Ethical AI in medical text generation: balancing innovation with privacy in public health

Introduction: The integration of artificial intelligence (AI) into medical text generation is transforming public health by enhancing clinical documentation, patient education, and decision support. However, the widespread deployment of AI in this domain introduces significant ethical challenges, including fairness, privacy protection, and accountability. Traditional AI-driven medical text generation models often inherit biases from training data, resulting in disparities in healthcare communication across different demographic groups. Moreover, ensuring patient data confidentiality while maintaining transparency in AI-generated content remains a critical concern. Existing approaches either lack robust bias mitigation mechanisms or fail to provide interpretable and privacy-preserving outputs, compromising ethical compliance and regulatory adherence.

Methods: To address these challenges, this paper proposes an innovative framework that combines privacy-preserving AI techniques with interpretable model architectures to achieve ethical compliance in medical text generation. The method employs a hybrid approach that integrates knowledge-based reasoning with deep learning, ensuring both accuracy and transparency. Privacy-enhancing technologies, such as homomorphic encryption and secure multi-party computation, are incorporated to safeguard sensitive medical data throughout the text generation process. Fairness-aware training protocols are introduced to mitigate biases in generated content and enhance trustworthiness.

Results and discussion: The proposed approach effectively addresses critical challenges of bias, privacy, and interpretability in medical text generation. By combining symbolic reasoning with data-driven learning and embedding ethical principles at the system design level, the framework ensures regulatory alignment and improves public trust. This methodology lays the groundwork for broader deployment of ethically sound AI systems in healthcare communication.

1 Introduction

The increasing use of artificial intelligence (AI) in medical text generation has revolutionized public health communication, clinical documentation, and patient education (1). Not only does AI-driven medical text generation improve efficiency in handling vast amounts of health data, but it also enhances accessibility by providing accurate and timely medical information (2). Moreover, AI models have shown the ability to bridge language barriers, making healthcare more inclusive (3). However, the sensitive nature of medical data introduces significant ethical challenges, including patient privacy, data security, and bias mitigation (4). Ensuring ethical AI deployment in this field requires a delicate balance between innovation and privacy protection, as mishandling such information could lead to severe consequences such as loss of trust, regulatory violations, and potential harm to individuals (5). Given the complexity of these challenges, research efforts have focused on different methodological approaches over time to enhance AI-driven medical text generation while safeguarding ethical standards (6). To address the limitations of early medical text generation methods, researchers initially relied on symbolic AI and knowledge representation techniques (7). These traditional approaches leveraged rule-based systems and expert-defined ontologies to generate structured and accurate medical text (8). By encoding medical knowledge in logical frameworks, these methods ensured transparency, interpretability, and compliance with regulatory standards (9). However, rule-based systems suffered from rigidity and could not generalize beyond predefined scenarios, limiting their scalability (10). Furthermore, these approaches required extensive manual effort to construct and maintain knowledge bases, making them inefficient for real-world applications where medical knowledge evolves rapidly (11). Despite these limitations, symbolic AI played a crucial role in establishing the foundation for ethical medical text generation, particularly in ensuring explainability and trustworthiness (12).

To overcome the rigidity of symbolic AI, researchers turned to data-driven approaches and machine learning techniques (13). These models utilized statistical learning and supervised learning algorithms trained on large datasets of medical texts (14). By extracting patterns from real-world data, machine learning methods significantly improved text generation quality and adaptability (15). These models reduced the manual burden of encoding knowledge and allowed for automated content generation in diverse medical contexts (16). Nevertheless, concerns regarding data privacy and bias have become prominent, as machine learning models have learned from historical records that may contain sensitive patient information or reflect systemic biases. Ethical challenges arose regarding the potential propagation of misinformation, the necessity of de-identification techniques, and the risk of model hallucination (17). While machine learning approaches introduced adaptability and efficiency, they also heightened the need for robust privacy-preserving mechanisms and fairness-aware model training. The advent of deep learning and pre-trained language models, such as transformer-based architectures, has further advanced medical text generation (18). These models leverage vast corpora of medical literature, clinical notes, and patient interactions to generate highly coherent and context-aware medical text. Notably, techniques such as federated learning, differential privacy, and bias mitigation strategies have been integrated into modern AI systems to address ethical concerns (19). Deep learning models enable scalable and dynamic text generation, enhancing the accuracy and personalization of AI-driven medical communication (20). However, challenges remain in ensuring that these models comply with regulatory frameworks such as HIPAA and GDPR, preventing unintended privacy breaches, and maintaining fairness in medical decision-making (21). Furthermore, the black-box nature of deep learning models raises concerns about explainability and accountability, which are crucial for building trust in AI-generated medical content (22).

Given the limitations of previous approaches, we propose a novel framework that strikes a balance between innovation and privacy in medical text generation. Our method integrates privacy-preserving AI techniques with interpretable model architectures to ensure ethical compliance. We employ a hybrid approach that combines knowledge-based reasoning with deep learning to maintain both accuracy and transparency. By incorporating privacy-enhancing technologies such as homomorphic encryption and secure multi-party computation, our model ensures that sensitive medical data remains protected throughout the text generation process. We introduce fairness-aware training protocols to mitigate biases in generated content and enhance the trustworthiness of the output. This approach addresses critical ethical concerns while enabling AI to drive advancements in public health communication, making it a robust solution for real-world applications.

The proposed method has several key advantages:

• Our method incorporates homomorphic encryption and federated learning to ensure that patient data remains confidential while enabling AI-driven medical text generation. This enhances data security and regulatory compliance without compromising performance.

• Unlike traditional deep learning models, our approach integrates explainable AI techniques, ensuring that medical professionals and patients can understand and validate AI-generated content. Fairness-aware training reduces biases and promotes ethical medical communication.

• Experimental results demonstrate that our method achieves superior text quality while maintaining privacy guarantees. Compared to existing models, our approach reduces privacy risks by 40% while improving text coherence and factual accuracy, making it a reliable solution for ethical AI-driven medical text generation.

2 Related research

2.1 Ethical challenges in AI-generated medical texts

The integration of artificial intelligence (AI) in medical text generation introduces significant ethical challenges that must be carefully addressed to ensure patient safety and the reliability of medical information (23). One of the primary concerns is the potential for AI-generated texts to contain inaccuracies or misleading information, which could negatively impact clinical decision-making and patient care. AI-powered medical transcription and summarization tools, for instance, have been reported to fabricate or hallucinate content that was not present in the original consultations (24). Such discrepancies can lead to miscommunication between healthcare providers and patients, potentially resulting in incorrect diagnoses, inappropriate treatments, or loss of trust in medical professionals. Another critical ethical issue is the question of authorship and accountability. When AI systems generate medical content, it becomes challenging to determine who bears responsibility for errors or misinformation—whether it is the developers, healthcare institutions, or the end-users (25). This lack of clear accountability raises legal and ethical concerns, particularly in high-stakes medical environments where erroneous information could have life-threatening consequences. AI models trained on biased datasets risk perpetuating or amplifying existing disparities in healthcare. If training data disproportionately represent certain demographics, AI-generated medical texts may reinforce biases in clinical recommendations, leading to unequal treatment outcomes across different patient groups. Addressing these concerns requires rigorous validation of AI-generated content, continuous monitoring for biases, and the implementation of robust accountability frameworks to maintain the integrity of medical information (26). Ethical deployment of AI in medical text generation must prioritize transparency, human oversight, and adherence to regulatory guidelines to safeguard patient welfare and uphold medical standards.

2.2 Balancing innovation and privacy in health data utilization

The advancement of artificial intelligence (AI) in healthcare is increasingly dependent on the extensive utilization of patient health data. AI-driven models have the potential to revolutionize medical diagnosis, predictive analytics, and personalized treatment plans by uncovering complex patterns in large-scale datasets. However, this innovation comes with significant challenges in maintaining patient privacy and ensuring ethical data handling (27). As health data often contain highly sensitive personal information, unauthorized access or misuse can lead to severe ethical, legal, and social implications. Striking a balance between leveraging AI for medical advancements and protecting individual privacy is crucial for fostering trust in AI-driven healthcare solutions. To address these concerns, the collection, storage, and analysis of health data must comply with stringent privacy regulations, such as the Health Insurance Portability and Accountability Act (HIPAA) and the General Data Protection Regulation (GDPR). Implementing robust data governance frameworks is essential for ensuring that patient information is handled responsibly and securely (28). Privacy-preserving techniques, such as data anonymization, differential privacy, and federated learning, offer effective strategies for minimizing privacy risks while enabling the beneficial use of data in AI applications (29). These approaches ensure that AI models can learn from health data without exposing personally identifiable information. Beyond technical safeguards, patient engagement plays a crucial role in the ethical utilization of health data. Transparent communication about how patient data is collected, processed, and utilized in AI-driven healthcare applications is crucial for maintaining public trust. Obtaining informed consent and allowing individuals greater control over their health data usage can help ensure that the benefits of AI innovations do not come at the expense of personal privacy rights (29). As AI continues to reshape healthcare, maintaining this delicate balance between innovation and privacy will be fundamental to building ethical and sustainable AI-driven medical systems.

2.3 Regulatory and legal considerations in AI deployment

The deployment of artificial intelligence (AI) in healthcare necessitates careful consideration of regulatory and legal frameworks to ensure that AI-driven technologies operate ethically, safely, and within the boundaries of the law (30). Given the sensitive nature of medical data and the high stakes involved in clinical decision-making, AI systems must comply with existing data protection regulations, such as the Health Insurance Portability and Accountability Act (HIPAA) and the General Data Protection Regulation (GDPR), to safeguard patient privacy and prevent data breaches (31). These regulations establish strict guidelines on data collection, processing, and sharing, ensuring that AI applications do not compromise patient confidentiality or expose sensitive health information to unauthorized entities. Beyond data protection, the deployment of AI in healthcare presents unique legal challenges, particularly concerning liability in cases of AI-induced errors. When AI-driven diagnostic or treatment recommendation systems make incorrect predictions, determining legal responsibility becomes complex—whether the liability falls on the healthcare provider, AI developer, or medical institution remains a subject of ongoing legal debate (32). Concerns have been raised regarding the potential for AI to engage in the unauthorized practice of medicine, especially in cases where AI systems provide clinical guidance without direct human oversight. Regulatory bodies worldwide are intensifying their scrutiny of AI applications in healthcare to ensure compliance with ethical standards and legal requirements (33). Authorities have issued warnings to healthcare providers and technology developers about the importance of responsible AI implementation, emphasizing the need to mitigate algorithmic bias, prevent discrimination, and uphold patient rights (34). Some jurisdictions are also considering the introduction of AI-specific regulatory policies, which would require rigorous validation and certification of AI models before their deployment in clinical settings. To facilitate the ethical and legal integration of AI in healthcare, it is essential to establish comprehensive policies and regulatory frameworks that address these challenges. This includes developing standardized evaluation metrics for AI model performance, ensuring transparency in AI decision-making processes, and enforcing accountability mechanisms for AI-related medical errors (35). As AI continues to transform the healthcare landscape, a well-defined regulatory approach will be crucial in fostering trust, ensuring patient safety, and enabling the responsible use of AI-driven medical innovations.

3 Method

3.1 Overview

The integration of artificial intelligence (AI) into healthcare has introduced significant advancements in diagnostics, treatment planning, and patient management. However, this integration raises critical ethical concerns that must be addressed to ensure the responsible and equitable deployment of AI technologies. A key ethical concern in AI-driven healthcare is patient autonomy, as discussed in Section 3.2. AI algorithms influence medical decision-making by providing diagnostic recommendations and treatment options. However, it is essential to ensure that these systems support, rather than replace, human judgment. The balance between AI-driven recommendations and clinician expertise raises questions about informed consent and patients' ability to challenge AI-generated decisions. This issue is closely tied to the principle of transparency, as understanding how AI models arrive at their conclusions is crucial for both medical professionals and patients.

Another major ethical challenge is the issue of bias and fairness in AI systems. AI models are trained on historical medical data, which may reflect existing biases in healthcare practices. If not properly addressed, these biases can lead to disparities in healthcare outcomes, disproportionately affecting underrepresented or vulnerable populations. The ethical imperative of fairness necessitates rigorous bias detection and mitigation strategies in AI model development, as will be discussed in Section 3.3. Accountability and liability in AI-driven healthcare remain unresolved ethical and legal questions. When an AI system provides incorrect diagnoses or suboptimal treatment recommendations, determining responsibility—whether it lies with the developers, healthcare providers, or the AI itself—becomes complex. This challenge underscores the need for well-defined regulatory frameworks, which will be explored in Section 3.4.

While existing studies on ethical AI often treat fairness, privacy, and transparency as isolated constraints or add-ons during post-processing, our approach is unique in that it embeds these ethical principles directly into the learning objective as regularization terms. We formulate a multi-objective optimization problem that simultaneously minimizes prediction loss while penalizing disparities (fairness), reducing information leakage (privacy), and aligning with interpretable models (transparency). This integrated modeling is not only theoretically grounded—with formal definitions of fairness via equalized odds and demographic parity, privacy via differential privacy guarantees, and transparency via surrogate modeling and Shapley values—but also practically efficient. Unlike prior frameworks that rely solely on empirical rebalancing or rule-based filters, our model dynamically adjusts its ethical trade-offs using an adaptive penalty mechanism and ethical drift monitoring. The proposed Ethical Risk Function, defined as a composite of fairness, privacy, and transparency risks, introduces a principled decision criterion for model deployment and retraining. Compared to conventional approaches, such as DEXPERTS or adversarial de-biasing models that handle fairness in isolation, our ethically constrained AI model (ECAM) enables the coupling of ethical dimensions, allowing for richer and more robust ethical compliance in real-world clinical applications.

3.2 Preliminaries

The deployment of artificial intelligence (AI) in healthcare necessitates a rigorous formalization of ethical concerns to ensure fairness, accountability, privacy, and transparency. This section introduces a structured framework to mathematically represent these concerns, enabling their systematic analysis and mitigation in AI-driven medical decision-making.

Let $\mathcal{D}={\left\{(x_i,y_i)\right\}}_{i=1}^n$ represent a healthcare dataset, where $x_i\in {ℝ}^d$ denotes patient features, and y_i ∈ 𝕐 represents the corresponding medical outcome. An AI model $f_{\theta }:{ℝ}^d\to 𝕐$ is trained to approximate the function y = f^*(x) that maps patient data to medical outcomes. Ethical concerns in AI-driven healthcare can be mathematically formulated through fairness, privacy, transparency, and accountability. Fairness can be defined by ensuring that model predictions are independent of sensitive attributes such as race, gender, or socioeconomic status. Let s_i ∈ 𝕊 denote sensitive attributes. One common criterion is demographic parity, expressed as follows:

$\begin{array}{ll}P(f_{\theta }(x)=y\mid s=s_1)=P(f_{\theta }(x)=y\mid s=s_2),\forall s_1,s_2\in 𝕊.& (1)\end{array}$

Transparency and interpretability are essential for ensuring trust and informed decision-making in healthcare AI systems. This can be achieved by approximating a complex model f_θ with an interpretable surrogate function g, such that

$\begin{array}{ll}\parallel f_{\theta }(x)-g(x)\parallel \le \tau ,\forall x\in \mathcal{D},& (2)\end{array}$

where τ represents the permissible approximation error. Shapley values offer a game-theoretic approach to feature importance attribution, calculated as follows:

$\begin{array}{ll}{\varphi }_j={\displaystyle \sum _{S\subseteq \left\{1,\dots ,d\right\}\backslash \left\{j\right\}}}\frac{\left|S\right|!(d-\left|S\right|-1)!}{d!}\left[v(S\cup \left\{j\right\})-v(S)\right],& (3)\end{array}$

where v(S) represents the model's predictive performance when using only features in subset S. Accountability and liability in AI-driven medical decision-making can be defined by assessing whether an erroneous prediction results in potential harm to patients. Given a model's prediction f_θ(x_i) and the corresponding ground truth y_i, an accountability function $\mathcal{A}:{ℝ}^d\times 𝕐\to \left\{0,1\right\}$ determines liability as follows:

$\begin{array}{ll}\mathcal{A}(x_i,y_i)=\{\begin{array}{ll}1,& \text{if }\mathcal{L}(f_{\theta }(x_i),y_i)>{\tau }_{\text{error}},\\ 0,& \text{otherwise},\end{array}& (4)\end{array}$

where $\mathcal{L}$ is a loss function, and τ_error is a threshold that defines harmful predictions.

To achieve ethical AI deployment, a multi-objective optimization problem is defined that balances predictive accuracy with fairness, privacy, and transparency. This is formulated as follows:

$\begin{array}{l}\underset{\theta }{\mathrm{min }}{𝔼}_{(x,y)~\mathcal{D}}[ℒ(f_{\theta }(x),y)+{\lambda }_1{ℛ}_{\text{fair}}(f_{\theta })+{\lambda }_2{ℛ}_{\text{priv}}(f_{\theta })\\ +{\lambda }_3{ℛ}_{\text{transp}}(f_{\theta })],& (5)\end{array}$

where ${\mathcal{R}}_{\mathrm{\text{fair}}}$, ${\mathcal{R}}_{\mathrm{\text{priv}}}$, and ${\mathcal{R}}_{\mathrm{\text{transp}}}$ represent regularization terms for fairness, privacy, and transparency, respectively. The parameters λ₁, λ₂, and λ₃ control the trade-offs between these ethical considerations and the model's predictive performance.

3.3 Ethically constrained AI model for healthcare

To address the ethical challenges in AI-driven healthcare, we propose a novel model (as shown in Figure 1), denoted as the ethically constrained AI model (ECAM), which explicitly incorporates fairness, privacy, and transparency constraints into the learning process. This model ensures that AI-assisted medical decisions remain unbiased, interpretable, and privacy-preserving while maintaining high clinical efficacy.

Figure 1

Figure 1. Illustration of the ethically constrained AI model (ECAM) framework, demonstrating privacy protection through Discrete Cosine Transform (DCT) and channel attention, and transparency enhancement via global and adaptive average pooling with learnable modulation. The architecture ensures secure, interpretable, and fair AI-driven decision-making in healthcare.

3.3.1 Fairness enforcement

To mitigate bias, we impose a fairness constraint using the equalized odds criterion (as shown in Figure 2), ensuring that predictions are independent of sensitive attributes given the true label. Fairness in AI-driven healthcare systems is critical to prevent discrimination against underrepresented groups, which may result from historical biases embedded in training data. Given a healthcare dataset $\mathcal{D}={\left\{(x_i,y_i,s_i)\right\}}_{i=1}^n$, where x_i represents patient features, y_i denotes medical outcomes, and s_i captures sensitive attributes such as race, gender, or socioeconomic status, fairness is achieved by ensuring that the prediction f_θ(x) remains consistent across subgroups defined by s_i. We define the fairness regularization term as follows:

$\begin{array}{l}{\mathcal{R}}_{\text{fair}}(f_{\theta })={\displaystyle \sum _{s_1,s_2\in 𝕊}}{\displaystyle \sum _{y\in 𝕐}}|P(f_{\theta }(x)=y\mid s=s_1)\\ -P(f_{\theta }(x)=y\mid s=s_2)|,& (6)\end{array}$

where 𝕊 represents the set of sensitive attributes and 𝕐 represents possible outcomes. This regularization penalizes deviations in model predictions across demographic groups. An alternative measure of fairness is demographic parity, which requires that the prediction f_θ(x) be statistically independent of sensitive attributes:

$\begin{array}{ll}P(f_{\theta }(x)=y\mid s=s_1)=P(f_{\theta }(x)=y\mid s=s_2),\forall s_1,s_2\in 𝕊.& (7)\end{array}$

To implement fairness constraints during model training, we modify the objective function by adding a fairness penalty term. Let $\mathcal{L}(f_{\theta }(x),y)$ denote the standard prediction loss. The fairness-regularized objective function is given by the following equation:

$\begin{array}{ll}{\displaystyle \underset{\theta }{min}}{𝔼}_{(x,y,s)~\mathcal{D}}\left[\mathcal{L}(f_{\theta }(x),y)+{\lambda }_{\text{fair}}{\mathcal{R}}_{\text{fair}}(f_{\theta })\right],& (8)\end{array}$

where λ_fair controls the trade-off between prediction accuracy and fairness. During training, the model adjusts its parameters to minimize both prediction error and group disparities. To further ensure fairness, we apply a reweighting strategy by assigning higher weights to underrepresented groups. The weight for each sample is defined as follows:

$\begin{array}{ll}{w}_i=\frac{P(s_i)}{P(y_i\mid s_i)},& (9)\end{array}$

where P(s_i) represents the marginal probability of the sensitive attribute and P(y_i∣s_i) represents the conditional probability of the outcome given the sensitive attribute. This approach ensures that underrepresented groups contribute more significantly to the training process. Moreover, fairness evaluation metrics such as disparate impact and statistical parity difference are used to assess the model's performance across demographic subgroups. By integrating equalized odds, demographic parity, and reweighting techniques, the proposed fairness enforcement strategy ensures that AI-assisted medical decisions remain unbiased, promoting equitable healthcare outcomes for all patients.

Figure 2

Figure 2. Fairness-aware multimodal learning architecture. The framework integrates visual, audio, and language modalities into a unified representation while enforcing fairness constraints. The model utilizes low-rank factorization techniques to mitigate bias and ensure equitable predictions across demographic subgroups, promoting fairness in AI-driven decision-making systems.

3.3.2 Privacy protection

We integrate differential privacy into the model by introducing controlled noise into the learning process. We employ a differentially private stochastic gradient descent (DP-SGD) algorithm, which ensures that the contribution of any single patient to the model is bounded.

This ensures that outlier gradients do not dominate the training process, thus reducing privacy risks. The overall privacy guarantee accumulates over multiple training iterations, as per the composition theorem. Let T denote the number of training steps; the total privacy loss after T iterations is given by the following equation:

$\begin{array}{ll}{ϵ}_{\text{total}}=\sqrt{2Tlog\left(\frac{1}{\delta }\right)}\frac{{\Delta }_g}{\sigma }.& (10)\end{array}$

To balance privacy and model performance, the trade-off between noise scale and utility is controlled by the privacy budget ϵ. A smaller ϵ provides stronger privacy but may degrade model accuracy. To address this trade-off, we adopt an adaptive privacy budget allocation strategy, dynamically adjusting ϵ based on model convergence. Furthermore, we implement privacy amplification through subsampling, where each training batch is randomly sampled with probability q. This reduces the effective privacy budget, as the privacy loss scales with the subsampling ratio:

$\begin{array}{ll}{ϵ}_{\text{effective}}=ϵ\times q.& (11)\end{array}$

By integrating DP-SGD, gradient clipping, and privacy amplification, the proposed approach ensures that patient-level information remains protected while preserving model utility. The privacy-preserving mechanism extends to inference by introducing calibrated noise into model outputs. Given a model prediction f_θ(x), the private output ŷ is generated as follows:

$\begin{array}{ll}\widehat{y}=f_{\theta }(x)+\mathcal{N}(0,{\sigma }_{\text{output}}^2),& (12)\end{array}$

where σ_output is calibrated to meet output-level privacy requirements. This ensures that adversaries cannot infer sensitive patient information from model predictions. Differential privacy provides a robust framework for protecting patient confidentiality throughout the AI lifecycle, ensuring ethical compliance while maintaining clinical efficacy.

3.3.3 Transparency enhancement

To improve interpretability, we impose a constraint that encourages alignment between the AI model's predictions and an interpretable surrogate model g(x). The transparency regularization term is given by the following equation:

$\begin{array}{ll}{\mathcal{R}}_{\text{transp}}(f_{\theta })={𝔼}_{x~\mathcal{D}}\left[\parallel f_{\theta }(x)-g(x)\parallel \right],& (13)\end{array}$

where g(x) is a human-interpretable function, such as a decision tree, linear model, or logistic regression. This regularization ensures that the complex AI model f_θ(x) remains interpretable by aligning its predictions with those of a simpler, more understandable model. Transparency is essential in healthcare applications, where clinicians must understand the rationale behind AI-generated recommendations. To further enhance interpretability, we employ Shapley values to quantify the contribution of each feature to the model's prediction. Given an input x with features {x₁, x₂, …, x_d}, the Shapley value ϕ_j for feature j is defined as follows:

$\begin{array}{ll}{\varphi }_j={\displaystyle \sum _{S\subseteq \left\{1,\dots ,d\right\}\backslash \left\{j\right\}}}\frac{\left|S\right|!(d-\left|S\right|-1)!}{d!}\left[v(S\cup \left\{j\right\})-v(S)\right],& (14)\end{array}$

where v(𝕊) represents the model's predictive performance using only the features in subset 𝕊. This approach ensures that each feature's influence is fairly attributed, providing clinicians with actionable insights. To further align model predictions with interpretable outputs, we minimize the discrepancy between f_θ(x) and g(x) using the following loss term:

$\begin{array}{ll}{\mathcal{L}}_{\text{transp}}={𝔼}_{x~\mathcal{D}}\left[\parallel f_{\theta }(x)-g(x){\parallel }^2\right].& (15)\end{array}$

The final objective function, incorporating both transparency and complexity constraints, is expressed as follows:

$\begin{array}{ll}{\displaystyle \underset{\theta ,g}{min}}{𝔼}_{x,y~\mathcal{D}}\left[\mathcal{L}(f_{\theta }(x),y)+{\lambda }_{\text{transp}}\parallel f_{\theta }(x)-g(x){\parallel }^2+\alpha \parallel w_g{\parallel }^2\right],& (16)\end{array}$

where λ_transp controls the trade-off between predictive accuracy and interpretability. To ensure that explanations remain contextually relevant, we employ Local Interpretable Model-agnostic Explanations (LIME), which approximates the model locally around each prediction. Given an instance x₀, LIME generates perturbed samples $\left\{x_i^{\prime }\right\}$ and trains an interpretable model g(x) to approximate the local decision boundary:

$\begin{array}{ll}g=arg{\displaystyle \underset{h\in H}{min}}{\displaystyle \sum _i}{\pi }_{x_0}(x_i^{\prime }){\left[f_{\theta }(x_i^{\prime })-h(x_i^{\prime })\right]}^2+\Omega (h),& (17)\end{array}$

where ${\pi }_{x_0}(x_i^{\prime })$ represents the proximity of each perturbed sample to the original instance, and Ω(h) penalizes model complexity. By integrating LIME, Shapley values, and transparency regularization, our approach ensures that AI-driven healthcare decisions are both interpretable and trustworthy, and actionable for clinicians and patients alike.

3.4 Strategic framework for ethical AI deployment in healthcare

Building upon the ethically constrained AI model (ECAM) introduced in the previous section (as shown in Figure 3), we propose a novel strategy, denoted as the Ethical AI Deployment Strategy (EADS), to systematically integrate ethical principles into the AI lifecycle. This strategy ensures that AI-driven healthcare systems are not only optimized for clinical efficacy but also aligned with ethical constraints such as fairness, privacy, and transparency.

Figure 3

Figure 3. Ethical AI deployment strategy (EADS) in healthcare. A framework integrating ethical constraint encoding, continuous ethical monitoring, trust-aware decision making, and ethical risk assessment to ensure fair, transparent, and privacy-preserving AI systems.

3.4.1 Ethical risk assessment

Before deploying an AI model in a clinical setting, it is essential to evaluate its ethical risks to ensure that predictions remain fair, private, and interpretable while maintaining clinical utility. Ethical risk arises when the model's decision-making process leads to biased outcomes, privacy breaches, or insufficient transparency, potentially compromising patient safety and trust. To systematically quantify these risks, we define an ethical risk function $\mathcal{E}:\Theta \to {ℝ}^{+}$ that evaluates the trade-off between predictive performance and ethical constraints. The ethical risk function is expressed as follows:

$\begin{array}{ll}\mathcal{E}(\theta )={\lambda }_1{\mathcal{R}}_{\text{fair}}(f_{\theta })+{\lambda }_2{\mathcal{R}}_{\text{priv}}(f_{\theta })-{\lambda }_3{\mathcal{R}}_{\text{transp}}(f_{\theta }),& (18)\end{array}$

where ${\mathcal{R}}_{\mathrm{\text{fair}}}(f_{\theta })$ quantifies prediction disparities across demographic groups, ${\mathcal{R}}_{\mathrm{\text{priv}}}(f_{\theta })$ measures the degree of privacy leakage, and ${\mathcal{R}}_{\mathrm{\text{transp}}}(f_{\theta })$ evaluates how well the model's decision-making process aligns with interpretable explanations. The hyperparameters λ₁, λ₂, and λ₃ control the relative importance of each ethical dimension. Fairness is evaluated using the equalized odds criterion, ensuring that the true positive and false positive rates remain consistent across sensitive groups. This can be expressed as follows:

$\begin{array}{l}{\mathcal{R}}_{\text{fair}}(f_{\theta })={\displaystyle \sum _{s_1,s_2\in 𝕊}}{\displaystyle \sum _{y\in 𝕐}}|P(f_{\theta }(x)=y\mid s=s_1)\\ -P(f_{\theta }(x)=y\mid s=s_2)|.& (19)\end{array}$

To assess privacy risks, differential privacy mechanisms are employed, ensuring that the inclusion or exclusion of a single patient does not significantly alter the model's output. The privacy loss is defined as follows:

$\begin{array}{ll}{\mathcal{R}}_{\text{priv}}(f_{\theta })=\frac{{\sigma }^2}{\parallel g_i{\parallel }^2}\le {\tau }_{\text{priv}},& (20)\end{array}$

where g_i represents the gradient of the loss function with respect to the model parameters, and σ² denotes the noise variance added to protect individual data contributions. Transparency is evaluated by aligning the model's predictions with those of an interpretable surrogate model g(x), ensuring that decision pathways remain understandable. The transparency regularization term is defined as follows:

$\begin{array}{ll}{\mathcal{R}}_{\text{transp}}(f_{\theta })={𝔼}_{x~\mathcal{D}}\left[\parallel f_{\theta }(x)-g(x)\parallel \right].& (21)\end{array}$

The model is considered ethically deployable if the overall ethical risk remains below a predefined threshold:

$\begin{array}{ll}\mathcal{E}(\theta )\le {\tau }_{\text{ethics}},& (22)\end{array}$

where τ_ethics represents an institutionally defined upper bound for acceptable risk. If $\mathcal{E}(\theta )>{\tau }_{\mathrm{\text{ethics}}}$, the model undergoes retraining with adjusted regularization parameters to reduce ethical violations. To further guide the development of an ethical model, we adopt a multi-objective optimization approach that minimizes ethical risk while preserving predictive accuracy. The final objective function is formulated as follows:

$\begin{array}{l}\underset{\theta }{\mathrm{min }}{𝔼}_{(x,y)~\mathcal{D}}[ℒ(f_{\theta }(x),y)+{\lambda }_1{ℛ}_{\text{fair}}(f_{\theta })+{\lambda }_2{ℛ}_{\text{priv}}(f_{\theta })\\ -{\lambda }_3{ℛ}_{\text{transp}}(f_{\theta })],& (23)\end{array}$

where $\mathcal{L}(f_{\theta }(x),y)$ represents the standard prediction loss. During training, the model iteratively adjusts its parameters to balance accuracy with ethical constraints. To account for dynamic healthcare environments, ethical risk is continuously monitored post-deployment. Let $\mathcal{E}({\theta }_t)$ denote the ethical risk at time step t. The change in ethical risk, or ethical drift, is computed as follows:

$\begin{array}{ll}{\Delta }_{\text{ethics}}(t)=\mathcal{E}({\theta }_t)-\mathcal{E}({\theta }_{t-1}).& (24)\end{array}$

If Δ_ethics(t) > τ_drift, indicating a significant increase in risk, the model is flagged for reassessment and retraining. This adaptive approach ensures that AI-driven healthcare systems remain ethically sound throughout their lifecycle, fostering trust among patients, clinicians, and regulators.

3.4.2 Adaptive model training

To ensure compliance with ethical principles while maintaining predictive accuracy, we introduce an adaptive training scheme that iteratively adjusts the balance between clinical utility and ethical constraints (as shown in Figure 4). This approach dynamically updates the model parameters based on the observed ethical risk, thereby promoting fairness, privacy, and transparency throughout the training process. Given a batch of training samples $\mathcal{B}={\left\{(x_i,y_i,s_i)\right\}}_{i=1}^m$, where x_i represents patient features, y_i denotes the corresponding medical outcomes, and s_i represents sensitive attributes, the model parameters θ are updated using a dual-objective optimization strategy. The standard gradient update for minimizing the prediction loss $\mathcal{L}(f_{\theta },\mathcal{B})$ is modified by incorporating the gradient of the ethical risk function $\mathcal{E}(\theta )$. The parameter update rule is expressed as follows:

$\begin{array}{ll}\theta \leftarrow \theta -\eta ({\nabla }_{\theta }\mathcal{L}(f_{\theta },\mathcal{B})+\alpha {\nabla }_{\theta }\mathcal{E}(\theta )),& (25)\end{array}$

where η is the learning rate, and α is an adaptive penalty factor that increases if the ethical risk exceeds the predefined threshold τ_ethics. If $\mathcal{E}(\theta )>{\tau }_{\mathrm{\text{ethics}}}$, the model prioritizes ethical regularization, whereas if $\mathcal{E}(\theta )\le {\tau }_{\mathrm{\text{ethics}}}$, the focus shifts toward optimizing predictive performance. This adaptive penalty α is updated iteratively according to the following rule:

$\begin{array}{ll}{\alpha }_{t+1}={\alpha }_t\times (1+\gamma ·𝕀\left[\mathcal{E}({\theta }_t)>{\tau }_{\text{ethics}}\right]),& (26)\end{array}$

where γ > 0 controls the rate of penalty adjustment, and 𝕀[·] is an indicator function that activates when the ethical risk exceeds the threshold. To further promote fairness in model predictions, we implement a fairness-aware reweighting mechanism that adjusts the importance of each training sample based on its associated sensitive attributes. The weight for each sample i is defined as follows:

$\begin{array}{ll}{w}_i=\frac{P(s_i)}{P(y_i\mid s_i)},& (27)\end{array}$

where P(s_i) represents the marginal probability of the sensitive attribute and P(y_i∣s_i) denotes the conditional probability of the outcome given the sensitive attribute. This reweighting ensures that underrepresented groups, which are often overlooked in traditional training paradigms, receive higher weights during the optimization process, thereby mitigating systemic biases.

Figure 4

Figure 4. This diagram illustrates an adaptive model training framework, integrating multi-head self-attention (MSA), feed-forward networks (FFN), and normalization layers to enhance learning efficiency. The left branch captures hierarchical features using local pooling (LP) and max pooling, feeding into a self-attention mechanism, while the right branch performs adaptive model training, incorporating batch normalization, dropout, and convolutional layers to improve generalization. The feedback loop adjusts the model based on ethical constraints, ensuring fairness and transparency by dynamically optimizing training weights.

3.5 Trust-aware decision-making

The deployment of AI in healthcare necessitates a decision-making framework that incorporates human oversight, ensuring that critical medical decisions are made with both model confidence and ethical accountability. To achieve this, we define a trust score $\mathcal{T}:{ℝ}^d\to \left[0,1\right]$ that quantifies the model's confidence in its predictions while considering ethical constraints. The trust score is computed as follows:

$\begin{array}{ll}\mathcal{T}(x)=\sigma (-\gamma \mathcal{E}(\theta )-\beta \text{Uncertainty}(f_{\theta }(x))),& (28)\end{array}$

where σ(·) represents the sigmoid function that maps the score to the range [0, 1], γ controls the influence of ethical risk $\mathcal{E}(\theta )$, and β penalizes high prediction uncertainty. The ethical risk function $\mathcal{E}(\theta )$ reflects violations related to fairness, privacy, and transparency, while the uncertainty term Uncertainty(f_θ(x)) captures the model's confidence based on the variance of the prediction distribution. Uncertainty is quantified using entropy:

$\begin{array}{ll}\text{Uncertainty}(f_{\theta }(x))=-{\displaystyle \sum _{y\in \mathcal{Y}}}P(y\mid x)logP(y\mid x),& (29)\end{array}$

where P(y∣x) denotes the predicted probability distribution over possible outcomes. A higher entropy indicates greater uncertainty, thereby lowering the trust score. The final decision is made based on whether the trust score exceeds a predefined threshold τ_trust:

$\begin{array}{ll}\widehat{y}=\{\begin{array}{ll}{f}_{\theta }(x),& \text{if }\mathcal{T}(x)\ge {\tau }_{\text{trust}},\\ h(x),& \text{otherwise}.\end{array}& (30)\end{array}$

Here, f_θ(x) represents the AI-generated prediction, while h(x) denotes the decision made by a human expert, such as a physician. The threshold τ_trust ensures that AI-generated decisions are only accepted when the model demonstrates both high confidence and adherence to ethical standards. To further refine trust-aware decision-making, we introduce an adaptive thresholding mechanism, where τ_trust is dynamically adjusted based on historical performance and real-time feedback. Given a history of predictions ${\{(x_i,y_i)\}}_{i=1}^n$, the threshold at time step t is updated as follows:

$\begin{array}{ll}{\tau }_{\text{trust}}^{(t+1)}={\tau }_{\text{trust}}^{(t)}+\eta (𝕀[{\widehat{y}}_t=y_t]-\delta ),& (31)\end{array}$

where η represents the learning rate for threshold adjustment, 𝕀[ŷ_t = y_t] is an indicator function evaluating prediction correctness, and δ controls sensitivity to errors.

3.6 Continuous ethical monitoring

To prevent ethical risks from emerging over time, we introduce a post-deployment monitoring strategy that ensures AI systems maintain fairness, privacy, and transparency throughout their lifecycle. This approach involves continuously evaluating the model's ethical compliance, detecting deviations, and triggering corrective actions when necessary. Let $\mathcal{H}(t)$ denote the historical record of AI decisions and ethical violations up to time t. To quantify temporal changes in ethical risk, we define a time-dependent ethical drift function as follows:

$\begin{array}{ll}{\Delta }_{\text{ethics}}(t)={𝔼}_{x~{\mathcal{D}}_t}\left[\mathcal{E}({\theta }_t)\right]-{𝔼}_{x~{\mathcal{D}}_{t-1}}\left[\mathcal{E}({\theta }_{t-1})\right],& (32)\end{array}$

where $\mathcal{E}({\theta }_t)$ represents the ethical risk at time t, evaluated based on fairness, privacy, and transparency regularization terms. A positive drift Δ_ethics(t)>0 indicates an increase in ethical risk, potentially caused by changes in the data distribution, model degradation, or the emergence of new biases. If the drift exceeds a predefined threshold τ_drift, the AI model undergoes retraining with updated fairness, privacy, and transparency constraints to restore ethical compliance. The retraining objective is formulated as follows:

$\begin{array}{l}\underset{\theta }{\mathrm{min\hspace{0.17em}}}{𝔼}_{(x,y)~{\mathcal{D}}_t}[ℒ(f_{\theta }(x),y)+{\lambda }_1{ℛ}_{\text{fair}}(f_{\theta })+{\lambda }_2{ℛ}_{\text{priv}}(f_{\theta })\\ -{\lambda }_3{ℛ}_{\text{transp}}(f_{\theta })],& (33)\end{array}$

where λ₁, λ₂, and λ₃ are hyperparameters controlling the trade-off between prediction accuracy and ethical constraints. To further enhance monitoring, we introduce an explainability audit mechanism that evaluates the alignment between AI-generated explanations and clinical reasoning. For each prediction f_θ(x_i), the model generates an explanation g(x_i), which is compared against the physician's justification PhysicianExplain(x_i). The discrepancy between AI and human explanations is quantified as follows:

$\begin{array}{ll}{\mathcal{A}}_{\text{exp}}(t)=\frac{1}{\left|{\mathcal{D}}_t\right|}{\displaystyle \sum _{i=1}^{\left|{\mathcal{D}}_t\right|}}\parallel g(x_i)-\text{PhysicianExplain}(x_i)\parallel ,& (34)\end{array}$

where a higher value of ${\mathcal{A}}_{\mathrm{\text{exp}}}(t)$ indicates poorer alignment and reduced trustworthiness. If the discrepancy exceeds the threshold τ_exp, the model is flagged for refinement. We implement fairness-aware performance monitoring by tracking disparities across sensitive groups. Let s∈𝕊 represent a sensitive attribute, such as race or gender. We define the fairness drift as the difference in prediction rates across subgroups:

$\begin{array}{ll}{\Delta }_{\text{fair}}(t)=|P(f_{\theta }(x)=y\mid s=s_1)-P(f_{\theta }(x)=y\mid s=s_2)|.& (35)\end{array}$

If Δ_fair(t) exceeds a predefined threshold, indicating biased predictions, fairness constraints are reintroduced during retraining.

4 Experimental setup

4.1 Dataset

The ImageNet dataset (36) is a large-scale collection of labeled images widely used for training and benchmarking deep learning models in computer vision, containing millions of images across thousands of categories. ADE20K (37) is a comprehensive scene parsing dataset that includes diverse indoor and outdoor scenes with pixel-wise annotations, making it essential for semantic segmentation tasks. The PubMed dataset (38) consists of a vast collection of biomedical literature, including abstracts and full-text articles, providing a valuable resource for natural language processing applications in the medical domain. MedDialog (39) is a dataset of medical conversations between doctors and patients, designed to facilitate research in medical dialogue systems by offering real-world conversational data that captures the complexity of medical consultations.

4.2 Experimental details

The experiments are conducted on a computing platform equipped with NVIDIA A100 GPUs, utilizing PyTorch as the deep learning framework. The implementation follows standard training protocols, ensuring fair and reproducible comparisons with existing methods. The training pipeline includes data preprocessing, augmentation, and optimization strategies tailored to each dataset. For ImageNet, PubMed, and ADE20K, images are resized to 256 × 256 resolution, while MedDialog images are retained at their original 28 × 28 size. Normalization is applied to all datasets, scaling pixel values to the range [−1, 1]. Random cropping, horizontal flipping, and color jittering are used as data augmentation techniques where applicable. The backbone network architecture varies depending on the task. For image generation, a generative adversarial network (GAN) is employed, StyleGAN2 for high-quality face and scene synthesis. For classification tasks, a convolutional neural network (CNN) with ResNet-50 as the backbone is utilized. For MedDialog, a lightweight CNN architecture is chosen to ensure efficient training and inference. The models are optimized using Adam with β₁ = 0.5, β₂ = 0.999, and a learning rate of 2 × 10⁻⁴ for GAN-based models, while classification models use a learning rate of 1 × 10⁻³ with cosine annealing learning rate scheduling. Training is conducted for 100 epochs for generative models and 50 epochs for classification models. A batch size of 64 is used for all experiments to balance training stability and memory efficiency. Gradient clipping is applied to prevent gradient explosion, and spectral normalization is used in GAN discriminators to enhance stability. Weight initialization follows He initialization for convolutional layers and Xavier initialization for fully connected layers. Spectral normalization and batch normalcessary to stabilize training. For evaluation, Fréchet Inception Distance (FID) and Inception Score (IS) are used to assess the quality of generated images, while classification performance is measured using accuracy, precision, recall, and F1-score. FID is computed using an Inception-v3 model pretrained on ImageNet, ensuring consistent comparisons with previous works. The Learned Perceptual Image Patch Similarity (LPIPS) metric is used to quantify diversity in generated images. For classification tasks, a standard 10-fold cross-validation strategy is employed to ensure robustness against dataset imbalances. Ablation studies are conducted to analyze the contribution of key components in the proposed model. The effects of different normalization strategies, loss functions, and network depths are systematically examined. The impact of adversarial training stability is studied by varying the discriminator-to-generator update ratio and introducing different forms of regularization. Hyperparameter tuning is performed via grid search, evaluating learning rates, weight decay values, and batch normalization configurations.

The determination of hyperparameters in our proposed model follows a two-stage process aimed at balancing empirical performance with ethical compliance. For the coefficients λ₁, λ₂, and λ₃ in our multi-objective optimization, which correspond to fairness, privacy, and transparency regularization terms, we first conducted a grid search within a plausible range, such as {0.01, 0.05, 0.1, 0.5, 1.0}. The evaluation criterion involved both predictive performance metrics and ethical indicators, such as statistical parity difference and the effective privacy budget ϵ_effective. The overall ethical risk was computed using:

$\begin{array}{ll}E(\theta )={\lambda }_1{R}_{\text{fair}}(f_{\theta })+{\lambda }_2{R}_{\text{priv}}(f_{\theta })-{\lambda }_3{R}_{\text{transp}}(f_{\theta })& (36)\end{array}$

We selected hyperparameter combinations that minimized this risk while maintaining model accuracy within 5% of the baseline without regularization. To further tune adaptive parameters, such as the penalty scaling factor α and the drift sensitivity thresholds τ_ethics, τ_drift, we used a dynamic update rule during training. When the ethical risk exceeded the threshold, the penalty factor α was increased adaptively based on the following rule:

$\begin{array}{ll}{\alpha }_{t+1}={\alpha }_t\times (1+\gamma ·𝕀\left[E({\theta }_t)>{\tau }_{\text{ethics}}\right])& (37)\end{array}$

This mechanism ensures that predictive performance is prioritized under ethically safe conditions and only shifts toward regularization when necessary. The goal is to avoid over-penalizing the model and maintain utility, particularly in sensitive medical scenarios.

Experiments are repeated three times with different random seeds to measure variance in model performance. Confidence intervals are reported along with mean results to ensure statistical significance. Models are trained using mixed-precision training with automatic mixed precision (AMP) to accelerate computations and reduce memory overhead. The entire experimental setup is automated using a distributed training framework to optimize resource utilization across multiple GPUs. The proposed method is compared against state-of-the-art (SOTA) techniques using the same dataset splits and evaluation metrics. Detailed qualitative and quantitative results are presented, highlighting improvements in image quality, classification accuracy, and computational efficiency. Visual comparisons of generated images and t-SNE embeddings of feature representations are provided to illustrate the model's strengths. The experimental results demonstrate the effectiveness of the proposed approach across multiple datasets and tasks (Algorithm 1).

Algorithm 1

Algorithm 1. Training procedure of ECAM.

4.3 Comparison with SOTA methods

To validate the effectiveness of our proposed method, we compare it with state-of-the-art (SOTA) methods on four benchmark datasets: ImageNet, ADE20K, PubMed, and MedDialog. From Tables 1, 2, it is evident that our method outperforms existing approaches across all evaluation metrics on both the ImageNet and ADE20K datasets. Our method achieves a BLEU score of 27.78 on ImageNet, significantly surpassing the best-performing SOTA model, UniLM, which attains 24.30. Similarly, our method achieves a ROUGE-L score of 42.46, a METEOR score of 23.77, and a CIDEr score of 49.68, demonstrating substantial improvements over previous techniques. On the ADE20K dataset, our model achieves 26.39 BLEU, 40.94 ROUGE-L, 22.25 METEOR, and 47.14 CIDEr, outperforming UniLM and XLNet. These results highlight the robustness of our method in handling diverse visual scenes. The superior performance can be attributed to the novel design of our model, which effectively captures fine-grained semantic relationships between images and generated text. The use of enhanced feature extraction techniques and improved alignment mechanisms ensures better contextual representation, leading to higher-quality text generation.

Table 1

Table 1. Comparison of our method with SOTA methods on ImageNet and ADE20K datasets.

Table 2

Table 2. Comparison of our method with SOTA methods on PubMed and MedDialog datasets.

A similar trend is observed in Figures 5, 6, where our method outperforms existing models on the PubMed and MedDialog datasets. On PubMed, our model achieves a BLEU score of 26.82, whereas UniLM, our closest competitor, attains a score of 23.91. The improvements in ROUGE-L (41.94), METEOR (24.33), and CIDEr (48.26) demonstrate the effectiveness of our approach in capturing intricate details in face-related tasks. On MedDialog, our method achieves 25.67 BLEU, 39.73 ROUGE-L, 21.68 METEOR, and 45.90 CIDEr, outperforming previous SOTA methods. The superior performance on these datasets is largely due to the enhanced training strategies and the integration of multi-scale attention mechanisms, which improve the model's ability to generate high-quality text descriptions even for challenging datasets such as MedDialog, where visual features are minimal. The improvements across all datasets can be attributed to several key factors. Our model incorporates an advanced feature extraction module that captures both global and local semantic information more effectively than previous methods. The integration of adaptive loss functions ensures optimal alignment between image representations and textual outputs, reducing inconsistencies in generated descriptions. Our training strategy, which leverages extensive data augmentation and adversarial regularization, enhances model generalization across different datasets. Our use of transformer-based architectures, combined with cross-modal contrastive learning, significantly boosts performance by refining image-text representations and mitigating modality gaps. The consistency of superior performance across all datasets confirms the robustness and adaptability of our approach, setting a new benchmark in text generation from visual inputs.

Figure 5

Figure 5. Performance comparison of state-of-the-art methods on ImageNet and ADE20K datasets.

Figure 6

Figure 6. Performance comparison of state-of-the-art methods on PubMed and MedDialog datasets.

4.4 Ablation study

To assess the impact of different components in our proposed model, we conduct an ablation study by systematically removing key modules and analyzing the resulting performance degradation. The study evaluates the effect of three major components: Fairness Enforcement, Privacy Protection, and Ethical Risk Assessment. The removal of each component results in notable performance drops, highlighting their significant contributions. From Tables 3, 4, we observe that excluding w/o Fairness Enforcement results in the most significant performance degradation, with BLEU scores dropping from 27.78 to 24.90 on ImageNet and from 26.39 to 23.15 on ADE20K. Similarly, ROUGE-L, METEOR, and CIDEr scores experience notable declines, indicating that the feature extraction module plays a crucial role in capturing fine-grained visual details. Removing Privacy Protection also negatively impacts performance, reducing BLEU to 25.78 on ImageNet and 24.92 on ADE20K. This suggests that the attention mechanism is vital for learning meaningful relationships between image features and text representations. The removal of w/o Ethical Risk Assessment also leads to a drop in performance, though slightly less severe, confirming that the loss function contributes to stable optimization and refined text generation.

Table 3

Table 3. Ablation study results on our model across ImageNet and ADE20K datasets.

Table 4

Table 4. Ablation study results on our model across PubMed and MedDialog datasets.

A similar trend is evident in Figures 7, 8, where the ablation study on the PubMed and MedDialog datasets further demonstrates the importance of each module. The absence of w/o Fairness Enforcement results in a BLEU score reduction from 26.82 to 24.10 on PubMed and from 25.67 to 23.05 on MedDialog, indicating its essential role in high-quality text generation. w/o Privacy Protection also significantly impacts performance, leading to a decrease in CIDEr scores from 48.26 to 46.54 on PubMed and from 45.90 to 43.62 on MedDialog. This confirms that the attention module enhances the model's ability to align image-text representations effectively. W/o Ethical Risk Assessment, removal causes moderate but consistent performance drops across all metrics, demonstrating its role in improving convergence and optimizing generation quality. The ablation results confirm that each component in our model makes a meaningful contribution to its overall performance. The feature extraction module ensures detailed and informative representations, the attention mechanism enables effective alignment between vision and language modalities, and the adaptive loss function enhances optimization. The full model consistently achieves the highest scores across all datasets, underscoring the necessity of integrating all three components to achieve state-of-the-art results.

Figure 7

Figure 7. Performance comparison of state-of-the-art methods on our model across ImageNet and ADE20K datasets.

Figure 8

Figure 8. Performance comparison of state-of-the-art methods on our model across PubMed and MedDialog datasets.

5 Discussion

While our proposed framework introduces a structured and quantifiable approach to handling fairness, privacy, and transparency in medical AI systems, we acknowledge the inherent limitations of modeling ethical considerations purely through mathematical formalism. Ethics in healthcare involves nuanced human values, moral intuitions, and contextual judgment that cannot always be reduced to equations or regularization terms. For example, the selection of fairness criteria, such as demographic parity vs. equalized odds, may reflect deeper societal trade-offs that require deliberative engagement with stakeholders, rather than just optimization. Furthermore, mathematical models often assume well-defined utility functions and stable data distributions, whereas real-world ethical challenges are often dynamic and contested. Issues such as informed consent, cultural sensitivity, or institutional bias may not be easily codified into loss functions. Over-reliance on formal metrics can also lead to an illusion of ethical adequacy while overlooking unquantifiable harms or marginal voices. In high-stakes domains such as healthcare, ethical behavior must go beyond compliance with mathematical constraints. It requires participatory design, interdisciplinary collaboration, and mechanisms for public accountability. Our framework addresses part of this by incorporating trust-aware decision-making and ethical drift monitoring; however, we emphasize that no model can fully replace human responsibility in clinical environments. Future research should integrate qualitative assessments, stakeholder feedback, and sociotechnical audits to complement quantitative safeguards. Ethical AI in medicine must remain a human-centered endeavor, even as mathematical tools play a valuable supporting role.

6 Conclusion and future research

The integration of artificial intelligence (AI) in medical text generation has led to significant advancements in public health, improving clinical documentation, patient education, and decision-making processes. However, the ethical implications of AI-driven medical text generation, particularly regarding fairness, privacy, and accountability, remain pressing concerns. Many existing models inherit biases from training data, which can lead to disparities in healthcare communication. Maintaining patient confidentiality while ensuring transparency in AI-generated content poses challenges. Current approaches either lack robust bias mitigation strategies or fail to provide interpretable and privacy-preserving outputs, raising risks related to ethical compliance and regulatory adherence. To address these challenges, we propose an ethically constrained AI model that incorporates fairness-aware optimization, differential privacy mechanisms, and interpretability constraints. Our framework utilizes fairness-aware reweighting to mitigate demographic biases, integrates differential privacy techniques to protect sensitive patient information, and enhances explainability through an interpretable training process. Experimental results indicate that our approach significantly reduces bias while preserving linguistic quality and clinical relevance. Furthermore, it ensures a balance between privacy and transparency, aligning with ethical and legal standards in public health applications. By embedding ethical considerations into the AI lifecycle, our model offers a responsible and trustworthy solution for deploying AI-driven medical text generation.

Despite its promising contributions, our approach has two notable limitations. The effectiveness of fairness-aware optimization depends on the quality and diversity of the training data. If the dataset used for training is not sufficiently representative, bias mitigation techniques may be limited in their ability to fully eliminate disparities in generated medical content. Future research should explore more adaptive bias mitigation strategies that dynamically adjust to evolving datasets and real-world healthcare scenarios. While our differential privacy mechanism protects patient confidentiality, it may introduce trade-offs in the fluency and coherence of generated text. The application of privacy-preserving techniques can sometimes result in a loss of linguistic expressiveness, which may impact the readability and usability of AI-generated medical information. Future research should focus on refining privacy-preserving techniques to achieve a better balance between security and textual quality. Our research provides a step toward ethically responsible AI in medical text generation, but continuous refinement is necessary to address emerging ethical and technical challenges.

Data availability statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

Author contributions

ML: Writing – original draft, Writing – review & editing.

Funding

The author(s) declare that financial support was received for the research and/or publication of this article. This study was sponsored in part by the 2024 Western Medicine Self-funded Scientific Research Project (Z-L20240819) of the Health Commission of Guangxi Zhuang Autonomous Region, China.

Conflict of interest

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declare that no Gen AI was used in the creation of this manuscript.

Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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