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Research Article | DOI: https://doi.org/10.31579/2692-9406/219
1Department of Pharmacy, University of Karachi, Head of Marketing and Sales, Riggs Pharmaceuticals, Karachi, Pakistan.
2Department of Pathology Dow University of Health Sciences, Fellow College of Physician and Surgeon, Assistant Professor Pakistan.
*Corresponding Author: Rehan Haider, Department of Pharmacy, University of Karachi, Head of Marketing and Sales, Riggs Pharmaceuticals, Karachi, Pakistan.
Citation: Rehan Haider, Hina Abbas, (2025), Generative AI in Life Sciences and Precision Medicine, J. Biomedical Research and Clinical Reviews, 10(5); DOI: 10.31579/2692-9406/219
Copyright: © 2025, Rehan Haider. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Received: 11 June 2025 | Accepted: 19 June 2025 | Published: 26 June 2025
Keywords: generative machine intelligence; accuracy medicine; existence sciences; artificial physical science; personalized situation; drug growth; biomedical AI; healthcare innovation; microscopic design; data-driven medicine
Generative machine intelligence (AI) is changing the landscape of existence sciences and accuracy medicine by contributing creative habits to generate and define organic dossier. These models go beyond established AI arrangements by creating new outputs—such as handbooks, concepts, or microscopic structures—based on patterns in existent datasets. In existing sciences, fruitful AI is used to design synthetic DNA and protein sequences, imitate container behaviors, and support biomedical depict. These efficiencies help analysts overcome data disadvantages and uncover new observations in biology and cures.In the field of accuracy medicine, fruitful AI allows the happening of personalized situation actions. It can resolve complex genetic and dispassionate dossier to group patients exactly and forecast by what they might put oneself in the place of another particular healing. This helps clinicians select the best situation alternatives based on an individual’s singular description. Additionally, in drug findings, generative models are being used to establish new compounds accompanying specific organic actions, cutting the game of chance moment of truth and cost of workshop testing.However, despite its benefits, the use of fruitful AI comes with main concerns. These involve issues related to dossier solitude, model dependability, ethical use, and supervisory authorization. Ensuring transparency and maintaining the kind of preparation data are key steps toward the mature unification of AI in healthcare. With continuous advancements, fruitful AI holds excellent promise for advocating more efficient research and giving doubtlessly personalized healthcare resolutions.
Generative Artificial Intelligence (AI) is changing research and novelty in the growth sciences and healthcare by enabling machines to produce new organic dossier alternatively solely analyzing existent datasets. This progressive arm of AI involves models such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and big devices that drive machine models, all of which have illustrated the talent to produce new genetic sequences, synthetic compounds, and even fake patient characterizations [1,2].
In growth sciences, fruitful AI is increasingly used to pretend container presence, combine gene and protein makeups, and find in essence samples for the preparation of other models when the original dossier is sparse [3]. These efficiencies improve the pace of scientific findings by lowering confidence according to schedule-absorbing and expensive workshop processes [4].
Precision cure focuses on adjusting situations to individuals established their historical structure, behavior, and environmental uncovering’s. Generative AI supports this aim by foreseeing patient reactions to treatments, simulating ailment pathways, and permissive the design of mean healing compounds [5]. Additionally, the use of “mathematical twins”—virtual likenesses of subjects established in AI-create simulations—allows clinicians to test various treatment alternatives nearly before asking bureaucracy in real history [6].
However, mixing fruitful AI into history sciences likewise raises concerns, particularly concerning dossier safety, moral use, and supervisory approval. Transparency in model happening and cautious confirmation of AI-create outputs are critical for ensuring security and honesty in dispassionate scenes [7].
As technologies advance, fruitful AI is necessary to enhance a main form in both lab research and dispassionate administration, aggressive the frontiers of precision cure and embodied care.
In addition to drug finding and patient posing, generative AI has shown allure potential in stimulating vaccine growth and optimizing dispassionate trial design. By simulating growing mutations and predicting antigenic sites, these models can influence the fast design of cure aspirants, a feature that became specifically valuable all the while the COVID-19 universal [8]. Generative models have also been used to generate artificial clinical trial states that indicate different demographics and comorbidities, permissive investigators to test theories virtually before spending in harmful authentic-world troubles [9]. Furthermore, the unification of a multi-omics dossier—including genomics, transcriptomics, proteomics, and metabolomics—accompanying generative AI principles allows for a complete view of disease progress and situation reaction, fostering a whole-level understanding of human physical science [10].
The union of generative AI accompanying different arising technologies, to a degree CRISPR deoxyribonucleic acid refining, wearable health sensors, and Internet of Medical Things (IoMT), is concreting the habit for unending, adaptive, and physical-period healthcare schemes [11]. For instance, AI-create models can predict wide belongings of CRISPR edits or advance guide RNA sequences for higher accuracy, making deoxyribonucleic acid refining safer and more direct. In parallel, unification accompanying wearable data admits fruitful models to pretend future health occurrences or plan interferences tailored to a patient’s corporal styles [12]. These uses, while transformational, highlight the indispensable need for multidisciplinary cooperation between calculating physicists, bioinformaticians, clinicians, and ethicists to guarantee that the deployment of fruitful AI is not only technically sound but too about the welfare of mankind responsible and psychologically impartial [13]
Research Methodology
This study employed a qualitative narrative review methodology to synthesize current literature on the applications, benefits, and challenges of generative artificial intelligence (AI) in life sciences and precision medicine. A comprehensive literature search was conducted using databases such as PubMed, Scopus, and Web of Science, focusing on peer-reviewed articles published between 2015 and 2024. Keywords used included “generative AI,” “life sciences,” “precision medicine,” “drug discovery,” “synthetic biology,” and “machine learning in healthcare.” Articles were screened based on relevance, novelty, and methodological rigor. In total, 74 studies were selected for final analysis. The review emphasizes technological trends, current applications, and future implications based on published empirical and theoretical research.
The review identified five major domains where generative AI is making a significant impact:
Drug Discovery and Molecular Design: Generative models such as GANs and VAEs are being used to design novel drug-like molecules with optimized ADMET (absorption, distribution, metabolism, excretion, and toxicity) profiles. Several studies demonstrated reduced lead optimization time and enhanced compound novelty.
Synthetic Biology and Genomics:AI-generated DNA/RNA sequences have been used to design promoters, optimize gene expression, and identify potential gene-editing targets.
Digital Twins and Personalized Medicine: Virtual patient simulations powered by generative models are being explored to test therapeutic responses, predict adverse effects, and individualize treatment regimens.
Medical Imaging and Diagnostics:Generative AI enhances radiological diagnostics by generating high-resolution synthetic images, aiding in disease classification, tumor segmentation, and rare case augmentation.
Clinical Trial Simulation and Population Modeling: Synthetic patient datasets are helping to simulate trial outcomes, improve patient stratification, and reduce the cost and duration of trials.
| Application Area | Description | Example Tools/Models | Benefits |
|---|---|---|---|
| Drug Discovery | Generation of novel molecular structures with desired properties | GANs, VAEs, DeepChem | Faster lead optimization |
| Genomic Engineering | Designing DNA/RNA sequences and optimizing gene expression | CRISPR-GAN, Geneformer | Precision in gene editing |
| Medical Imaging | Generation and enhancement of diagnostic images | CycleGAN, StyleGAN | Better diagnostic accuracy |
| Digital Twins & Virtual Patients | Simulating individual patient physiology and drug response | Neural ODEs, GFlowNet | Personalized treatment strategies |
| Clinical Trial Simulation | Generation of synthetic patient populations | SyntheticHealth, Synthea | Cost-effective trial design |
| Precision Diagnostics | Biomarker discovery and disease classification | BioBERT, DeepVariant | Improved early detection |
Table 1: Applications of Generative AI in Life Sciences and Precision Medicine
Description: Diagram showing three common model types:
Diagram Labels (Flowchart):
1. Data Sources
2. Data Preprocessing
3. AI Models
4. Fine-tuning
5. Applications
6. Outcomes
Source: Topol, E. (2019). High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine, 25(1), 44–56.
The integration of generative AI in the life sciences signifies a paradigm shift from static data interpretation to dynamic data creation and prediction. This transition allows for unprecedented exploration of biological complexity and therapeutic design. In drug discovery, AI systems like Deep Chem and Alpha Fold have already disrupted traditional pipelines by predicting molecular interactions and protein folding patterns with high accuracy. These advancements shorten the preclinical phase and reduce reliance on animal models.
In precision medicine, generative models enable the creation of virtual populations and predictive biomarkers, offering tailored treatment plans based on simulated responses. This can drastically reduce the trial-and-error approach in therapeutic interventions and enhance patient safety. Moreover, combining AI with high-throughput sequencing and wearable health data can yield real-time insights for chronic disease management and early detection.
However, several challenges remain. Data quality, algorithmic bias, interpretability, and regulatory uncertainty are critical barriers. While synthetic data addresses the scarcity of biological datasets, its utility depends on the realism and diversity of generated samples. Additionally, ethical concerns such as consent, privacy, and misuse must be addressed through comprehensive policies and cross-sector cooperation.
Generative AI is revolutionizing life sciences and precision medicine by enabling rapid drug discovery, enhanced diagnostics, and truly personalized care. As the technology matures, it offers the potential to overcome data limitations, improve healthcare outcomes, and reduce development costs. However, to fully realize these benefits, a multidisciplinary approach is needed—blending AI innovation with robust validation, ethical safeguards, and transparent governance. Future research should focus on developing explainable AI models, integrating multimodal biological data, and ensuring equity in access and application across global healthcare systems.
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