AI In Health Care And Biotechnology: Promise, Progress, And Challenges – Healthcare

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.

Footnotes

1 Bhushan and Misra (2025) Unlocking the potential:
multimodel AI in biotechnology and digital medicine –
economic impact and ethical challenges, npj |digital medicine,

2 Id. at page 1

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