Integrating Multimodal Data with Large Foundation Models in Healthcare

1. Introduction

The intersection of artificial intelligence (AI) and healthcare has witnessed transformative advancements over the past decade, driven in large part by the development of increasingly sophisticated machine learning models. Among these, Large Foundation Models (LFMs)—a class of deep learning architectures characterized by their scale, general-purpose nature, and pretraining on massive datasets—have emerged as a groundbreaking paradigm with the potential to revolutionize medical analysis. These models, which include but are not limited to large language models (LLMs) such as GPT, BERT, PaLM, and multimodal architectures like CLIP, Flamingo, and Med-PaLM, have demonstrated exceptional capabilities across a variety of downstream tasks [1]. Originally designed for general-purpose natural language understanding and generation, LFMs have since been adapted and fine-tuned for an increasingly diverse set of clinical applications, ranging from medical image interpretation to patient record summarization, clinical decision support, biomedical literature mining, and even drug discovery [2]. The core principle behind LFMs is the use of transfer learning at an unprecedented scale. By training on broad and often heterogeneous corpora—spanning general web data, scientific literature, and structured clinical databases—these models acquire a robust and flexible representation of language, vision, or multimodal inputs [3]. Once pretrained, LFMs can be fine-tuned on smaller, task-specific datasets to achieve state-of-the-art performance on specialized medical tasks, often with significantly fewer labeled examples than traditional supervised approaches require [4]. This shift from task-specific, data-hungry models to generalist, data-efficient architectures has profound implications for medical AI, where high-quality labeled data is often scarce, expensive, or ethically challenging to obtain [5]. Moreover, the architecture of LFMs facilitates capabilities such as few-shot learning, zero-shot inference, and in-context learning, which allow models to generalize beyond their training distribution and perform novel tasks without explicit retraining [6]. In clinical settings, where variability in disease presentation, patient demographics, and diagnostic modalities is the norm rather than the exception, such generalization is not merely advantageous—it is essential [7]. LFMs also enable continual learning and domain adaptation, allowing them to remain current with evolving medical knowledge and guidelines, a critical feature in fast-moving domains such as oncology or infectious disease [8]. The promise of LFMs in medicine is further enhanced by the development of domain-specific variants trained on curated medical corpora [9]. Models such as BioBERT, ClinicalBERT, PubMedGPT, and Med-PaLM represent a growing ecosystem of biomedical LFMs that are designed to meet the stringent accuracy, interpretability, and safety requirements of clinical environments [10]. These models are increasingly being integrated into medical workflows, powering tools that assist radiologists in detecting anomalies, help clinicians interpret unstructured electronic health records (EHRs), and support researchers in synthesizing vast quantities of biomedical literature [11]. Despite these promising advances, the deployment of LFMs in real-world medical settings raises numerous technical, ethical, and regulatory challenges. Issues of bias, fairness, explainability, data privacy, and model robustness are particularly salient in healthcare, where decisions can have life-altering consequences. Furthermore, the black-box nature of many LFMs complicates their integration into clinical workflows that demand transparency, accountability, and rigorous validation. The computational cost and environmental footprint associated with training and maintaining these large-scale models also pose sustainability concerns, especially for resource-constrained healthcare systems [12]. In this survey, we aim to provide a comprehensive overview of the landscape of medical analysis using Large Foundation Models. We begin by categorizing the types of LFMs used in the biomedical domain and describing their architectural underpinnings [13]. We then examine the breadth of applications enabled by these models across various medical subfields, including radiology, pathology, genomics, pharmacology, and clinical informatics [14]. Special attention is given to the challenges and limitations associated with their deployment, with a discussion of ongoing research efforts aimed at improving interpretability, mitigating bias, and ensuring regulatory compliance. Finally, we explore emerging trends and future directions, including the integration of multimodal data sources, the use of federated learning and privacy-preserving techniques, and the development of next-generation foundation models tailored specifically for healthcare [15]. Through this survey, we seek to elucidate the transformative potential of LFMs in medicine, while also highlighting the critical considerations that must guide their responsible and equitable adoption [16]. As the field of medical AI moves toward increasingly general and powerful models, a clear understanding of the capabilities, limitations, and ethical implications of LFMs is essential for clinicians, researchers, developers, and policymakers alike [17]. We hope this work will serve as a foundational reference for the growing community at the intersection of AI and medicine, and as a catalyst for interdisciplinary collaboration in the design of safe, effective, and human-centered medical AI systems.

2. Theoretical Foundations of Large Foundation Models in Medical Analysis

Large Foundation Models (LFMs), particularly those rooted in transformer architectures, are fundamentally built upon the framework of representation learning, statistical inference, and optimization over high-dimensional function spaces [18]. In the context of medical analysis, these models serve as approximators of complex, often nonlinear mappings between input modalities (e.g., clinical text, radiological images, genetic sequences) and outputs that may include diagnostic labels, treatment recommendations, or patient outcome probabilities. The goal is to learn a parameterized function $f_{\theta }:\mathcal{X}\to \mathcal{Y}$, where $\mathcal{X}$ denotes a potentially multimodal input space and $\mathcal{Y}$ the output task space, with parameters $\theta \in {\mathbb{R}}^d$ optimized to minimize a task-specific loss function [19]. The training of LFMs typically begins with a self-supervised pretraining phase on a large corpus of unlabeled data [20]. Formally, let ${\mathcal{D}}_{\mathrm{pre}}={\left\{x_i\right\}}_{i=1}^N$ denote the pretraining dataset, where each $x_i\in \mathcal{X}$ may represent a tokenized medical sentence, a patch of a medical image, or a segment of structured EHR data [21]. A common pretraining objective is the masked language modeling (MLM) loss, defined as:

$${\mathcal{L}}_{\mathrm{MLM}}\left(\theta \right)=-{\mathbb{E}}_{x\sim {\mathcal{D}}_{\mathrm{pre}}}\sum _{t\in \mathcal{M}\left(x\right)}log{p}_{\theta }(x_t\mid x_{\setminus t}),$$

where $\mathcal{M}\left(x\right)$ denotes the set of masked positions in x, and $x_{\setminus t}$ is the input with the t-th token removed [22]. This objective encourages the model to learn bidirectional contextual representations, which can be transferred to downstream clinical tasks [23]. In multimodal settings, LFMs are trained to jointly model heterogeneous medical data modalities. Let $x=(x^{\left(1\right)},x^{\left(2\right)},\dots ,x^{\left(m\right)})$ be an input consisting of m modalities, such as $x^{\left(1\right)}$ for radiology images, $x^{\left(2\right)}$ for free-text reports, and $x^{\left(3\right)}$ for structured lab values [24]. The multimodal encoder seeks to learn a joint embedding space $\mathcal{H}\subset {\mathbb{R}}^k$ such that:

$$\mathbf{h}=\varphi (x^{\left(1\right)},x^{\left(2\right)},\dots ,x^{\left(m\right)};\theta ),$$

where $\varphi$ denotes the multimodal fusion function, typically instantiated as a cross-attention transformer or a contrastive encoder-decoder architecture. A popular objective in this context is the contrastive loss:

$${\mathcal{L}}_{\mathrm{contrast}}\left(\theta \right)=-log{\displaystyle \frac{exp(\mathrm{sim}(\varphi \left(x^{\left(i\right)}\right),\varphi \left(x^{\left(j\right)}\right))/\tau )}{{\sum }_{k=1}^{K}exp(\mathrm{sim}(\varphi \left(x^{\left(i\right)}\right),\varphi \left(x^{\left(k\right)}\right))/\tau )}},$$

where $\mathrm{sim}(·,·)$ denotes cosine similarity, $\tau$ is a temperature parameter, and K includes positive and negative pairs. In clinical settings, this may correspond to aligning a chest X-ray with its corresponding radiology report while distinguishing it from non-matching report-image pairs [25]. Once pretrained, the model is adapted to downstream medical tasks via fine-tuning [26]. Let ${\mathcal{D}}_{\mathrm{fine}}={\left\{(x_i,y_i)\right\}}_{i=1}^M$ be a task-specific labeled dataset, such as diagnosis codes for EHR entries. The fine-tuning objective is typically:

$${\mathcal{L}}_{\mathrm{task}}\left(\theta \right)={\displaystyle \frac{1}{M}}\sum _{i=1}^M\ell (f_{\theta }\left(x_i\right),y_i),$$

where ℓ is a task-dependent loss function, such as cross-entropy for classification or mean squared error for regression. Notably, the parameter vector $\theta$ is often initialized from the pretrained weights, enabling improved generalization in low-data regimes—especially critical in domains like rare disease diagnosis, where labeled samples are limited. Moreover, the probabilistic interpretation of LFMs allows for uncertainty quantification, which is indispensable in high-stakes medical applications [27]. Let $f_{\theta }\left(x\right)$ denote the predictive distribution over outcomes [28]. The model’s epistemic uncertainty can be estimated via Bayesian approximations, such as Monte Carlo dropout:

$$\mathbb{V}\left[f_{\theta }\left(x\right)\right]\approx {\displaystyle \frac{1}{T}}\sum _{t=1}^T{f}_{{\theta }_t}{\left(x\right)}^2-{\left({\displaystyle \frac{1}{T}}\sum _{t=1}^T{f}_{{\theta }_t}\left(x\right)\right)}^2,$$

where ${\theta }_t$ are sampled from a variational posterior over model weights. This estimation guides clinical users by signaling when a model is uncertain and may need human review or additional testing [29]. A further theoretical perspective comes from information theory and mutual information. The objective of pretraining can be viewed as maximizing the mutual information between inputs and learned representations:

$$\underset{\theta }{max}I(X;Z_{\theta }),$$

where $Z_{\theta }$ denotes the latent representation induced by the model [30]. In a medical context, maximizing this quantity ensures that critical diagnostic information (e.g., lesion presence, temporal changes in symptoms, biomarker anomalies) is preserved in the learned representations, thereby enhancing interpretability and utility in clinical decision-making [31]. Collectively, these mathematical formulations provide a rigorous foundation for understanding and advancing LFMs in medicine [32]. By leveraging self-supervised learning, multimodal fusion, probabilistic modeling, and information-theoretic principles, LFMs offer a powerful and generalizable framework for tackling a wide array of clinical challenges [33]. The next section explores how these models are operationalized across different medical domains and the empirical results that support their adoption.

3. Applications of Large Foundation Models in Medical Domains

The deployment of Large Foundation Models (LFMs) across diverse medical domains has resulted in substantial improvements in both predictive accuracy and workflow efficiency. These models, when fine-tuned or prompted appropriately, can be applied to a wide spectrum of healthcare challenges, including medical imaging interpretation, clinical text summarization, patient risk stratification, genomics, and personalized treatment planning [34]. Their generalizability enables transfer across tasks and modalities, reducing the need for developing bespoke solutions for each individual application.

Table 1. Representative Applications of Large Foundation Models in Medical Domains

Domain	Example LFM Models	Application Task	Performance
Radiology	CLIP, BioViL, GLoRIA, MedCLIP	Zero-shot or few-shot classification of pathologies in chest X-rays and CT scans [35]	Outperforms supervised baselines in low-label regimes; aligns image and report semantics effectively
Clinical NLP	BioBERT, ClinicalBERT, PubMedGPT, GatorTron	Named entity recognition (NER), relation extraction, clinical note summarization	Achieves state-of-the-art results on multiple clinical NLP benchmarks such as i2b2 and MIMIC-III
Health Records	RETAIN, Med-PaLM, BEHRT, TransformerEHR	Disease progression modeling, risk prediction, medication recommendation	Improves AUC/ROC and calibration in longitudinal patient modeling; captures temporal dependencies
Pathology	PaLM-E, HEAL, Vision Transformers	Whole slide image (WSI) classification, cancer subtype prediction	Enables WSI analysis without patch-level supervision

Table 1. Representative Applications of Large Foundation Models in Medical Domains

Domain	Example LFM Models	Application Task	Performance
Radiology	CLIP, BioViL, GLoRIA, MedCLIP	Zero-shot or few-shot classification of pathologies in chest X-rays and CT scans [35]	Outperforms supervised baselines in low-label regimes; aligns image and report semantics effectively
Clinical NLP	BioBERT, ClinicalBERT, PubMedGPT, GatorTron	Named entity recognition (NER), relation extraction, clinical note summarization	Achieves state-of-the-art results on multiple clinical NLP benchmarks such as i2b2 and MIMIC-III
Health Records	RETAIN, Med-PaLM, BEHRT, TransformerEHR	Disease progression modeling, risk prediction, medication recommendation	Improves AUC/ROC and calibration in longitudinal patient modeling; captures temporal dependencies
Pathology	PaLM-E, HEAL, Vision Transformers	Whole slide image (WSI) classification, cancer subtype prediction	Enables WSI analysis without patch-level supervision

In the domain of radiology, LFMs have been particularly effective in bridging the gap between imaging data and associated reports [36]. Models such as CLIP and GLoRIA use contrastive objectives to align visual and textual modalities, allowing for semantic understanding of image features without the need for dense annotations [37]. In practice, this enables radiologists to perform zero-shot labeling of findings such as pneumothorax or cardiomegaly from chest X-rays, a task previously reliant on supervised CNNs trained on labeled corpora like CheXpert or MIMIC-CXR. Furthermore, vision-language models can generate natural language explanations that mirror the style and structure of radiology reports, enhancing interpretability and clinician trust. Clinical natural language processing (NLP) has also seen transformative progress due to foundation models trained on biomedical text corpora [38]. Models such as BioBERT and ClinicalBERT, pretrained on PubMed abstracts and EHR notes, outperform previous baselines on tasks like named entity recognition (NER), relation extraction, and coreference resolution [39]. With the emergence of GPT-based models, zero-shot and few-shot learning have become practical in clinical settings, enabling question answering over unstructured clinical narratives or summarization of discharge notes with minimal task-specific data [40]. These capabilities are vital in environments with high documentation burdens and limited annotation resources. Electronic Health Record (EHR) modeling presents unique challenges due to its heterogeneous, temporal, and sparse nature. LFMs tailored for EHRs, such as BEHRT and TransformerEHR, are capable of modeling longitudinal sequences of coded medical events. These models treat visits as tokenized events and leverage self-attention mechanisms to capture temporal relationships and interdependencies between diagnoses, procedures, and medications [41]. They are used for risk prediction (e.g., predicting sepsis onset), disease progression modeling, and personalized treatment recommendation, often yielding improved metrics such as AUROC and precision-recall compared to traditional models like RNNs or logistic regression [42]. In pathology, the processing of gigapixel whole slide images (WSIs) has traditionally required tile-based training pipelines. LFMs based on vision transformers (ViTs), pretrained on general or medical image datasets, can now process these slides in a more holistic manner [43]. Moreover, multimodal models that integrate image and textual report data have been developed for cancer subtype classification, enabling explainable predictions and report generation. These models reduce the annotation burden and provide better alignment with clinical narratives [44]. Genomics is another frontier where LFMs show immense promise [45]. Models like DNABERT and Enformer adapt transformer architectures to sequence-based inputs, treating DNA as a structured language [46]. These models can predict gene regulatory effects, model enhancer-promoter interactions, and perform variant effect prediction by learning patterns in raw nucleotide sequences [47]. The ability to model long-range dependencies (e.g., >10kb) using self-attention mechanisms has led to breakthroughs in understanding gene expression regulation and the effects of non-coding variants [48]. The domain of drug discovery has been enhanced through molecular LFMs that embed chemical representations such as SMILES strings or molecular graphs [49]. Using transformer-based encoders and pretrained on millions of molecules, these models predict physical and biochemical properties, facilitate drug-target interaction modeling, and even generate novel drug-like compounds in silico. The incorporation of graph-based encoders such as GraphGPT allows models to better capture molecular topology, leading to higher accuracy in virtual screening tasks [50]. Lastly, LFMs are being deployed as generalist models for clinical decision support by integrating multiple modalities. Med-PaLM 2 and Flamingo are examples of such systems that process medical questions alongside image inputs and structured records [51]. These models achieve performance close to or surpassing that of domain experts in benchmark medical exams such as the USMLE [52]. By reasoning over images, labs, and text jointly, they provide holistic assessments that reflect real-world diagnostic processes. However, their deployment still requires careful validation due to the high stakes of clinical decision-making. These diverse applications collectively highlight the versatility and strength of LFMs in tackling some of the most pressing challenges in medical AI. In the following sections, we examine in greater detail the current limitations and risks associated with their use, particularly in relation to fairness, interpretability, and trustworthiness [53].

4. Architectural Paradigms of Large Foundation Models in Medical Analysis

The architecture of Large Foundation Models (LFMs) applied to medical analysis is both modular and hierarchical, designed to process, align, and reason across various data modalities [54]. These include clinical free-text, radiological images, structured tabular data from electronic health records (EHRs), and genomic sequences [55]. At a high level, a typical LFM pipeline consists of modality-specific encoders, a multimodal fusion module, a shared representation space, and task-specific heads [56]. This structure enables cross-modal information exchange while preserving domain-specific priors essential for accurate clinical reasoning [57]. At the input stage, raw data from diverse sources is first processed through domain-specific tokenization or embedding layers [58]. For instance, radiology images are divided into non-overlapping patches, each of which is flattened and projected into a latent space via a linear embedding layer, similar to the ViT paradigm [59]. Clinical text, on the other hand, is tokenized using subword or byte-pair encoding (BPE) schemes, which allow the model to generalize across varying terminologies and abbreviations. Structured EHRs may be converted into event sequences, where each token represents a timestamped medical code, lab test, or procedure [60]. The modality-specific encoders (e.g., CNNs for images, transformers for text and EHRs) transform these inputs into intermediate embeddings in a latent space ${\mathbb{R}}^d$ [61]. These embeddings are then passed into a fusion module—often a cross-attention transformer or a contrastive learning block—which aligns features from different modalities and learns joint representations. The resulting unified embedding captures semantic relationships across modalities, enabling the model to perform complex inference tasks such as diagnosis, prognosis, or triage with a holistic understanding of patient data.

Figure 1. Schematic architecture of a multimodal Large Foundation Model in medical analysis. Inputs from distinct medical data sources are encoded by modality-specific encoders, fused via cross-modal attention mechanisms, and projected into a unified latent representation.

Figure 1. Schematic architecture of a multimodal Large Foundation Model in medical analysis. Inputs from distinct medical data sources are encoded by modality-specific encoders, fused via cross-modal attention mechanisms, and projected into a unified latent representation.

The design of this architecture accommodates the inherent variability and granularity of healthcare data. Radiology images vary in resolution and modality (e.g., DICOM-based CT scans vs [62]. low-resolution X-rays), clinical notes are often unstructured and include domain-specific abbreviations, and EHRs contain sparse and asynchronous measurements [63]. LFMs address these issues by aligning modalities in a common embedding space, where semantic consistency is preserved across vastly different input types [64]. Another crucial feature of these architectures is their ability to support both pretraining and fine-tuning in modular ways [65]. Modality-specific encoders can be pretrained independently on domain-relevant tasks (e.g., image captioning or masked token prediction) and then jointly fine-tuned with fusion and output layers [66]. This decoupled training process not only facilitates scalable development but also promotes model reuse across institutions and tasks [67]. In clinical deployment scenarios, frozen encoders can be used in conjunction with lightweight adapters, enabling rapid domain adaptation without full retraining—a vital requirement in privacy-sensitive healthcare environments. Furthermore, these architectures increasingly incorporate interpretable attention mechanisms and saliency-based explainability tools. By visualizing attention weights across modalities, clinicians can inspect which parts of a radiology scan or which terms in a clinical note contributed most to a model’s prediction. This is essential for building trust and supporting human–AI collaboration in decision-making [68]. Taken together, this architectural paradigm provides the necessary flexibility, scalability, and transparency for applying LFMs to real-world medical tasks [69]. It enables both horizontal scaling (across institutions and populations) and vertical specialization (for rare diseases or personalized care), positioning LFMs as a central pillar of the next generation of medical AI systems.

5. Challenges and Ethical Considerations in Deploying Large Foundation Models in Medical Analysis

Despite the transformative potential of Large Foundation Models (LFMs) in advancing medical analysis, their deployment in clinical settings is fraught with multifaceted challenges and ethical considerations [70]. These range from technical obstacles inherent to the models themselves, to broader societal implications such as fairness, privacy, and trustworthiness. A thorough understanding of these issues is essential to responsibly harness LFMs in healthcare, ensuring that their benefits are equitably distributed and aligned with medical ethics [71]. One of the most pressing technical challenges stems from the complexity and opacity of LFMs [72]. Their sheer scale—often encompassing billions of parameters—and their training on heterogeneous, noisy data complicate interpretability [73]. While attention mechanisms provide some insight into the model’s decision-making process, these are often insufficient to fully explain predictions in a clinical context where stakes are high. The black-box nature of these models raises concerns about reliability, especially when deployed for diagnoses or treatment recommendations [74]. Moreover, LFMs can inadvertently learn and amplify biases present in training data. For instance, underrepresentation of minority populations or specific disease phenotypes in large biomedical corpora can lead to systematic disparities in model performance, thereby exacerbating healthcare inequities [75]. Addressing these issues requires rigorous evaluation protocols, development of robust uncertainty quantification methods, and integration of fairness constraints during training. Another significant challenge lies in the availability, quality, and privacy of medical data used to train LFMs. Medical data is inherently sensitive, regulated by frameworks such as HIPAA and GDPR, which restrict data sharing and complicate large-scale data aggregation. This scarcity and fragmentation of data can limit the diversity and representativeness of training sets, impacting generalizability [76]. Federated learning and differential privacy techniques offer promising avenues to mitigate these issues, allowing models to learn from distributed datasets without compromising patient confidentiality. However, these approaches introduce additional computational overhead and complexity, and their efficacy in large-scale clinical deployments remains an active area of research [77]. From an ethical standpoint, transparency and accountability are paramount when integrating LFMs into clinical workflows [78]. The delegation of decision-making to AI systems must not undermine the clinician’s authority or responsibility [79]. Instead, LFMs should function as assistive tools that augment human judgment, providing interpretable outputs and clear indications of confidence or uncertainty. Furthermore, there is an ethical imperative to ensure equitable access to these advanced technologies across healthcare settings, including under-resourced and rural areas. This involves not only technological deployment but also education and training for medical professionals to effectively utilize and critically evaluate AI-generated insights. Regulatory frameworks must evolve in tandem with the rapid development of LFMs to safeguard patient safety and promote ethical standards [80]. Current guidelines for medical devices and software are being adapted to accommodate AI’s continuous learning and data dependency. Transparency in model provenance, validation on diverse populations, and post-deployment monitoring are critical components of regulatory compliance [81]. Additionally, mechanisms for reporting and addressing adverse outcomes related to AI use need to be established, fostering an environment of continual improvement and accountability. Finally, the societal implications of deploying LFMs in medicine extend beyond clinical efficacy [82]. Issues such as data ownership, informed consent for AI-assisted care, and the potential for automation to disrupt clinical jobs warrant careful consideration [83]. Engaging diverse stakeholders—including patients, clinicians, ethicists, and policymakers—in the development and governance of medical LFMs is essential to align technological advances with societal values and patient-centered care principles [84]. In summary, while LFMs hold immense promise for revolutionizing medical analysis, realizing this potential requires addressing substantial technical, ethical, and regulatory challenges. Responsible development, transparent deployment, and inclusive governance are crucial to ensure that these powerful models enhance healthcare delivery without compromising fairness, privacy, or human dignity.

6. Future Directions and Research Opportunities in Large Foundation Models for Medical Analysis

The field of Large Foundation Models (LFMs) in medical analysis is rapidly evolving, with numerous promising avenues for future research that can further unlock their transformative potential while addressing existing limitations. As LFMs mature, interdisciplinary collaboration between machine learning researchers, clinicians, and ethicists will be critical to steer innovation towards impactful and responsible applications in healthcare [85]. This section outlines key future directions and research opportunities that will likely shape the next generation of medical LFMs. One major direction is the development of more efficient and scalable training methods tailored to the unique challenges of medical data [86]. Current LFMs require enormous computational resources and vast amounts of labeled data, which are often scarce in the medical domain [87]. Research into data-efficient learning paradigms such as self-supervised learning, continual learning, and few-shot adaptation holds promise to reduce these demands [88]. For instance, leveraging large volumes of unlabeled medical imaging and textual data through masked token prediction or contrastive learning can provide rich pretraining signals that improve downstream task performance with limited annotation. Additionally, exploring modular architectures that allow selective updating of model components can enable lifelong learning from incremental clinical data without catastrophic forgetting, an important capability for adapting to evolving medical knowledge and practices [89]. Another fruitful area of investigation is enhancing the interpretability and explainability of LFMs in clinical settings [90]. Bridging the gap between high model complexity and human-understandable insights remains a fundamental challenge [91]. Future research could focus on developing novel attention visualization tools, causal inference frameworks, and counterfactual explanations that provide clinicians with actionable rationale behind model predictions [92]. Embedding interpretability objectives directly into the training process may also yield models that are inherently more transparent. Furthermore, integrating domain knowledge, such as clinical guidelines or biological pathways, into model architectures could produce explanations grounded in medical reasoning rather than purely data-driven correlations. Improving fairness and mitigating bias in medical LFMs represent another critical frontier [93]. Future work should focus on developing robust methods for detecting and correcting disparities across demographic groups, disease subtypes, and healthcare systems [94]. Techniques such as adversarial debiasing, fairness-aware loss functions, and synthetic data augmentation can be adapted and extended for the complexities of medical data. Moreover, establishing standardized benchmarks and datasets that reflect diverse patient populations is essential to fairly evaluate and compare model performance. Ethical frameworks and community-driven governance structures will be needed to ensure equitable model development and deployment, particularly for marginalized and underserved groups [95]. Multimodal learning will continue to be a pivotal area of growth, as integrating heterogeneous data sources unlocks richer clinical insights [96]. Future research may explore advanced fusion strategies that dynamically weight modalities based on data quality, clinical context, and uncertainty [97]. Incorporating real-time streaming data such as continuous vital sign monitoring alongside static modalities like imaging and genomics can enable more precise and timely clinical interventions. Additionally, the expansion of LFMs to incorporate emerging data types—such as wearable sensor data, digital pathology, and patient-reported outcomes—will broaden their applicability and relevance in personalized medicine [98]. The intersection of LFMs with privacy-preserving technologies also offers fertile ground for innovation [99]. Techniques like federated learning, homomorphic encryption, and secure multiparty computation could be refined to accommodate the scale and complexity of foundation models, allowing collaborative model training across institutions without compromising patient confidentiality [100]. Developing practical frameworks and tools to balance privacy, model utility, and regulatory compliance will be essential for widespread adoption [101]. Finally, integrating LFMs into clinical workflows demands robust evaluation protocols and human–AI interaction research. Future studies should rigorously assess how clinicians interpret and act on model outputs, the impact on diagnostic accuracy and patient outcomes, and potential unintended consequences [102]. Designing user interfaces that seamlessly incorporate AI assistance and provide meaningful feedback loops can foster trust and effective collaboration [103]. Moreover, developing adaptive models that learn from clinician corrections and feedback in real time could continuously improve system performance and user satisfaction [104]. In conclusion, the future of LFMs in medical analysis is rich with opportunities for innovation that can enhance healthcare delivery, diagnostics, and patient outcomes [105]. Addressing current challenges through targeted research and multidisciplinary cooperation will be pivotal in realizing the full potential of these powerful models while upholding ethical standards and clinical relevance [106].

7. Conclusion

Large Foundation Models (LFMs) have emerged as a groundbreaking paradigm in medical analysis, offering unprecedented capabilities in processing and interpreting complex, multimodal healthcare data. Their ability to learn rich, generalizable representations from vast amounts of unstructured and structured data has unlocked new possibilities across a diverse array of clinical tasks, ranging from imaging interpretation and natural language processing to genomics and drug discovery. This survey has explored the foundational architectures, applications, challenges, and future directions associated with LFMs, highlighting their potential to revolutionize modern medicine by augmenting clinical decision-making and improving patient outcomes.

Despite their impressive performance and versatility, LFMs also bring significant challenges that cannot be overlooked. The technical hurdles of model interpretability, data scarcity, bias, and computational expense must be addressed to ensure safe and effective deployment in clinical environments. Moreover, the ethical and regulatory dimensions surrounding privacy, fairness, transparency, and accountability are critical to gaining trust from both clinicians and patients. Careful attention to these considerations will be essential in guiding the responsible integration of LFMs into healthcare systems worldwide.

Looking forward, the continued evolution of LFMs will depend on collaborative efforts that bridge the fields of artificial intelligence, medicine, ethics, and policy. Advances in scalable training techniques, interpretable model design, and privacy-preserving learning will drive the development of more robust and equitable medical AI systems. Additionally, embedding human-centered design principles and fostering effective human–AI collaboration will ensure that these technologies serve as reliable partners to healthcare professionals rather than opaque replacements. The dynamic nature of medical knowledge and patient populations calls for adaptive LFMs that can learn continuously and respond to emerging health challenges in real time.

In essence, LFMs represent both a technological leap and a profound opportunity to transform healthcare delivery. Their ability to synthesize heterogeneous data and provide nuanced clinical insights promises to enhance diagnostic accuracy, streamline workflows, and enable personalized treatment strategies. By confronting the attendant challenges with rigorous research, ethical foresight, and stakeholder engagement, the medical community can harness the full power of LFMs to improve health outcomes and equity on a global scale. This ongoing journey underscores a new era where artificial intelligence and human expertise converge to redefine the future of medicine.