Description:FIELD OF DISCLOSURE
[0001] The present disclosure relates generally relates to the field of medical artificial intelligence systems. More specifically, it pertains to an explainable AI interface system for multimodal medical diagnosis using layered attention and visual attribution maps.
BACKGROUND OF THE DISCLOSURE
[0002] The use of artificial intelligence (AI) in medical diagnosis has undergone remarkable progress in recent years, with machine learning and deep learning systems playing pivotal roles in aiding clinicians across various domains. The expansion of computational power, availability of large-scale datasets, and rapid advances in algorithmic design have enabled the development of predictive models that can classify, segment, and interpret medical data with accuracy that often rivals or exceeds human experts. Despite these advances, one of the persistent challenges in deploying AI-based systems in healthcare is the lack of interpretability and transparency. Clinicians and healthcare stakeholders require diagnostic systems not only to provide predictions but also to explain the reasoning behind such decisions in ways that align with medical understanding and professional accountability. This need has given rise to a dedicated research domain known as Explainable Artificial Intelligence (XAI), which is especially critical in high-stakes fields like medical diagnosis where human lives are directly impacted.
[0003] The foundation of medical diagnosis lies in integrating multimodal information, which encompasses clinical narratives, imaging data such as MRI, CT scans, X-rays, laboratory test results, genetic information, and physiological signals. Human clinicians rely on combining these diverse forms of evidence to reach a diagnostic conclusion. However, AI models traditionally focus on unimodal data, often specializing in a single type of medical information. While unimodal models have demonstrated success in narrow tasks, they fail to capture the holistic clinical picture. Recent advances in multimodal learning attempt to address this by fusing information from multiple sources, such as combining radiology images with patient histories or linking genomic data with pathology slides. By integrating multiple modalities, AI systems can generate more robust, accurate, and context-aware diagnoses. Nevertheless, the complexity of multimodal models poses additional challenges for interpretability, as the internal decision-making pathways become increasingly opaque.
[0004] Deep learning models, particularly convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers, have demonstrated powerful capabilities in medical image analysis and natural language processing of clinical records. For example, CNNs can identify subtle lesions in radiology scans, while RNNs can analyze sequential patient records. More recently, transformer-based models with self-attention mechanisms have gained prominence due to their ability to capture long-range dependencies and context across data. These models, however, operate as “black boxes,” making it difficult for practitioners to understand how specific features contribute to the final prediction. This black-box nature not only limits trust among clinicians but also hinders the adoption of AI systems in regulatory and clinical environments where interpretability and accountability are non-negotiable.
[0005] The importance of explainability in AI for medical diagnosis cannot be overstated. Trust, ethics, and regulatory requirements demand that AI decisions be interpretable and understandable to human stakeholders. In healthcare, the stakes are uniquely high: an incorrect diagnosis can lead to delayed treatment, mismanagement of patient care, or even fatal outcomes. Clinicians are trained to follow evidence-based practices, where each diagnostic conclusion must be supported by clinical findings and rationale. Thus, when AI models provide predictions without explaining their reasoning, clinicians may hesitate to adopt such systems, regardless of their predictive accuracy. Furthermore, regulations and guidelines from bodies such as the U.S. Food and Drug Administration (FDA) and the European Medicines Agency (EMA) increasingly emphasize transparency in AI-driven medical tools, reinforcing the demand for explainability.
[0006] To address this demand, various techniques for model interpretability have emerged. Broadly, interpretability approaches can be categorized into intrinsic interpretability and post-hoc explainability. Intrinsic interpretability refers to models that are inherently understandable, such as decision trees or linear models. While these are easy to explain, they often lack the predictive power required for complex multimodal medical data. Post-hoc explainability, on the other hand, applies to high-performing models like deep neural networks and involves techniques to uncover the reasoning behind predictions after the fact. Examples include feature attribution methods such as saliency maps, layer-wise relevance propagation, SHAP (SHapley Additive exPlanations), and LIME (Local Interpretable Model-agnostic Explanations). These methods attempt to map the importance of features or regions of input data that influenced a model’s output.
[0007] In the field of medical imaging, visual attribution maps have gained popularity as a means of post-hoc explainability. Such maps highlight areas of an image that contributed most to the model’s decision, allowing clinicians to verify whether the AI’s reasoning aligns with medical knowledge. For instance, in detecting pneumonia from chest X-rays, a visual attribution map can highlight lung regions exhibiting opacity, which is consistent with clinical interpretation. While visual attribution has shown promise, it is often noisy, inconsistent, and sensitive to model architecture and parameterization. Furthermore, attribution maps typically provide only local explanations without offering a holistic understanding of how multimodal inputs interact across the entire decision-making process.
[0008] Layered attention mechanisms, inspired by advances in transformer models, provide another avenue for enhancing interpretability. Attention mechanisms allow models to weigh the relevance of different input features or modalities when generating outputs. In a medical diagnostic setting, attention layers can be applied hierarchically, first focusing on intra-modality features (e.g., regions within an MRI scan) and then on inter-modality relationships (e.g., connections between imaging features and lab results). Such layered attention can serve as a built-in interpretability tool, as it offers insights into which elements of the input the model prioritized at different stages of reasoning. By combining layered attention with visual attribution, AI systems can provide richer, multi-level explanations that are closer to how clinicians reason when synthesizing evidence.
[0009] Despite these advancements, significant challenges remain in the field of explainable multimodal medical AI. One of the foremost difficulties lies in ensuring that explanations are not only technically accurate but also clinically meaningful. A saliency map or attention weight visualization may satisfy computer scientists, but unless it aligns with medically relevant patterns, clinicians may find it unhelpful. Another challenge is scalability: healthcare involves a vast range of diagnostic contexts, modalities, and patient populations, making it difficult to design universal interpretability frameworks. Moreover, there is the issue of consistency across different models two models trained on the same dataset may produce differing explanations for the same case, raising concerns about reliability and reproducibility.
[0010] The evolution of XAI in healthcare is also intertwined with ethical considerations. Explainability is a cornerstone of ethical AI, ensuring accountability, fairness, and transparency. Without explainable outputs, AI models risk perpetuating biases embedded in training datasets, such as underrepresentation of minority populations or skewed diagnostic patterns. These biases can lead to systematic disparities in medical outcomes if left unchecked. Explainability tools can help detect such biases by revealing which features the model relies on disproportionately, thus offering opportunities for correction. Additionally, from the perspective of informed consent, patients have the right to understand the reasoning behind medical decisions that affect their health. If AI systems contribute to these decisions, their explanations must be interpretable not only to clinicians but also to patients and their families in accessible ways.
[0011] Research on multimodal AI for healthcare has accelerated in recent years, particularly with the availability of large-scale biomedical datasets. Initiatives like the Cancer Imaging Archive, MIMIC-III, and various genomic repositories provide diverse sources of data that can be combined to train multimodal models. For example, studies have demonstrated the integration of histopathological images with genomic data to improve cancer subtype classification, or the fusion of radiology images with electronic health records to enhance prognosis prediction. These approaches highlight the promise of multimodal AI, yet they also underscore the need for explainability, as the reasoning pathways in such models are highly complex and involve cross-modal dependencies.
[0012] The interface design for explainable AI systems in medicine is another critical aspect that influences usability and adoption. Explanations must be communicated effectively to end-users through intuitive visualizations, interactive dashboards, or layered summaries that match clinical workflows. A poorly designed interface can render otherwise useful explanations inaccessible or overwhelming. User-centered design principles are therefore essential in creating interfaces that bridge the gap between advanced computational outputs and the cognitive processes of medical professionals. Human factors research emphasizes that explanations should not overload clinicians with technical detail but instead provide concise, interpretable insights that aid decision-making without distracting from patient care.
[0013] Regulatory bodies emphasize transparency, traceability, and auditability of AI-based diagnostic systems. For instance, the European Union’s General Data Protection Regulation (GDPR) includes a “right to explanation,” which has implications for AI-driven healthcare tools. Similarly, the FDA has been developing guidelines for AI and machine learning-based software as medical devices, stressing the importance of interpretability in ensuring patient safety. These regulations create strong incentives for developing robust explainability mechanisms in medical AI systems, particularly those dealing with multimodal data where the potential for opacity is greater.
[0014] In parallel, academic and industrial communities continue to explore emerging techniques to enhance explainability in AI systems. Recent research has proposed combining symbolic reasoning with neural models to provide more structured and human-readable explanations. Hybrid approaches aim to integrate the interpretability of symbolic systems with the predictive power of deep learning. In multimodal medical diagnosis, hybrid strategies may involve linking structured medical ontologies with neural attribution methods to provide explanations that are both computationally rigorous and clinically coherent. Similarly, interactive explanation methods are being investigated, where clinicians can query an AI system to explore counterfactual scenarios, such as how the diagnosis would change if certain lab results were different.
[0015] The role of human-AI collaboration is central to the discourse on explainable medical AI. Rather than viewing AI as a replacement for clinicians, the current consensus emphasizes augmentation where AI provides supportive evidence, and clinicians retain the final decision-making authority. Explainability plays a crucial role in this paradigm, as it enables clinicians to critically evaluate AI recommendations and integrate them into their broader diagnostic reasoning. A well-designed explainable system can also support training and education, allowing medical students and junior doctors to understand complex patterns in multimodal data through interpretable visualizations generated by AI models.
[0016] Looking ahead, the integration of multimodal data, layered attention mechanisms, and visual attribution maps represents a convergence of trends in both AI and medical diagnostics. While each of these components has been studied independently, their combined use opens new possibilities for creating systems that not only perform at high levels of accuracy but also align with clinical reasoning processes. However, achieving this integration requires addressing technical, clinical, and ethical challenges simultaneously. It involves ensuring that attention mechanisms faithfully represent decision pathways, that attribution maps are robust and reliable, and that interfaces translate computational complexity into clinically actionable insights.
[0017] Thus, in light of the above-stated discussion, there exists a need for an explainable AI interface system for multimodal medical diagnosis using layered attention and visual attribution maps.
SUMMARY OF THE DISCLOSURE
[0018] The following is a summary description of illustrative embodiments of the invention. It is provided as a preface to assist those skilled in the art to more rapidly assimilate the detailed design discussion which ensues and is not intended in any way to limit the scope of the claims which are appended hereto in order to particularly point out the invention.
[0019] According to illustrative embodiments, the present disclosure focuses on an explainable AI interface system for multimodal medical diagnosis using layered attention and visual attribution maps which overcomes the above-mentioned disadvantages or provide the users with a useful or commercial choice.
[0020] An objective of the present disclosure is to design and develop an explainable AI interface system that integrates multimodal medical data, including radiological images, EHRs, genomic profiles, and patient-reported outcomes, into a unified diagnostic framework.
[0021] Another objective of the present disclosure is to provide layered attention mechanisms that hierarchically highlight the contribution of each modality and sub-feature in the diagnostic process, thereby improving transparency and interpretability.
[0022] Another objective of the present disclosure is to generate visual attribution maps that allow clinicians to identify key regions in medical images correlated with diagnostic predictions, ensuring clinical relevance and trust.
[0023] Another objective of the present disclosure is to enable clinician-centric explanations by tailoring the system’s outputs to match the reasoning workflows of healthcare professionals, facilitating adoption in real-world medical settings.
[0024] Another objective of the present disclosure is to support comprehensive multimodal fusion by capturing interdependencies between visual and non-visual data sources, enhancing diagnostic accuracy and robustness.
[0025] Another objective of the present disclosure is to overcome the limitations of black-box AI models by providing interpretable outputs that justify diagnostic decisions, thereby fostering regulatory acceptance and patient confidence.
[0026] Another objective of the present disclosure is to create a dynamic and interactive user interface that allows medical professionals to explore layered explanations at different levels of granularity, from global diagnostic rationale to local feature attributions.
[0027] Another objective of the present disclosure is to ensure that explainability outputs are clinically actionable, allowing healthcare providers to validate or contest AI-generated insights based on their expertise and patient context.
[0028] Another objective of the present disclosure is to advance trustworthy AI in healthcare by combining layered attention with visual attribution methods, reducing biases, and mitigating risks associated with opaque decision-making.
[0029] Yet another objective of the present disclosure is to establish a scalable and adaptable system architecture that can be integrated into diverse clinical workflows, supporting continuous learning and refinement with evolving medical datasets.
[0030] In light of the above, an explainable AI interface system for multimodal medical diagnosis using layered attention and visual attribution maps comprises a multimodal data acquisition module configured to receive and integrate heterogeneous medical data. The system also includes a layered attention processing module configured to perform intra-modal attention to capture salient features within each modality and inter-modal attention to dynamically weigh cross-modality relationships. The system also includes a visual attribution generation module configured to produce modality-specific attribution maps. The system also includes an interactive clinician-centric interface module configured to present diagnostic reasoning in alignment with clinical workflows. The system also includes a diagnostic decision support engine configured to provide transparent, explainable, and inspectable diagnostic recommendations that support clinician decision-making without replacing clinical judgment.
[0031] In one embodiment, the multimodal data acquisition module is further configured to receive medical imaging data comprising X-ray, magnetic resonance imaging (MRI), computed tomography (CT), ultrasound, and positron emission tomography (PET) scans.
[0032] In one embodiment, the multimodal data acquisition module is further configured to process unstructured textual data including electronic health records (EHRs), discharge summaries, physician notes, and clinical guidelines.
[0033] In one embodiment, the multimodal data acquisition module is further configured to integrate structured numerical data comprising laboratory test results, vital signs, demographic attributes, and medication history.
[0034] In one embodiment, the layered attention processing module is further configured to perform temporal attention modeling that captures sequential and conditional dependencies in patient data over time.
[0035] In one embodiment, the layered attention processing module comprises a hierarchical attention network configured to combine intra-modal, inter-modal, and temporal attention scores into a unified representation for diagnostic reasoning.
[0036] In one embodiment, the visual attribution generation module is further configured to generate heatmaps over radiological images, highlight salient textual tokens in clinical notes, and emphasize critical numerical variables in laboratory data.
[0037] In one embodiment, the visual attribution generation module is further configured to map feature contributions to specific diagnostic outcomes, thereby enabling clinicians to trace the reasoning path of the system’s predictions.
[0038] In one embodiment, the interactive clinician-centric interface module is further configured to allow user interaction for drilling down into diagnostic reasoning steps, exploring modality-specific contributions, and adjusting contextual patient parameters to observe real-time changes in system output.
[0039] In one embodiment, the interactive clinician-centric interface module is designed with cognitive ergonomics to ensure interpretability, usability, and minimal cognitive overload under clinical time pressure.
[0040] These and other advantages will be apparent from the present application of the embodiments described herein.
[0041] The preceding is a simplified summary to provide an understanding of some embodiments of the present invention. This summary is neither an extensive nor exhaustive overview of the present invention and its various embodiments. The summary presents selected concepts of the embodiments of the present invention in a simplified form as an introduction to the more detailed description presented below. As will be appreciated, other embodiments of the present invention are possible utilizing, alone or in combination, one or more of the features set forth above or described in detail below.
[0042] These elements, together with the other aspects of the present disclosure and various features are pointed out with particularity in the claims annexed hereto and form a part of the present disclosure. For a better understanding of the present disclosure, its operating advantages, and the specified object attained by its uses, reference should be made to the accompanying drawings and descriptive matter in which there are illustrated exemplary embodiments of the present disclosure.
BRIEF DESCRIPTION OF THE DRAWINGS
[0043] To describe the technical solutions in the embodiments of the present disclosure or in the prior art more clearly, the following briefly describes the accompanying drawings required for describing the embodiments or the prior art. Apparently, the accompanying drawings in the following description merely show some embodiments of the present disclosure, and a person of ordinary skill in the art can derive other implementations from these accompanying drawings without creative efforts. All of the embodiments or the implementations shall fall within the protection scope of the present disclosure.
[0044] The advantages and features of the present disclosure will become better understood with reference to the following detailed description taken in conjunction with the accompanying drawing, in which:
[0045] FIG. 1 illustrates a flowchart outlining sequential step involved in an explainable AI interface system for multimodal medical diagnosis using layered attention and visual attribution maps, in accordance with an exemplary embodiment of the present disclosure;
[0046] FIG. 2 illustrates a conceptual architecture of an explainable AI interface system for multimodal medical diagnosis using layered attention and visual attribution maps, in accordance with an exemplary embodiment of the present disclosure.
[0047] Like reference, numerals refer to like parts throughout the description of several views of the drawing;
[0048] The explainable AI interface system for multimodal medical diagnosis using layered attention and visual attribution maps, which like reference letters indicate corresponding parts in the various figures. It should be noted that the accompanying figure is intended to present illustrations of exemplary embodiments of the present disclosure. This figure is not intended to limit the scope of the present disclosure. It should also be noted that the accompanying figure is not necessarily drawn to scale.
DETAILED DESCRIPTION OF THE DISCLOSURE
[0049] The following is a detailed description of embodiments of the disclosure depicted in the accompanying drawings. The embodiments are in such detail as to communicate the disclosure. However, the amount of detail offered is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the present disclosure.
[0050] In the following description, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the present disclosure. It may be apparent to one skilled in the art that embodiments of the present disclosure may be practiced without some of these specific details.
[0051] Various terms as used herein are shown below. To the extent a term is used, it should be given the broadest definition persons in the pertinent art have given that term as reflected in printed publications and issued patents at the time of filing.
[0052] The terms “a” and “an” herein do not denote a limitation of quantity, but rather denote the presence of at least one of the referenced items.
[0053] The terms “having”, “comprising”, “including”, and variations thereof signify the presence of a component.
[0054] Referring now to FIG. 1 to FIG. 2 to describe various exemplary embodiments of the present disclosure. FIG. 1 illustrates a flowchart outlining sequential step involved in an explainable AI interface system for multimodal medical diagnosis using layered attention and visual attribution maps, in accordance with an exemplary embodiment of the present disclosure.
[0055] An explainable AI interface system for multimodal medical diagnosis using layered attention and visual attribution maps 100 comprises a multimodal data acquisition module 102 configured to receive and integrate heterogeneous medical data. The multimodal data acquisition module 102 is further configured to receive medical imaging data comprising X-ray, magnetic resonance imaging (MRI), computed tomography (CT), ultrasound, and positron emission tomography (PET) scans. The multimodal data acquisition module 102 is further configured to process unstructured textual data including electronic health records (EHRs), discharge summaries, physician notes, and clinical guidelines. The multimodal data acquisition module 102 is further configured to integrate structured numerical data comprising laboratory test results, vital signs, demographic attributes, and medication history.
[0056] The system also includes a layered attention processing module 104 configured to perform intra-modal attention to capture salient features within each modality and inter-modal attention to dynamically weigh cross-modality relationships. The layered attention processing module 104 is further configured to perform temporal attention modeling that captures sequential and conditional dependencies in patient data over time. The layered attention processing module 104 comprises a hierarchical attention network configured to combine intra-modal, inter-modal, and temporal attention scores into a unified representation for diagnostic reasoning.
[0057] The system also includes a visual attribution generation module 106 configured to produce modality-specific attribution maps. The visual attribution generation module 106 is further configured to generate heatmaps over radiological images, highlight salient textual tokens in clinical notes, and emphasize critical numerical variables in laboratory data. The visual attribution generation module 106 is further configured to map feature contributions to specific diagnostic outcomes, thereby enabling clinicians to trace the reasoning path of the system’s predictions.
[0058] The system also includes an interactive clinician-centric interface module 108 configured to present diagnostic reasoning in alignment with clinical workflows. The interactive clinician-centric interface module 108 is further configured to allow user interaction for drilling down into diagnostic reasoning steps, exploring modality-specific contributions, and adjusting contextual patient parameters to observe real-time changes in system output. The interactive clinician-centric interface module 108 is designed with cognitive ergonomics to ensure interpretability, usability, and minimal cognitive overload under clinical time pressure.
[0059] The system also includes a diagnostic decision support engine 110 configured to provide transparent, explainable, and inspectable diagnostic recommendations that support clinician decision-making without replacing clinical judgment.
[0060] FIG. 1 illustrates a flowchart outlining sequential step involved in an explainable AI interface system for multimodal medical diagnosis using layered attention and visual attribution maps.
[0061] At 102, the foundation of the system lies the multimodal data acquisition module, which is responsible for collecting and integrating diverse medical inputs. These inputs may include radiological images such as X-rays, CT scans, or MRIs, textual clinical records like physician notes or electronic health records (EHRs), and structured numerical data from laboratory reports or physiological measurements. By consolidating heterogeneous data into a unified representation, this module ensures that the system can leverage the full spectrum of clinical evidence available, thereby overcoming the limitations of single-modality diagnostic tools.
[0062] At 104, once the raw data is captured and aligned, it is processed by the layered attention processing module. This component plays a central role in identifying patterns and relationships within and across data modalities. Intra-modal attention mechanisms are applied first, focusing on the most critical features within each modality for instance, highlighting suspicious regions in medical images, isolating important phrases within textual records, or flagging abnormal lab results. The system then applies inter-modal attention, where it evaluates and balances the relative contribution of each modality in a diagnostic context. This allows the system to adaptively weigh imaging evidence against textual or numerical information, recognizing that the significance of each modality may vary depending on the clinical case. Additionally, the module incorporates temporal reasoning, enabling it to account for the sequential progression of patient data, such as changes in lab results over time or the evolution of imaging findings across multiple scans.
[0063] At 106, the refined outputs of the layered attention process are then passed to the visual attribution generation module. This module translates complex computational reasoning into interpretable visualizations that can be understood by clinicians. For imaging data, it generates heatmaps overlaid on the scans to pinpoint areas contributing most strongly to the diagnostic suggestion. In textual records, it highlights specific phrases or tokens such as symptom descriptions or diagnostic terms that influenced the outcome. For structured data, it emphasizes key numerical variables, for example, an elevated biomarker or abnormal vital sign. These modality-specific attribution maps provide clinicians with transparency into the model’s decision-making process, ensuring that they can clearly trace how and why a certain diagnostic pathway was prioritized.
[0064] At 108, the system then conveys these processed results to the interactive clinician-centric interface module. This interface is carefully designed to mirror existing clinical workflows, minimizing cognitive burden and time delays in high-pressure medical environments. Within this interface, clinicians can view diagnostic predictions alongside visual attribution maps, navigate through reasoning steps, and interact with the outputs to better understand the system’s assessment. For example, they may drill down into attention layers to explore how different modalities influenced the prediction, or adjust contextual parameters, such as patient demographics or a lab value, to observe how these modifications alter the diagnostic outcome in real time. This interactivity transforms the AI from a static predictor into a dynamic assistant, enhancing transparency and clinical trust.
[0065] At 110, the core of the system is the diagnostic decision support engine, which synthesizes the fused multimodal data, the layered attention outputs, and the attribution explanations to generate diagnostic recommendations. Unlike traditional black-box systems that simply produce predictions, this engine ensures that recommendations are transparent, explainable, and inspectable by the clinician. It is explicitly designed to act as a diagnostic co-pilot, supporting but not replacing the expertise of the medical professional. This approach facilitates shared decision-making, where clinicians can rely on the system’s insights while retaining ultimate control over clinical judgments.
[0066] FIG. 2 illustrates a conceptual architecture of an explainable AI interface system for multimodal medical diagnosis using layered attention and visual attribution maps.
[0067] The process begins on the left side of the diagram, where multiple streams of patient information are introduced into the system. These streams include radiological images, such as X-rays or MRI scans, textual electronic health records (EHRs), and structured laboratory data containing numerical test results and physiological measurements. Each of these modalities carries unique information critical for clinical reasoning, and the system is designed to handle them simultaneously rather than in isolation.
[0068] Once the multimodal data is ingested, it is processed by a hierarchical cross-modal attention fusion module. This module is central to the system’s novelty, as it integrates different layers of attention mechanisms. At the intra-modal level, attention mechanisms highlight the most relevant features within a single modality, such as identifying abnormal regions within an image or key phrases in textual notes. At the inter-modal level, the system weighs the relative contributions of each modality, dynamically adjusting based on the diagnostic scenario. Finally, temporal attention is applied to reflect the progression of clinical conditions over time, allowing the model to consider sequences of patient data in the context of evolving health states. This layered attention structure provides a comprehensive foundation for reasoning across complex and heterogeneous medical inputs.
[0069] The processed and fused representations are then passed into the diagnostic reasoning and prediction module. This component functions as the classifier or risk predictor, generating diagnostic outcomes or risk assessments based on the learned multimodal patterns. Importantly, rather than being a black-box predictor, this module is designed to work hand-in-hand with attribution and explanation mechanisms that illuminate the basis of its outputs.
[0070] To achieve explainability, the system incorporates attribution generator and explanation modules. These modules produce visual and textual explanations that map diagnostic predictions back to contributing features. For radiological images, this may involve heatmaps highlighting abnormal or diagnostically relevant regions. For textual EHRs, the system emphasizes critical tokens or phrases, such as symptom descriptions or discharge notes. In structured lab data, attribution highlights the most influential variables, such as abnormal blood counts or vital signs. These explanations are generated both at the stage of attention fusion and after prediction, ensuring that interpretability is integrated throughout the pipeline rather than added as a superficial layer.
[0071] The final step in the workflow is the clinician-centric interactive XAI interface, where the processed results and explanations are presented in a transparent and usable form. The interface is designed to align with clinical workflows, displaying multimodal attributions alongside diagnostic predictions. For example, an X-ray image may be shown with a highlighted region indicating an abnormality, while textual notes or lab results are simultaneously emphasized to reveal their diagnostic importance. Clinicians can interact with the interface, drilling deeper into reasoning steps, exploring cross-modal contributions, and even adjusting contextual variables, such as patient demographics or laboratory values, to observe real-time changes in system predictions. This ensures that clinicians remain in control, treating the AI system as a diagnostic co-pilot that supports, rather than replaces, their judgment.
[0072] While the invention has been described in connection with what is presently considered to be the most practical and various embodiments, it will be understood that the invention is not to be limited to the disclosed embodiments, but on the contrary, is intended to cover various modifications and equivalent arrangements included within the scope of the appended claims.
[0073] A person of ordinary skill in the art may be aware that, in combination with the examples described in the embodiments disclosed in this specification, units and algorithm steps may be implemented by electronic hardware, computer software, or a combination thereof.
[0074] The foregoing descriptions of specific embodiments of the present disclosure have been presented for purposes of illustration and description. They are not intended to be exhaustive or to limit the present disclosure to the precise forms disclosed, and many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described to best explain the principles of the present disclosure and its practical application, and to thereby enable others skilled in the art to best utilize the present disclosure and various embodiments with various modifications as are suited to the particular use contemplated. It is understood that various omissions and substitutions of equivalents are contemplated as circumstances may suggest or render expedient, but such omissions and substitutions are intended to cover the application or implementation without departing from the scope of the present disclosure.
[0075] Disjunctive language such as the phrase “at least one of X, Y, Z,” unless specifically stated otherwise, is otherwise understood with the context as used in general to present that an item, term, etc., may be either X, Y, or Z, or any combination thereof (e.g., X, Y, and/or Z). Thus, such disjunctive language is not generally intended to, and should not, imply that certain embodiments require at least one of X, at least one of Y, or at least one of Z to each be present.
[0076] In a case that no conflict occurs, the embodiments in the present disclosure and the features in the embodiments may be mutually combined. The foregoing descriptions are merely specific implementations of the present disclosure, but are not intended to limit the protection scope of the present disclosure. Any variation or replacement readily figured out by a person skilled in the art within the technical scope disclosed in the present disclosure shall fall within the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure shall be subject to the protection scope of the claims.
, Claims:I/We Claim:
1. An explainable AI interface system for multimodal medical diagnosis using layered attention and visual attribution maps (100) comprising:
a multimodal data acquisition module (102) configured to receive and integrate heterogeneous medical data;
a layered attention processing module (104) configured to perform intra-modal attention to capture salient features within each modality and inter-modal attention to dynamically weigh cross-modality relationships;
a visual attribution generation module (106) configured to produce modality-specific attribution maps;
an interactive clinician-centric interface module (108) configured to present diagnostic reasoning in alignment with clinical workflows;
a diagnostic decision support engine (110) configured to provide transparent, explainable, and inspectable diagnostic recommendations that support clinician decision-making without replacing clinical judgment.
2. The system (100) as claimed in claim 1, wherein the multimodal data acquisition module (102) is further configured to receive medical imaging data comprising X-ray, magnetic resonance imaging (MRI), computed tomography (CT), ultrasound, and positron emission tomography (PET) scans.
3. The system (100) as claimed in claim 1, wherein the multimodal data acquisition module (102) is further configured to process unstructured textual data including electronic health records (EHRs), discharge summaries, physician notes, and clinical guidelines.
4. The system (100) as claimed in claim 1, wherein the multimodal data acquisition module (102) is further configured to integrate structured numerical data comprising laboratory test results, vital signs, demographic attributes, and medication history.
5. The system (100) as claimed in claim 1, wherein the layered attention processing module (104) is further configured to perform temporal attention modeling that captures sequential and conditional dependencies in patient data over time.
6. The system (100) as claimed in claim 1, wherein the layered attention processing module (104) comprises a hierarchical attention network configured to combine intra-modal, inter-modal, and temporal attention scores into a unified representation for diagnostic reasoning.
7. The system (100) as claimed in claim 1, wherein the visual attribution generation module (106) is further configured to generate heatmaps over radiological images, highlight salient textual tokens in clinical notes, and emphasize critical numerical variables in laboratory data.
8. The system (100) as claimed in claim 1, wherein the visual attribution generation module (106) is further configured to map feature contributions to specific diagnostic outcomes, thereby enabling clinicians to trace the reasoning path of the system’s predictions.
9. The system (100) as claimed in claim 1, wherein the interactive clinician-centric interface module (108) is further configured to allow user interaction for drilling down into diagnostic reasoning steps, exploring modality-specific contributions, and adjusting contextual patient parameters to observe real-time changes in system output.
10. The system (100) as claimed in claim 1, wherein the interactive clinician-centric interface module (108) is designed with cognitive ergonomics to ensure interpretability, usability, and minimal cognitive overload under clinical time pressure.