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Artificial intelligence (AI) is transforming health care and biotechnology, propelling advancements in drug discovery, genomics, medical imaging, and personalized medicine. It promises faster innovation, lower costs, and precision treatments tailored to individualized patients. Yet while the technology dazzles with the speed and the data that has been evaluated, from AI models identifying new drug candidates in weeks to U.S. Food and Drug Administration (FDA)-cleared imaging algorithms assisting diagnosis, its real world impact on reducing costs is still unfolding. The current and future impact of AI in health care and biotechnology is discussed by Arya Bhushan and Preeti Misra in the recent review "Unlocking the potential: multimodal AI in biotechnology and digital medicine – economic impact and ethical challenges" (hereinafter "Potential").1
The Market
The interest and need are here. The authors acknowledge that cloud-based and AI-driven technologies are increasingly automating drug discovery and advancing biomedical research. The global AI market is rapidly expanding, with significant growth projected through 2032, especially in North America. In the pharmaceutical and biotechnology sectors, AI's market value is expected to rise sharply, and by 2030, AI is predicted to play a role in developing more than half of new drugs.2 However, key challenges in the development and application of AI in health care are present, including data quality, algorithmic transparency, and ethical concerns, highlighting the urgent need for explainable AI models, robust regulatory frameworks, and equitable implementation to ensure responsible and impactful adoption across global healthcare systems.
Current Application in Health Care and Biotechnology
The authors evaluated a wide spectrum of AI technologies applied within biotechnology, including multimodal AI models that integrate imaging data, electronic health records, and clinical notes; advanced algorithms for drug discovery and development; precision medicine platforms; genomics and proteomics analysis tools; synthetic biology applications; automated diagnostics; and digital biomarkers. Specific subfields highlighted include AI-driven solutions in drug discovery, precision medicine, genomics, bioinformatics, clinical trials, and health care systems. The analysis also considered generative models such as Variational Autoencoder (VAE) and Generative Adversarial Network (GAN) for virtual screening, as well as convolutional neural networks (CNNs) in medical imaging.
Endpoints assessed in Potential were the volume and growth of AI-related publications and patents (across languages and subfields), trends in research activity, the impact of AI on research & development timelines and operational costs, clinical adoption rates (such as FDA-cleared AI/Machine Learning (ML)-enabled imaging devices), and the legal status and distribution of patents by jurisdiction. Additional endpoints are the economic value generated through efficiency improvements, market valuation trends, and the concentration of intellectual property among leading institutions and corporations. The article also examines publication bias, accessibility, and inclusivity within the global research landscape, recommending systematic reviews and bias-aware techniques to ensure balanced assessments.
The Bottom Line
The research and analysis of Potential demonstrate that artificial intelligence is fundamentally transforming biotechnology, with major impacts on research, diagnostics, and economic value creation. AI's integration into medical imaging and diagnostics has accelerated workflows, improved accuracy, and enabled the discovery of novel biomarkers, driving more personalized and effective therapies. The authors also evaluated patent filings as a measure of economic investment and conclude that the rapid growth in AI-related publications and patents signals increased global investment and interest, particularly in subfields like drug discovery, precision medicine, and genomics.
The promise lies in AI's ability to revolutionize biotechnological processes, deliver precision medicine, and expand opportunities for innovation and economic growth. However, the authors also identified pain points: there is significant publication bias favoring positive outcomes, limited access to unpublished and proprietary data, and underreporting of failures. The authors believe that the predominance of English-language publications raises concerns about global accessibility and inclusivity. To address these challenges, the authors recommend systematic reviews of gray literature, inclusion of qualitative insights, and adoption of bias-aware bibliometric techniques to ensure a balanced assessment of AI's impact. Overall, while AI offers transformative potential, realizing its full benefits will require robust evidence-gathering, global collaboration, and attention to the limitations inherent in current research and reporting practices.
AI is poised to revolutionize the biotechnology landscape, offering unprecedented opportunities for advancements in drug discovery, genomics, medical imaging, and synthetic biology. While challenges remain, the economic benefits of AI—cost reduction, increased productivity, market growth, job creation, and healthcare savings—are driving rapid adoption and development. As technology continues to evolve, the integration of AI in biotechnology promises to unlock new frontiers in biological research and healthcare, ultimately improving human health and well-being while contributing to economic growth. Addressing the challenges and ensuring ethical practices will be key to realizing the full potential of AI in biotechnology.
Footnotes
1 Bhushan and Misra (2025) Unlocking the potential: multimodel AI in biotechnology and digital medicine – economic impact and ethical challenges, npj |digital medicine, https://doi.org/10.1038/s41746-025-01992-6.
2 Id. at page 1
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