Revolutionizing personalized medicine with generative AI: a systematic review

Adhering to Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) (Page et al. 2021) guidelines, the protocol for this paper was drafted and the review was conducted thoroughly. The approach involves a structured and transparent method for gathering, evaluating, and synthesizing the research study.

From the initial search of the two electronic databases retrieved 481 citations, we meticulously applied our selection criteria. Following the removal of duplication and title/abstract screening, 407 were excluded. A close examination of the 50 remaining full-text articles resulted in the further exclusion of 21, as depicted in Fig. 4. Ultimately, only 29 studies aligned with our stringent criteria and were included in this systematic review.

As shown in Table 2 it is evident from the increasing number of publications in this domain, rising from one article in 2019 to 17 articles in 2023. The global nature of this research is highlighted by the diverse geographic distribution of publications, with leading contributions from the United States and China. All the included publications that have been reviewed are journal articles with the majority focusing on applications in clinical informatics (31%), medical imaging (28%), and bioinformatics (31%).

DGMs are advancing in clinical informatics, including the generation of synthetic patient data, data anonymization, and the development of predictive models, contributing significantly to realizing the potential of precision medicine while addressing privacy and ethical concerns. These models also contribute significantly to advancing our understanding of complex biological processes, enabling personalized medicine, and improving treatment outcomes across various diseases. Meanwhile, in imaging informatics, the convergence of generative AI and medical imaging is propelling advancements in disease prognosis and diagnosis, exemplified by studies in pulmonary imaging, neuroimaging, retinal imaging, and cancer molecular imaging. These breakthroughs underscore the transformative potential of informatics and advanced technologies in shaping the future of healthcare and personalized medicine. In bioinformatics, the spotlight is mainly on VAEs and GANs, but also foundation models like LLMs for gene prioritization and knowledge-driven candidate selection, highlighting the capability of these models in uncovering hidden patterns within multi-omics data. Furthermore, the integration of LLMs in precision oncology and radiation oncology presents a paradigm shift in decision support for healthcare practitioners, offering complementary insights and potentially enhancing individualized clinical decision-making.

Table 3 provides an overview of the 29 included papers, presenting the applied DGMs or LLMs along with their modified or underlying generative model and the focus of their application.

A diverse array of generative models is tailored for personalized clinical, biomedical, and healthcare applications. These models, such as omicsGAN, GANITE, Dr.VAE, and innovativeGAN architectures with attention mechanisms, demonstrate their efficacy in tasks ranging from cancer phenotype classification to drug response prediction. Notably, they contribute to data generation, simulation of rare scenarios, and personalized experiments in a virtual environment. These models exhibit capabilities in handling high-dimensional biological data, predicting disease progression, and generating synthetic healthcare data for translational research. In imaging informatics generative models such as are U-HPNet model are designed to forecast lung nodule progression, addressing uncertainties in medical images and radiologist annotations. For brain tumor growth prediction, the Growth Prediction (GP-GAN) method utilizes stacked 3D generative adversarial networks, outperforming existing models in accuracy. In Alzheimer's and mild cognitive impairment, BrainStatTrans-GAN and GANCMLAE are proposed to decode individualized brain atrophy patterns, providing a crucial approach for more personalized interventions. Other studies introduced ccGAN for imputing missing values in EHR datasets, excelling in diabetic retinopathy detection, while others proposed a semi-supervised method using graph representations and GANs to generate synthetic data, outperforming alternative approaches with minimal labels. Generative models, such as MixEHR-G, a Bayesian topic model facilitating automatic phenotyping in large-scale EHR data, have proven effective in predicting phenotypes and revealing associations between diseases. In data anonymization, ADS-GAN and extended GAN-based approaches demonstrated anonymization for general health data, ensuring patient confidentiality and preserving data characteristics while maintaining identifiability. For prognosis, modified generative models such as SCAN, a semi-supervised cancer prognosis classifier, and GluGAN, for personalized glucose time series data generation, have shown promise in enhancing glucose prediction algorithms, respectively. In counterfactual explanations, SCGAN has provided sparse, diverse, and plausible explanations for breast cancer patient response predictions, contributing to interpretability and causal understanding. These advancements collectively drive progress in precision medicine, clinical research, and healthcare data privacy. Additionally, the review highlights the potential of advanced foundation models like ChatGPT-4 in medical decision-making and proposes standardized workflows for integrating large language models into candidate gene prioritization processes. The collective impact of these models spans diverse aspects of healthcare, from molecular interactions to patient prognosis and diagnostic support.

Challenges in the present ecosystem of deep generative modes and foundation models like LLMs in precision medicine are underscored by their limitations. These challenges involve issues such as restricted generalizability, insufficient model evaluation metrics, and a lack of external validation across diverse datasets. The intricacies of model architectures and their dependencies on data quality and representativeness present additional hurdles, along with concerns about training process stability, potential biases stemming from unobserved confounders, and the risk of overfitting. Additionally, the limited consideration of multi-omics complexities, sparse modeling techniques, and a focus on specific diseases emphasize the necessity for improvements in the applicability, interpretability, and clinical validation of these models for practical use in healthcare settings. These limitations of the studies highlight the complexities and challenges faced in various scientific and medical research endeavors, emphasizing the ongoing need for careful consideration, validation, and improvement in generative methodologies. Table 5 provides a comprehensive overview of the strengths and limitations of each generative model. Addressing these limitations and implementing the suggested recommendations will contribute to the advancement of generative AI and its potential for personalized clinical applications is provided in Table 6.

This systematic review is subject to several limitations that warrant acknowledgment. Firstly, there is a possibility that the review might have overlooked certain publications that could contribute valuable insights and data pertinent to the scope of the study. The vast and continually evolving nature of medical literature poses a challenge, and despite comprehensive search strategies, some relevant studies may have been inadvertently omitted. Recognizing these limitations is crucial for a subtle interpretation of the review's findings and underscores the importance of future updates or supplementary reviews conducted with the input of medical professionals to enhance the comprehensiveness and accuracy of the analysis.

In navigating the future trajectory of research, numerous pathways emerge with the potential to transcend current boundaries and rectify existing limitations. These avenues necessitate a collaborative effort to confront prevailing constraints, bolster model robustness, enhance generalizability, and advocate for the conscientious and effective utilization of DGMs and FMs in precision medicine. One key direction involves cultivating enhanced generalizability through the development and training of models on diverse datasets encompassing a spectrum of cancer types and diseases. Moreover, adopting quantitative metrics beyond accuracy scores and emphasizing external validation on independent datasets becomes imperative for rigorous evaluation. Simultaneously, prioritizing model interpretability, investing in research to elucidate generative model decision-making processes, and fostering transparency cater to the needs of healthcare practitioners. This involves simplifying model architectures, implementing standardized validation procedures, and addressing challenges related to data quality to contribute to the reliability and accuracy of models, facilitating their adoption in clinical settings. The integration of multi-omics data and an understanding of system biology expands the depth of disease comprehension, ensuring clinical applicability through validation and focusing on ethical considerations, patient privacy, and continuous evaluation underscore the commitment to responsible model deployment. Strategies such as heterogeneous data utilization, improved model robustness, and interdisciplinary collaboration further fortify the models' efficacy. Furthermore, the exploration of advanced architectures, refined validation criteria, disease-specific tailoring, and real-world implementation considerations contributes to the evolution of generative models. Continuous learning from new data, advancements in foundation models for medical imaging, exploration of new model paradigms, and human-in-the-loop approaches propel the field forward, anticipating and addressing challenges for a comprehensive and impactful advancement in healthcare technology.