2025, demonstrating the tangible outcomes of AI drug development. This drug is the first AI-designed case to enter late-stage clinical trials, with the development period shortened from eight years to four and a half years.
American pharmaceutical giants are also expanding large-scale investments in AI drug development. Johnson & Johnson (New Jersey) invested $1.5 billion in 2025 in its dedicated AI drug development organization, ‘J&J Innovation Labs,’ to accelerate the development of next-generation immuno-oncology and rare disease treatments. Pfizer (New York) has built a quantum computing-based molecular simulation platform through a partnership with IBM. This new platform can calculate molecular interactions 1,000 times faster than existing supercomputers, enabling the development of next-generation therapies targeting complex protein-protein interactions. Due to such technological advancements, the number of AI-designed drugs approved by the FDA reached 23 in 2025, nearly tripling from eight in 2024.
The use of AI in clinical trial design and patient recruitment is also rapidly increasing. Swiss-based Roche (Basel) introduced an AI-based patient screening system in 2025, reducing the average recruitment period for clinical trial participants from six months to 2.5 months and improving trial success rates from 78% to 89%. This system comprehensively analyzes electronic health records (EHR), genomic data, and lifestyle information to pre-identify patient groups most suitable for specific treatments. Additionally, Novartis (Basel, Switzerland) commercialized a system that uses digital biomarkers to collect and analyze data from patients’ smartphones and wearable devices in real-time, monitoring drug responses 24/7 during clinical trials. These innovative approaches have simultaneously improved the accuracy and efficiency of clinical trials, with the global clinical trial market size growing from $48 billion in 2024 to $59 billion in 2025, a 22.9% increase.
A particularly noteworthy case is AbbVie’s (Illinois) AI-based immuno-oncology development project. In early 2025, AbbVie entered into a strategic partnership with Google Cloud to build an AI platform that analyzes the tumor immune environment in real-time and proposes personalized cancer treatment strategies. This platform analyzes over 50,000 immune cell profiles from a patient’s tumor tissue sample to suggest the optimal combination of immuno-oncology drugs, showing a 50% higher response rate compared to existing standard treatments in early clinical trials. Based on these achievements, AbbVie’s market capitalization surpassed $320 billion by the end of 2025, firmly maintaining its position as one of the top three companies in the global pharmaceutical industry.
New Horizons in Precision Medicine and Personalized Treatment
The advancement of AI technology is also driving revolutionary changes in the field of precision medicine. As of 2025, the global collection of genomic data has surpassed one billion, leading to the full-scale development of personalized treatment strategies based on this data. Genomics England completed a database of five million genomes in 2025, increasing the accuracy of rare disease diagnosis to 95%, while the US NIH’s All of Us program secured genomic data from two million people of diverse ethnicities, successfully analyzing drug response differences by race. AI models trained on these large datasets can now propose optimal treatment strategies by comprehensively considering individual patients’ genetic variations, lifestyle, and environmental factors.
In South Korea, the use of AI in the precision medicine field is rapidly expanding. Seoul National University Hospital and Samsung Medical Center jointly established a ‘Korean Personalized Precision Medicine Platform’ in 2025, developing AI models that analyze the unique genetic variations and disease susceptibilities of Koreans. This platform integrates and analyzes the genomes, proteomes, and metabolomes of 500,000 Koreans to predict the risk of major diseases such as diabetes, hypertension, and cancer with 95% accuracy and suggests optimal drugs and dosages for individuals. Particularly for gastric cancer, which has a high prevalence among Koreans, early diagnosis using this AI model has significantly improved the five-year survival rate from 75% to 92%.
The global precision medicine market is witnessing fierce competition between US and Chinese companies. Illumina (California) succeeded in lowering the cost of next-generation sequencing (NGS) to below $100 per genome in 2025, leading the popularization of genomic analysis, while China’s BGI Genomics (Shenzhen) established the largest genomic database in Asia to develop Asia-specific precision medicine solutions. Notably, BGI achieved a 99.9% accuracy rate for its AI-based prenatal diagnostic kits in the second half of 2025, capturing a 45% global market share and recording an annual revenue of $3.8 billion, experiencing rapid growth.
In the precision medicine field, immunotherapy and cell therapy are particularly noteworthy areas. In the case of CAR-T cell therapy, AI-driven personalized design has significantly improved treatment success rates. Novartis, the developer of Kymriah, enabled the design of optimized CAR-T cells for individual patients within 48 hours using an AI-based CAR-T design platform in 2025, improving the treatment response rate from 60% to 85%. Additionally, China’s Legend Biotech achieved a complete remission rate of 95% for multiple myeloma patients with its AI-optimized CAR-T therapy, gaining global attention. Based on these achievements, the global cell therapy market size grew from $8.9 billion in 2024 to $12.5 billion in 2025, a 40.4% increase.
Meanwhile, the combination of liquid biopsy technology and AI is also significantly contributing to the advancement of precision medicine. Guardant Health (California) commercialized a technology in 2025 that can detect over 50 types of cancer with 95% accuracy through an AI-based blood test, achieving an annual revenue of $1.5 billion. This technology analyzes subtle changes in circulating tumor DNA (ctDNA) in the blood to predict not only the presence of cancer but also its location, stage, and treatment response. Domestically, Macrogen and Theragen Etex are also accelerating the development of AI-based liquid biopsy technology, with the domestic liquid biopsy market size estimated to reach 280 billion won by the end of 2025.
The AI revolution in the biotechnology industry in 2025 is fundamentally changing the medical paradigm beyond mere technological progress. The significant reduction in drug development timelines, the practical application of precision medicine, and the generalization of personalized treatment provide better treatment opportunities for patients while greatly improving the profitability of the pharmaceutical industry. With the global AI biotech market expected to sustain high growth at an average annual rate of 32% over the next five years, the technological competitiveness and market responsiveness of major countries, including South Korea, will be key factors determining their status in the future biotech ecosystem. Particularly, the harmonious development of AI technology, data infrastructure, and regulatory innovation is analyzed to be crucial for the success of the next-generation biotech revolution, which investors and industry stakeholders should closely monitor.
This analysis is based on publicly available market data and industry reports and is not intended as investment advice or a recommendation for specific companies. Please seek expert advice when making investment decisions.
