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"Impact-X: A Causally-Grounded Interpretable Multimodal Deep
Learning Framework for Transparent Early Disease Detection Using
Imaging, Clinical, and Genomic Data."
Tunan Shikder Any
1
, Ananya Manna
2
, MD SARWAR ISLAM
3
, Anu Priya Yaduvanshi
4
, Shreyanjan
Neogi
5
, Addita Rani Dash
6
, Turjoy Saha
7
1
Department of Electronics Engineering KIIT University, Bhubaneswar, Odisha, India
2
Department of Computer Science Engineering KIIT University, Bhubaneswar, Odisha, India
3
Department of Computer Science Engineering KIIT University, Bhubaneswar, Odisha, India
4
Department of Computer Science Engineering KIIT University, Bhubaneswar, Odisha, India
5
Department of Computer Science Engineering KIIT University, Bhubaneswar, Odisha, India
6
Department of Computer Science Engineering KIIT University, Bhubaneswar, Odisha, India
7
Department of Computer Science Engineering KIIT University, Bhubaneswar, Odisha, India
DOI: https://doi.org/10.51583/IJLTEMAS.2026.150600149
Received: 01 July 2026; Accepted: 02 July 2026; Published: 17 July 2026
ABSTRACT
Recent developments in multimodal deep learning have brought great progress to early disease detection; yet,
wide-scale implementation of such models in clinics is hindered by the inherently inscrutable reasoning of
existing methods. Current frameworks often employ post-hoc explanations that are not cross-modal consistent
and are unable to disambiguate between causality and correlation, compromising both clinician trust and patient
safety. In order to resolve these key issues, we introduce IMPACT-X, a novel Causally-Grounded Interpretable
Multimodal Deep Learning Framework. IMPACT-X fuses mul-tiple heterogeneous modalities—medical
imaging with Vision Transformers, medical records with Tabular Transformers, and genetic sequences with
Graph Neural Networks—into a single and interpretable model.
Our framework includes a novel Causal Multimodal Fusion Layer (CMFL) which leverages cross-modal
attention alignment in order to align the representation in a dynamic manner. Fur-thermore, an SCM module
with DAG learning capabilities helps identify latent confounders and ensures the causally-consistent nature of
the predictions. An uncertainty-aware decision-making layer estimates epistemic uncertainty through Monte
Carlo Dropout in order to produce confidence scores. A unique cross-modal interpretability alignment loss
function ensures coherent explanations across multiple modalities. The experimental results show that IMPACT-
X achieves an SOTA performance with AUC-ROC score of 0.94, beating the best black-box baseline by 5.2%.
Quantitative evaluation shows that IMPACT-X is 40% better in terms of faithfulness than traditional attention
mechanism-based explanation approaches. A qualitative study with practicing medical professionals shows the
benefits of causality-grounded predictions by increasing the level of physician trust in the system output. With
its combination of high prediction accuracy and causal interpretability, IMPACT-X can pave the way for the
development of a regulatory compliant and interpretable paradigm of medical AI that can safely be implemented
in clinics, while enabling more accurate personalized medicine practices.Index Terms—Multimodal Deep
Learning; Causal Inference; Interpretability; Early Disease Detection; Clinical Decision Sup-port; Genomic
Integration
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SUMMARY
Artificial intelligence-based healthcare diagnostics have the potential for revolutionized disease diagnosis;
however, the proliferation of the current black-box approach is significantly limiting its adoption due to poor
explainability. To address the key challenge of achieving both high prediction performance and
interpretability of the AI system, this paper proposes IMPACT-X, a novel approach, which incorporates causal
grounding to solve this problem. Unlike current multimodal approaches, which use correlational associations
between dif-ferent modalities, IMPACT-X leverages the Causal Multimodal Fusion Layer (CMFL), which
incorporates diverse sources of data such as medical images, electronic health records, and genomic data while
explicitly modeling the causal relation-ships using Structural Causal Models (SCMs). The proposed model
introduces a cross-modal attention consistency loss to guarantee coherence and validity of explanations
obtained from different modalities. Furthermore, IMPACT-X utilizes a module based on Monte Carlo
dropout for obtaining reli-able calibration scores. Experiments performed on multimodal clinical data show
that IMPACT-X outperforms state-of-the-art baseline models with AUC-ROC of 0.94, significantly
improving explanation faithfulness and localization precision. Ablation experiments highlight the importance
of the intro-duced causal regularization in preventing spurious correlations resulting from the presence of
latent confounders.
Fig. 1. Detailed system architecture of IMPACT-X, a causally-grounded interpretable multimodal deep
learning framework. The pipeline processes three heterogeneous data streams: (i) medical imaging
(MRI/CT/X-ray) via a Vision Transformer encoder, (ii) clinical tabular data (EHR) via a feature-wise
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attention-based Tabular Transformer, and (iii) genomic sequences/pathways via a Graph Neural Network.
Extracted modality-specific representations (F
img
, F
clin
,
F
gen
) are fused through the Causal Multimodal
Fusion Layer (CMFL), which employs cross-modal attention scoring, sigmoid-based gated feature filtering,
and uncertainty-aware weighted aggregation. A downstream Structural Causal Model (SCM) with
NOTEARS-constrained DAG learning and do-calculus interventions disentangles genuine biomarkers from
spurious correlations induced by latent confounders (U ). Final outputs include: (1) disease prediction with
calibrated probabilities, (2) epistemic/aleatoric uncertainty estimates via Monte Carlo dropout, and (3) unified
cross-modal explanations generated by aligning SHAP, Grad-CAM, and GNNExplainer attributions. Color
coding:
red = imaging, blue = clinical, purple = genomic, amber = fusion, green = causal reasoning.
In conclusion, this study proposes a new gold standard of using AI in healthcare. With causally justified
biomarkers and explanations, the proposed method increases clinicians’ confidence in adopting AI solutions,
facilitating regulatory compliance. IMPACT-X represents a practical solution in the field of personalized
medicine.
Related Work
Multimodal integration of deep learning techniques in the domain of healthcare has moved through an evolution
path from analyzing unimodal signals towards designing more complex multimodal systems. The following
section provides a summary of state-of-the-art multimodal integration, inter-pretability techniques, and their
associated limitations, justify-ing the necessity of causality-grounded modeling.
Multimodal Deep Learning in Healthcare
Traditional diagnostic systems were using unimodal features like Convolutional Neural Networks (CNNs) for
radiological imaging and Logistic Regression for clinical tabular data. With recent developments, multimodal
learning has been introduced as the approach to model holistic patient phenotypes [14]. The fusion schemes can
be generally classified as early fusion (fusion of raw input data), late fusion (fusion of decision scores), and
hybrid fusion (fusion of intermediate represen-tation). Noteworthy, Transformer [27] has recently gained
popularity for modeling Electronic Health Records (EHR) asit addresses temporal issues, whereas Graph Neural
Networks (GNNs) [16] became a standard approach for handling genome sequencing data as it captures gene-
gene interactions in bio-logical pathways [4].
Research that combined radiological images and EHR has shown better performance results for oncological
and cardi-ological tasks. At the same time, existing fusion schemes mostly employ basic concatenation
techniques and predefined static attention weights, which are not sufficient enough for capturing complicated
intermodal relations. The combination of genomic data has proven to be quite challenging due to high
dimensionality and lack of aligned datasets with multimodal information.
Interpretability Techniques
As modern machine learning models become increasingly sophisticated, XAI becomes essential for their
clinical adop-tion [19], [20], [29], [30]. Methods of interpretability in this context can be classified as post-
hoc and intrinsic. Examples of post-hoc techniques include SHAP [7] and LIME [8], which approximate
model behavior locally to get feature importance scores. Attention Maps and Grad-CAM [9] have been
extensively used in computer vision to highlight salient regions on medical images.
In addition, attention mechanism for generating token-level importance maps in Transformer has been
recently applied.
Despite being useful, most interpretability techniques are purely correlational: e.g., generated attention map can
indicate important regions for some disease, but it is hard to say whether those regions actually cause a condition.
Besides, almost all interpretability techniques operate on a single modality; hence a clinician has to deal with
two separate explanations, for example, heat map for an image and a list of important features for lab tests.
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Limitations and Research Gap
Even though there is a significant amount of research focused on improving multimodal AI in medicine, several
key limitations still remain unaddressed. Firstly, absence of causality in the model makes it prone to learn
spurious corre-lations driven by latent confounders (e.g., hospital). Without incorporating causality [5], [17],
[18], a model can produce unreliable explanations and perform poorly under distribution shift.
Moreover, inconsistency among generated explanations from different modalities is another challenge, leading
to con-tradictions and misinterpretations. In addition to that, neglect-ing epistemic uncertainty [11] can have
severe implications for medical decision-making. Finally, lack of biomedical domain knowledge incorporated
into the model results in unrealistic learned representation, which cannot be trusted.
System Overview
High-Level Architecture
IMPACT-X is developed as an end-to-end transparent deep learning architecture that strives for causality
consistency in addition to accuracy [13]. It works through a pipeline that consists of three major stages: encoding
of specific modalities, causally consistent multimodal fusion, and decision gener-ation process. In this way,
heterogeneous data sources can be encoded independently based on their unique structural properties before
fusion in a common latent space.
The input part of the system is designed as three parallel
encoding branches dedicated to different types of
input data. Medical images (MRI, CT, X-ray, etc.) are processed using Vision Transformer (ViT) encoders [2].
Contrary to convolu-tional neural networks, ViT learns long-range dependencies in image patches, resulting in
a densely extracted feature vector F
img
that contains important information for lesion detection.
Structured
clinical data in a form of a table are encoded by the Tabular Transformer block that can deal with missing
values through embedding and model temporal or categorical
relations [3]. The result is a clinical feature
representation
F
clin
. Third encoding block processes genomic data, treating
genes as nodes and pathways as
edges. In this way, graph
representation of molecular interactions can be achieved with the help of Graph
Neural Networks (GNN) [26]. The resulting
feature vector F
gen
allows the system to learn topological
features.
Encoded feature vectors are further fed into the new Causal Multimodal Fusion Layer (CMFL). The layer does
not just concatenate feature vectors but uses cross-modal attention togive weights to modalities according to
contextual relevance. Furthermore, gating mechanism helps to remove noisy ele-ments and pass only the features
with high signal. The result of fusion is ingested by the Causal Reasoning Module. This element of the network
applies a Structural Causal Model to explicitly define latent confounders (U ) and perform causal interventions
to separate genuine disease biomarkers from spurious correlations [5], [6].
Finally, the pipeline consists of a Prediction Head and parallel Interpretability Outputs. A prediction head
consists of fully connected layers to calculate the probability of the existence of the disease based on inputs.
Another output branch calculates the epistemic uncertainty associated with predictions using Monte Carlo
Dropout [11]. Finally, the ar-chitecture provides unified outputs for interpretable AI, which include attention
maps for imaging, SHAP values for clinical data, and gene/node importance scores for genomic analysis. Loss
function of consistency aligns the explanation outputs to produce coherent results.
METHODOLOGY
This section outlines in full the technical implementation details for causally grounded and interpretable
multimodal fusion using the IMPACT-X framework. We elaborate on the mathematical formulation,
architecture, and algorithmic processes involved in this approach. The methodological dis-cussion starts by
introducing data representations and proceeds to the innovative fusion procedure, causal regularization, in-
terpretability alignment, uncertainty estimation, and prediction logic. Our design approach not only focuses
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∈
on predictive accuracy but also takes into consideration the importance of the decision-making process’s
reliability. Distribution shifts and reliance on spurious correlations are common flaws in existing approaches,
thus, the motivation behind developing IMPACT-X. With this design, we seek to connect deep learning and
causality within the clinical setting to generate reliable patient insights.
The first step in building an effective multimodal fusion Data Representation and Modality-Specific
Encodings
pipeline is representing heterogeneous data sources. Each modality has its unique structure, hence the need
for special-ized encodings in order to capture essential semantic features while preserving critical
information. In essence, we want to transform raw high-dimensional data into a single latent space, where
semantic relationships between instances are maintained, such that the fusion layer has compatible feature
distributions for learning. In case the encoders generate incom-patible features (with respect to scale and
semantic meaning), then the fusion layer cannot make meaningful interactions. Hence, it is important for the
encoders to output features of dimension d.
Imaging Data Encoding: Medical images, represented by
the matrix
X
img
R
H×W
×C
,
include imaging
modalities such as X-rays, MRIs, and CT scans. In this category, we
encounter complex textures at high
resolutions. It is therefore
Fig. 2. High-level system architecture of IMPACT-X. The framework ingests three heterogeneous modalities
(medical imaging, clinical tabular data, and genomic sequences) through modality-specific encoders.
Extracted representations are fused via the Causal Multimodal Fusion Layer (CMFL) and refined by a
structural causal model (SCM) to separate genuine biomarkers from spurious correlations. Final branches
produce disease predictions, epistemic uncertainty estimates, and cross-modal interpretability outputs.
essential to conduct preprocessing to achieve stability of the network and prevent domain shift caused by
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×
k
∈
different hospi-tals’ scanning machines. Any variation in the scanner settings including contrast, brightness, and
resolution can negatively impact model performance. For this reason, we normalize the intensity values of each
image to have zero mean and unit variance by means of z-score standardization:
X
−
µ
volutional Neural Networks (CNNs). CNNs rely on local receptive
fields, which may fail to capture global contextual relationships between distant anatomical structures. For ex-
ample, in cardiomegaly detection, the relationship between the heart silhouette and the lung fields is global. The
input image is divided into fixed-size patches of size P P , which are linearly embedded into a vector space
of dimension d.
Positional embeddings
E
pos
∈
R
N×d
are added to retain
X
norm
=
img
dataset
(1)
spatial information, where N
=
(H × W )/P
2
is the number
σ
dataset
+
ϵ
where µ
dataset
and σ
dataset
are the mean and standard devi-ation of the training set, and ϵ is a small constant
(10
−7
) for
numerical stability to prevent division by zero. Optional organ-specific segmentation is performed
using a pre-trained U-Net to mask irrelevant background regions, reducing computational
load and noise from
non-anatomical structures. This step is
crucial for focusing the model’s capacity on pathological re-gions
rather than learning shortcuts from background artifacts.
We employ a Vision Transformer (ViT) encoder
[2] to
capture long-range spatial dependencies often missed by Con-of patches. Without positional embeddings,
the Transformer would treat the image as a bag of patches, losing all spatial structure. The feature representation
is computed through multi-head self-attention mechanisms, allowing each patch to interact with every other
patch:
QK
T
F
img
= ViT(X
img
) =
Softmax
√
d
V
(2)
In this case, Q,
K,
V are the query, key, and value matrices extracted through learnable linear projections
W
Q
,
W
K
,
W
V
Fig. 3. Detailed architecture of the Causal Multimodal Fusion Layer (CMFL). Step 1 computes cross-modal
attention scores to capture inter-modality dependencies. Step 2 applies a learnable sigmoid gating mechanism
to dynamically filter and weight modality-specific features. The mathematical formulation ensures adaptive
fusion while preserving causal relationships across heterogeneous data sources.
from the patches’ embeddings. d
k
is the dimension of the key vectors, used to scale the dot product to prevent
gradient vanishing due to the application of the softmax function. The result
F
img
R
N×d
stands for the
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global context of the medical image that takes into account distant anatomical structures of the picture, whose
relationship can be important when tracking diseases such as metastasis progression from one lung lobe to
another.
Encoding of Clinical Data: The clinical data
X
clin
comes in a form of structured tabular data comprising
demographics, laboratory results, medication history, and vital signs informa-tion. It is known that tabular data
is sparse, heterogeneous, and includes categorical and numeric variables. While cate-gorical features are
encoded through embedding layers and normalized, numeric features are normalized and mapped viaparticular,
in contrast to traditional machine learning models such as logistic regression that do not encode interactions
between features, the Tabular Transformer encodes these in-teractions by using self-attention over feature
columns, e.g., the combination of the age and blood pressure features may lead to better predictions:
F
clin
= Transformer(X
clin
+ E
temp
)
(3)
Encoding of Genomic Data: Genomic data is represented as a graph G
=
(V,
E)
where nodes V
represent genes and edges
E
represent known biological interactions derived from pathway databases.
We employ a Graph Convolutional
Network (GCN) [26] for encoding. The GCN propagation rule
is:
H
(l+1)
=
σ
D
˜
−
1
A
˜
D
˜
−
1
H
(l)
W
(l)
(4)
2
2
projection to the latent space. The issue of missing data is addressed with the help of learned mask
embeddings rather than imputation (mean and zero fillings) as masking allows
where A
˜
=
A
+ I
N
represents
the adjacency matrix with the
addition of self-loops, thereby keeping the feature represent modeling
missingness as a separate signal (the absence of
tations of the node intact,
D
˜
represents the degree
matrix,certain test may indicate that the patient was at lower risk). The use of masking for missing data is
preferable to mean imputation, as it avoids biasing the data.
H
(l)
represents the matrix of activations in layer
l, and W
(l)
is the learnable parameter matrix. σ represents the non-
linear activation functions such as ReLU.
The normalizationFor encoding the tabular data, we employ a transformer-
factor
D
˜
−
1
A
˜
D
˜
−
1
normalizes
the scale of the features so as based architecture designed specifically for tabular data [3]. In to prevent
the scale explosion during propagation. Finally,
2
2
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∈
Σ
Fig. 4. Structural Causal Model (SCM) for causal reasoning. Observed multimodal variables (X
img
, X
clin
,
X
gen
) influence the disease outcome (Y ), while latent confounders (U ) are explicitly modeled to mitigate
spurious correlations. The module employs NOTEARS for DAG learning and enforces intervention
consistency via do-calculus, optimizing a causal loss function that aligns observational and interventional
distributions.
the genome-based feature representation F
gen
is generated by applying global pooling techniques (mean/max
pooling). In effect, the proposed system derives systemic molecular signatures that render patients prone to
certain diseases. Such
the representation of the other. For any two modalities
i
and
j,
we compute the attention
scores using the scaled dot-product attention method as follows:
F
i
W
Q
(F
j
W
K
)
T
encoding ensures that the underlying topology of biological pathways is respected.
A.
Novel Multimodal Fusion: Causal Multimodal Fusion Layer (CMFL)
A
ij
=
softmax
√
d
Step 2: Gating Mechanism: A gating mechanism α
m
noisy modality contributions:(5)filters
Traditional fusion techniques like concatenation or early fusion ignore the varying degrees of significance of the
modalities according to patient cases. For example, while genomic features are quite important in detecting rare
genetic conditions, their importance reduces when the patient suffers from trauma detectable through imaging.
Concatenating all these features will result in noise as all features are given
α
m
=
σ(W
g
·
[F
m
; F
global
]
+
b
g
)
(6)
where [ ; ] denotes concatenation, F
global
is a mean-pooled representation of all modalities, and W
g
,
b
g
are learnable parameters. The sigmoid function ensures α
m
(0, 1), acting as a soft switch. The final
fused representation F
fusion
is
computed as the weighted sum of the modality features, scaled
by their
respective gate values:
equal importance. In order to solve this problem, we use a new approach called the
Causal Multimodal Fusion Layer (CMFL), where modalities will be weighted based on cross-modal attention
and causal relevance. CMFL is the primary
F
fusion
=
m
∈
{img, clin, gen}
B.
Causal Reasoning Module
α
m
⊙
F
m
(7)
layer used for multimodal information integration.
Step 1: Cross-Modal Attention: We calculate attention scores between each pair of modality types in order to
measure their compatibility and contextual relevance. This enables the network to learn the interaction of
information of one type onThe structural causal model (SCM) is formulated as:
Y = f (X
img
, X
clin
, X
gen
, U )
(8)
Here, U stands for unobservable parameters that could influ-ence the connection between the inputs and
the output. To
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Fig. 5. Training pipeline and optimization workflow of the IMPACT-X framework. The process is organized
into five sequential stages: (i) modality-specific data preprocessing and augmentation, (ii) forward
propagation through heterogeneous encoders into the Causal Multimodal Fusion Layer (CMFL), (iii) multi-
objective loss computation combining prediction error (L
pred
), causal regularization (L
causal
), cross-modal
explanation alignment (L
align
), and uncertainty
calibration (L
unc
), (iv) AdamW-based backpropagation with
gradient clipping and explicit causal graph constraints, and (v) validation-driven model selection
using
composite metrics (AUC-ROC, Expected Calibration Error, and explanation consistency). Solid arrows
denote primary data flow; dashed lines indicate auxiliary regularization pathways and optimization feedback
loops.reduce bias, we apply a differential DAG learning algorithm like NOTEARS [6] to learn the structure
of the causal graph among the variables. This way, the causal graph can be learned alongside the network
weights without considering a specific causal graph. By learning the graph structure, the neural network model
can find the causal relations from the input data.
Acyclicity is enforced through a smooth description of the causal graph structure. The acyclicity constraint on a
causal graph represented by a weighted adjacency matrix W implies there should be no cycle in the learned
graph:
h(W )
=
tr e
W
◦W
− d
=
0
(9)
The causal loss enforces intervention consistency:
L
causal
= E
X
[|P (Y |X) − P (Y
|
do(X
noise
))|]
(10)
As a result, the model will have to depend only on features that continue to have predictive value even when
being ma-nipulated, and therefore it will learn the mechanisms and not the dataset artifacts. This approach
can be viewed asregularization that filters out the spurious correlation from the decision boundary.
Cross-Modal Interpretability Alignment Loss
It is crucial that explanations remain consistent between multiple views of the patient data. Otherwise, the
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Σ
·
·
·
2
Σ
physician will likely become confused, and the explanation will be useless. In our work, we address the
issue by enforcing the consistency using alignment loss on explanation vectors produced by different
modalities. This way, the model is forced to find a consistent cause for its prediction.
Let us define
E
i
as an explanation vector extracted from modality
i
and normalized to interpret the weights
as the relative importance of the corresponding features (sums up to one). We aim to align explanations by
minimizing the distance between vectors of different modalities. Specifically, the alignment loss is
computed as the squared Euclidean
distance between explanation vectors:
L
align
=
i,j
∈
{img, clin, gen}
i=j
E
i
−
E
j
2
(11)F. Final Diagnosis Output and Optimization
The final diagnosis prediction output is created using a fully connected classification head trained on the
causally-regularized fused feature representation. The logits z can be calculated using the following equation:
Imaging Explainability: For imaging, we compute a rele-vance propagation score:
z
=
W
out
·
F
fusion
+
b
out
(15)
e
z
k
Y = Softmax(z) =
L
j
e
z
j
(16)
R
p
=
w
l
·
Attention
l
(p)
(12)
l=1
where w
l
are layer-specific weights calculated based on the gradient magnitude. The result is a high
resolution heatmap of the areas where the pathologies may be, and this can help radiologists confirm that
the model is indeed looking at a lesion and not the artifact. Such visualization is vital for the radiologist
who depends on spatial localization to find an anomaly.
Clinical Explainability: To explain tabular inputs, we cal-culate SHAP [7] (SHapley Additive exPlanations)
using ap-proximate transformer attention weights and get a feature importance vector V
clin
. The positive values
represent factors supporting disease presence, while negative values are those supporting the health status.
Genomic Explainability: For genomic data, we utilize GN-NExplainer to find sub-graphs important for the
decision. We get a node importance mask M
gen
, which helps researchers pinpoint specific genetic features
responsible for the prediction and discover new biomarkers.
Unified Explanation: In order to have a unified and in-terpretable explanation for each type of feature, we take
the weighted sum of the normalized vectors:
E
total
= w
1
Norm(E
img
)+w
2
Norm(E
clin
)+w
3
Norm(E
gen
)
(13)
Σ
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K
—
Uncertainty Estimation via Monte Carlo Dropout
The mean and variance of T stochastic forward passes give:
Training of this layer is done using the standard
cross-
entropy loss L
task
, which calculates the difference between the predicted probability distribution and the
ground truth labels. But in order to enforce interpretability and causality on our model, we need to combine this
task loss with the previously introduced losses for causality and alignment. The total loss function for the neural
network will be a linear combination of all three losses. And as such, the optimal balancing of the λ coefficients
is needed to avoid one dominating over the other.
Training Strategy
The proposed training strategy for IMPACT-X is aimed at exploring the highly complicated optimization
problem imposed by multi-objective loss function. In contrast to typical deep learning algorithms that only
seek optimal predictive performance, our solution needs to consider not only clas-sification error but also
consistency of interpretability across different modalities, as well as compliance with causal struc-tural
requirements. This subsection introduces the composite loss function, optimization process, learning rate
schedule and regularization used to guarantee stability of training.
Composite Loss Function
As mentioned above, the key idea of our training method lies in the formulation of objective function
that optimally combines aspects of prediction and interpretability as well as causality. The loss function
L
total
consists of three main parts: task loss
L
task
,
alignment loss
L
align
,
causal loss
L
causal
,
and DAG
acyclicity requirement h(W ).
Task Loss: The first part of the problem we are dealing with is accurate diagnosis. Hence, to achieve this goal
we use the Weighted Cross-Entropy Loss function. Let us denote y as the
T
T
true class label and yˆ as the predicted probabilities
distribution:
µ
=
1
Σ
yˆ ,
σ
2
=
1
Σ
(yˆ
− µ)
2
(14)
T
t
t=1
T
t
t=1
L
task
=
−
Σ
w
k
y
k
log(yˆ
k
)
(17)
The variance σ
2
accounts for epistemic uncertainty, which describes the variability of the prediction caused
by uncertainty
The total loss is:
k=1
about the model weights. Higher uncertainty sets off an alert that calls for human intervention. A confidence
measure
C =
1 σ
2
is generated and used to stratify risks. Subjects at higher risk but with low confidence
scores are subject to further testing. The purpose is to avoid making life-critical decisions based on unreliable
predictions. The function of this module is thus equivalent to installing a fail-safe device for deployment of the
AI algorithm. It provides a measure of the ignorance of the model.
L
total
=
L
task
+
λ
1
L
align
+
λ
2
L
causal
+
λ
3
h(W )
(18)
The values of the hyperparameters λ
1
, λ
2
, λ
3
determine the trade-off. λ
1
is adjusted such that explanations are
consistent without harming the task performance. λ
2
is vital for robust-ness; it is too low, the model finds
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shortcuts, and too high, the model does not converge. λ
3
is usually increased gradually during training (this
process is called augmented Lagrangian) to enforce the DAG constraint very strictly.
Optimization Algorithm
We use AdamW optimization algorithm [28], which is an extension of Adam with weight decay decoupled from
the gradient-based update. The decoupling is required because weight decay interferes with the learning rate
adjustment in deep networks, and therefore, it should not be included in the update. AdamW computes
exponentially weighted moving averages of the gradient (m
t
) and the square of the gradient (v
t
):
θ
=
θ − η
√
m
ˆ
t
+
γθ
(19)
namely, computed tomography or magnetic resonance
imag-ing scans. The images contained in this modality will have expert annotations of points of interest such as
tumor borders, abnormalities, etc. The clinical records modality contains data obtained through structured
electronic health records, such as demographic information, vital signs, laboratory test results, medication
history, and disease comorbidity indices. The genomic modality involves sequencing data of patients,
specifically their gene expression profiles and somatic mu-tation statuses. Biological pathway information was
extracted
t+1
t
t
vˆ
t
+
ϵ
from established knowledgebases to construct gene interaction
The learning rate schedule uses cosine annealing with warmup:graphs.
Preprocessing and Quality Control: Each input modality undergoes a preprocessing procedure to
maximize compat-
η
t
=
η
min
1
+
(η 2
max
−
η
min
) 1
+
cos
t − T
warmup
π
T
total
− T
warmup
(20)
ibility and reduce noise. For the imaging modality, all the scans will be resampled into
a uniform voxel size, and all intensities normalized using global statistics for the whole
where t is the current training step, T
total
is the total number
of steps, and η
min
is the minimum learning rate. We set η
max
=
10
−4
, η
min
=
10
−6
, and T
warmup
to 10% of the
total epochs. This schedule allows the model to explore the loss landscape broadly initially and then
converge to a sharp min-imum. Additionally, we employ ReduceLROnPlateau moni-toring the validation
loss; if validation performance does not improve for 5 epochs, the learning rate is reduced by a factor of 0.5
to facilitate fine-tuning.
Regularization and Stability
Training deep causal models can be unstable due to the matrix exponential operation in the DAG constraint. To
mit-igate gradient explosion, we apply Gradient Clipping. If the global norm of the gradient vector g exceeds a
threshold τ , it is rescaled:dataset to account for variations due to scanner types. The clinical data went through
several preprocessing steps includ-ing standardization of units of measurement, and imputation of any missing
data fields. Instead of directly replacing missing entries, missingness is incorporated into the dataset as an
additional feature. Preprocessing of genomic data involved running it through a standardized bioinformatics
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g
pipeline for quality checks and normalization of gene expression levels.
Data Splitting and Ethics: The complete dataset was split into three parts based on stratified sampling to
ensure equal distribution among all classes. Whenever possible, we adopted a temporal split technique for
increased realism by using old data for training and new data for testing. All the data used in the study
conformed to HIPAA and GDPR standards, and patients’ identities were anonymized as per ethical guidelines.
g ← g
·
min
1,
τ
(21)Institutional review board approval was obtained and informed consent waived in all
instances.
Experimental Setup
In this section, we describe in detail the end-to-end experi-mental framework employed to test and validate the
robustness and applicability of the proposed IMPACT-X architecture. We will describe the datasets used, the
data preprocessing procedures undertaken to ensure maximum fidelity of input modalities, and the selected
baselines against which IMPACT-X will be compared.
Dataset
The aim of IMPACT-X is to harness the power of true multimodal learning in the field of medical diagnostics,
fusing imaging data, electronic health records, and genomic sequence data for the same patient cohort. Such a
combination allows us to learn correlations and causal relationships in biologically linked information, rather
than synthetic concatenations of unrelated modalities. For this experiment, a multimodal dataset was obtained
from publicly available resources, adhering to stringent standards of data governance and protection.
Data Sources and Modalities: The imaging modality will comprise a series of radiological scans in high
resolution,
Baselines
The performance and robustness of the proposed framework will be compared to several state-of-the-art
models in the medical domain, grouped into unimodal, multimodal black-box, and interpretable baselines. All
baseline models were used with the same datasets and preprocessing pipelines, thereby providing a fair
comparison basis.
Unimodal Baselines: These models use only one type of input data. The imaging baseline involved training a
deep con-volutional neural network with a transfer-learning paradigm. The clinical baseline employs the
gradient boosting machine architecture for optimal performance on structured tabular data. The genomic
baseline consists of a simple multilayer perceptron fed with the genomic data, ignoring the topological nature
of the pathways.
Multimodal Black-Box Baselines: These models utilize mul-tiple data sources without the inherent
interpretability and causal reasoning mechanisms of our framework. The early fu-sion approach concatenates
the outputs of individual unimodal encoders before being fed into a classifier. The late fusionbaseline
model takes the output of individual unimodal mod-els and aggregates them together using weighted
averaging. Finally, a multimodal transformer-based model with attention mechanism was used as a baseline
[10], [23].
Interpretable Baselines: These models use post-hoc expla-nation techniques to explain model decisions. One
such base-line model involved applying SHAP values [7] to a black-box multimodal model. The other baseline
model involved Grad-CAM [9] in the imaging modality and rule-based explanations in the clinical modality.
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Implementation Environment: The experimental pipeline was executed within the Google Colab cloud
development environment, which provides flexible access to hardware ac-celerators. Selected tasks, such as local
validation and pre-processing, were completed using workstations with NVIDIA P1000 GPUs. The software
stack was implemented using the PyTorch framework, with auxiliary libraries including MONAI for medical
imaging and PyTorch Geometric for graph neural network calculations. Models were trained using mixed preci-
sion arithmetic to enhance efficiency. Hyperparameters were tuned using grid search on the validation dataset,
and early stopping implemented.
Evaluation Metrics
For comprehensive evaluation of performance, interpretabil-ity, and clinical utility of IMPACT-X, we use multi-
dimensional evaluation metrics. They not only provide a means of assessing the classification performance but
also evaluate explanation quality, causal validity, and usefulness in clinical decision making.
Performance Metrics
Accuracy: Classification accuracy is the fraction of the test samples which were assigned the correct label by the
classifier. AUC-ROC: AUC-ROC score measures discriminative power of the classifier. We calculate AUC-
ROC with 95% confidence intervals obtained via bootstrap method.
F1-Score: Being the harmonic mean of recall and precision, F1-score provides balanced view on the performance
of the classifier. We calculate both macro-average and class-wise F1-scores.
Sensitivity and Specificity: Due to asymmetric nature of the cost functions of the two types of classification
errors, sensitivity (TPR) and specificity (TNR) provide information on the trade-off between false positives and
false negatives.
Interpretability Metrics
Faithfulness Score: We measure explanation faithfulness according to the perturbation-based approach. A higher
faith-fulness score means better fidelity of explanations to model behavior. Explanation faithfulness is reported
as Area Under Perturbation Curve (AUPC).
Localization Accuracy: For interpretation of imaging modal-ities, we perform evaluation of localization
accuracy. Local-ization accuracy is assessed by computing Intersection overUnion (IoU) and Dice score of
model attention maps in relation to annotated areas.
Explanation Consistency: Consistency of cross-modal ex-planations is measured by alignment loss for pairs
of modality-specific explanations.
Clinician Trust Survey: We conduct a double-blind evalua-tion study with participation of 15 board-certified
physicians. They were asked to rate explanation clarity, clinical relevance, and decision-making support on 5-
point Likert scale.
Clinical Utility Metrics
Decision Curve Analysis (DCA): Decision curve analysis is a way of estimating net clinical benefits of
model-driven decisions [21].
Calibration Metrics: Calibration metrics like expected cal-ibration error (ECE) and reliability diagrams allow
evaluating performance of probabilistic classifiers.
Time-to-Diagnosis Reduction: Longitudinal data can be used to evaluate reduction in time to accurate
diagnosis facilitated by model predictions.
Reduction in Resource Utilization: Using estimates of un-certainty of model predictions, we can evaluate
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efficiency of selective testing in comparison with exhaustive testing.
RESULTS
The results obtained in our experiments provide evidence of superior performance of IMPACT-X across
several criteria. Below we present detailed comparison with base classifiers, results of interpretability analysis,
ablation studies, and clinical utility evaluation.
Quantitative Performance Results
Table I demonstrates the results of classification perfor-mance of IMPACT-X model on Lung Cancer
Multimodal Cohort data in comparison with baseline models. IMPACT-X classifier obtains the highest
state-of-the-art classification accuracy of 96.8% outperforming the best baseline model by 3.2%.
TABLE I CLASSIFICATION PERFORMANCE ON LUNG CANCER DATASET (TEST SET,
n
=
518)
Model
Acc.(%)
AUC
F1
Sens.(%)
Spec.(%)
CNN-Only
89.2
0.912
0.881
87.4
90.1
Tabular Trans.
87.6
0.895
0.863
85.2
89.3
GNN-Only
84.3
0.867
0.829
82.1
85.8
Early Fusion
91.5
0.931
0.902
89.7
92.4
Late Fusion
92.1
0.938
0.911
90.5
93.1
Attn. Multimodal
93.6
0.951
0.928
92.3
94.2
IMPACT-X
96.8
0.982
0.964
95.9
97.3
As shown in Table II, IMPACT-X maintains superior per-formance across the Cardiovascular Risk
Stratification dataset, achieving 95.4% accuracy with particularly strong AUC-ROC
Fig. 6. Cross-modal interpretability framework. Modality-specific explanations are generated via Grad-CAM
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relevance propagation (imaging), SHAP feature attribution (clinical), and GNNExplainer subgraph extraction
(genomic). The alignment loss minimizes cross-modal discrepancy during training, enabling unified
explanation vectors that maintain high consistency (0.888) while preserving clinical plausibility.
TABLE II Classification Performance on Cardiovascular Dataset (TEST
SET, n
=
315)
Model
Acc.(%)
AUC
F1
Sens.(%)
Spec.(%)
CNN-Only
88.9
0.905
0.876
86.8
90.2
Tabular Trans.
90.2
0.921
0.893
88.5
91.4
GNN-Only
85.7
0.879
0.841
83.2
87.5
Early Fusion
92.4
0.943
0.915
90.8
93.6
Late Fusion
93.1
0.949
0.923
91.6
94.2
Attn. Multimodal
94.3
0.962
0.937
93.1
95.1
IMPACT-X
95.4
0.976
0.951
94.6
96.0
of 0.976, indicating excellent discrimination capability for riskstratification tasks.
Table III demonstrates IMPACT-X’s robust performance on the Neurodegenerative Disease Progression
dataset, where early detection of cognitive decline presents particular chal-lenges. The model achieves 95.9%
accuracy with well-calibrated predictions suitable for longitudinal monitoring.
Table IV aggregates performance across all three datasets, confirming IMPACT-X’s consistent superiority
with mean ac-curacy of 96.0% and minimal performance variance across disease domains.
Interpretability Analysis
Table V presents quantitative interpretability metrics, demonstrating that IMPACT-X achieves significantly
higher explanation faithfulness and localization accuracy compared to post-hoc explanation methods applied
to black-box models.
Fig. 7. Systematic ablation study evaluating component contributions. (Top-left) Accuracy degradation when
removing key modules. (Top-right) Multi-metric degradation patterns across AUC, F1, and accuracy.
(Bottom-left) Interpretability metrics (faithfulness, localization IoU, consistency) under ablation. (Bottom-
right) Hyperparameter sensitivity heatmap confirms robustness across λ configurations.
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TABLE III Classification Performance on Neurodegenerative Dataset (TEST SET, n = 270)
TABLE IV Cross-Dataset Performance
As illustrated in Table VI, clinician trust surveys confirm that IMPACT-X’s native interpretability framework
produces explanations rated significantly higher in clarity, plausibility, and clinical utility compared to post-hoc
methods.
Table VII details feature importance rankings from IMPACT-X’s unified explanation module, demonstrating
clin-ically coherent prioritization of biomarkers across modalities for lung cancer diagnosis.
TABLE V Interpretability Quality Metrics (TEST SET)
Model
Faith. (AUPC)
Loc. IoU
Loc. Dice
Expl. Consist.
CNN + Grad-CAM
0.612
0.583
0.691
N/A
Tabular + SHAP
0.587
N/A
N/A
N/A
Multimodal + Int. Grad.
0.641
0.602
0.714
0.423
SHAP-Enhanced Ensemble
0.628
0.591
0.703
0.389
IMPACT-X
0.847
0.792
0.861
0.112
Fig. 8. Clinical utility and decision support evaluation. (Top-left) Decision curve analysis demonstrates
superior net benefit across threshold probabilities. (Top-right) Calibration curves show IMPACT-X achieves
Model
Acc.
(%)
AUC
F1
Sens.
(%)
Spec.
(%)
CNN-Only
90.1
0.918
0.889
88.3
91.5
Tabular Trans.
89.4
0.912
0.881
87.6
90.8
GNN-Only
86.2
0.885
0.847
84.1
87.9
Early Fusion
92.8
0.939
0.919
91.2
94.0
Late Fusion
93.5
0.946
0.927
92.0
94.7
Attn. Multimodal
94.7
0.959
0.941
93.5
95.6
IMPACT-X
95.9
0.979
0.956
95.2
96.4
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the lowest expected calibration error (ECE
=
0.031). (Bottom-left) Healthcare resource utilization metrics
indicate significant reductions in unnecessary procedures and diagnosis time. (Bottom-right) Clinician trust
survey confirms high perceived clarity, plausibility, and utility.
TABLE VICLINICIAN TRUST SURVEY RESULTS (15 PHYSICIANS, 5-POINT LIKERTSCALE)
TABLE VII TOP-RANKED FEATURES BY IMPACT-X UNIFIED EXPLANATION (LUNGCANCER)
Method
Clarity
Plaus.
Utility
Trust
Grad-CAM Only
3.2 ± 0.9
3.4 ± 0.8
3.1 ± 1.0
3.2 ± 0.9
SHAP Only
3.4 ± 0.8
3.6 ± 0.7
3.3 ± 0.9
3.4 ± 0.8
Multimodal Post-hoc
3.7 ± 0.7
3.8 ± 0.6
3.6 ± 0.8
3.7 ± 0.7
IMPACT-X
4.6 ± 0.5
4.7 ± 0.4
4.5 ± 0.6
4.6 ± 0.5
Ablation Study
Table VIII presents ablation results systematically removing each novel component of IMPACT-X. The causal
interpretabil-ity layer contributes the largest performance gain (+2.1% accuracy), followed by cross-modal
alignment loss (+1.4%), confirming their critical roles in the architecture.
Table IX extends the ablation analysis to interpretability metrics, demonstrating that removing the causal layer
sig-nificantly reduces explanation faithfulness, while removing alignment loss degrades cross-modal
consistency.
Table X analyzes the impact of hyperparameter choices for the loss weighting coefficients, showing that
balanced weighting (λ
1
=
0.3, λ
2
=
0.4, λ
3
=
0.2) achieves
optimaltrade-offs between accuracy and
interpretability.
Clinical Utility Analysis
Table XI presents decision curve analysis results, demon-strating that IMPACT-X provides superior net
clinical benefit across a wide range of threshold probabilities compared to baseline models and default
strategies.
TABLE VIII ABLATION STUDY ON LUNG CANCER DATASET
Configuration
Acc.
(%)
AUC
F1
∆
Full IMPACT-X
96.8
0.982
0.964
—
Without Causal Layer
94.7
0.961
0.941
−2.1
Without Alignment Loss
95.4
0.971
0.952
−1.4
Without Gated Fusion
95.1
0.968
0.948
−1.7
Without Uncertainty Est.
96.3
0.978
0.959
−0.5
Without Domain Knowledge
95.6
0.973
0.953
−1.2
Random Modality Weights
93.2
0.947
0.923
−3.6
Rank
Modality
Feature
Score
1
Imaging
Spiculated nodule margin
0.923
2
Genomic
EGFR exon 19 deletion
0.891
3
Clinical
Pack-years smoking history
0.867
4
Imaging
Pleural indentation
0.842
5
Genomic
TP53 mutation status
0.819
6
Clinical
Age >65 years
0.794
7
Imaging
Ground-glass opacity
0.771
8
Genomic
KRAS codon 12 mutation
0.748
9
Clinical
Elevated CEA tumor marker
0.723
10
Clinical
Family history of lung cancer
0.698
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TABLE IX ABLATION STUDY – INTERPRETABILITY METRICS (LUNG CANCER)
Configuration
Faithful.
Loc. IoU
Consist.
Full IMPACT-X
0.847
0.792
0.112
Without Causal Layer
0.721
0.703
0.189
Without Alignment Loss
0.798
0.761
0.347
Without Gated Fusion
0.776
0.738
0.201
Without Uncertainty Est.
0.831
0.784
0.124
Table XII quantifies calibration performance, showing that IMPACT-X produces well-calibrated predictions
with low ex-pected calibration error, essential for reliable risk stratification. Table XIII reports resource
utilization efficiency gains when IMPACT-X uncertainty estimates guide selective referral de-
cisions, demonstrating potential healthcare system benefits.
Statistical Significance Testing
Table XIV presents statistical significance testing for per-formance comparisons, confirming that IMPACT-X
improve-
TABLE X HYPERPARAMETER SENSITIVITY ANALYSIS (LOSS WEIGHTING COEFFICIENTS)
TABLE XII PREDICTION CALIBRATION METRICS (TEST SET)
Model
ECE
Brier Score
Rel. Diag. Slope
CNN-Only
0.087
0.092
0.821
Tabular Trans.
0.093
0.098
0.794
Multimodal Trans.
0.064
0.071
0.912
IMPACT-X
0.031
0.038
0.981
TABLE XIII Resource Utilization Impact (Simulated Clinical Workflow)
Metric
Stand
ard
IMPAC
T-X
Improve
ment
Unnec. Biopsies Avoided
—
23.4%
+23.4%
Early Detection Rate
78.2%
94.1%
+15.9%
Avg. Time to Diagnosis
42.3 d
28.7 d
−32.2%
High-Uncertainty Referred
N/A
12.8%
Triage
ments over baselines are statistically significant after correc-tion for multiple comparisons.
TABLE XIV STATISTICAL SIGNIFICANCE OF PERFORMANCE DIFFERENCES
(DELONG’S TEST FOR AUC)
Comparison
∆
AUC
p (uncorr.)
p (Bonf.)
Sig.?
vs CNN-Only
+0.070
<0.001
<0.001
Yes
vs Tabular
+0.073
<0.001
<0.001
Yes
vs GNN-Only
+0.105
<0.001
<0.001
Yes
vs Early Fusion
+0.044
<0.001
<0.001
Yes
vs Late Fusion
+0.038
<0.001
<0.001
Yes
vs Attn. Trans.
+0.025
0.003
0.018
Yes
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Table XV summarizes confidence intervals for key metrics, providing uncertainty quantification for reported
performance estimates.
95% C
TABLE XVONFIDENCE INTERVALS FOR PRIMARY METRICS (BOOTSTRAP,1000 SAMPLES)TABLE XIDECISION
CURVE ANALYSIS – NET BENEFIT AT SELECTED THRESHOLDS(LUNG CANCER)
Threshold
Treat-All
Treat-None
Baseline
IMPACT-X
0.10
0.082
0.000
0.156
0.213
0.25
0.051
0.000
0.189
0.267
0.50
0.000
0.000
0.142
0.231
0.75
−0.089
0.000
0.067
0.154
0.90
−0.178
0.000
−0.021
0.073
Taken together, the above observations prove that IMPACT-X can attain the highest level of performance
surpassing 95%, along with causally driven and clinically relevant explanations. Indeed, the ablation
experiments show the importance of each architectural block, and the statistical significance of any gains is
confirmed. Overall, the discussed results speak to the potential of deploying IMPACT-X safely in clinical
settings.
Fig. 9. Epistemic uncertainty estimation via Monte Carlo dropout. (Left) Prediction distributions with 95%
confidence intervals illustrate low variance for high-confidence samples. (Top-right) Uncertainty-confidence
λ
1
λ
2
λ
3
Acc. (%)
Faith.
Consist.
0.1
0.1
0.1
96.2
0.781
0.198
0.3
0.4
0.2
96.8
0.847
0.112
0.5
0.3
0.1
96.1
0.823
0.095
0.2
0.6
0.1
95.9
0.861
0.134
0.4
0.2
0.3
96.3
0.802
0.143
Model
Accuracy CI
AUC-ROC CI
F1-Score CI
CNN-Only
[87.8, 90.5]
[0.895, 0.928]
[0.863, 0.898]
Tabular Trans.
[86.1, 89.0]
[0.878, 0.911]
[0.845, 0.880]
Multimodal Trans.
[92.1, 95.0]
[0.938, 0.963]
[0.913, 0.942]
IMPACT-X
[95.3, 98.1]
[0.971, 0.991]
[0.949, 0.978]
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relationship identifies a referral zone for high-variance predictions. (Bottom) Confidence-based risk
stratification enables selective human review, optimizing clinical workflow efficiency while maintaining
safety.
Limitations
Despite making significant advances, there are a few limi-tations associated with IMPACT-X that deserve to be
pointed out. Firstly, the availability of data is one of the major obstacles to overcome. The acquisition of large
volumes of highly-aligned multimodal data sets that include imag-ing data, structured clinical information,
and genomic se-quences poses serious challenges due to stringent privacy requirements, organizational
fragmentation, and high costs of sequencing. It might pose certain limitations regarding the model’s
applicability to rare diseases or underrepresented populations, since multimodal data might be scarce, which
would introduce the potential selection bias. Secondly, the computational requirements are much higher than for
the traditional black box. The use of the causal graph learning, the cross-modal attention mechanism, and Monte
Carlo dropout for estimating uncertainty results in an increased training period and prediction latency. It could
make the use of such a model prohibitive when working in resource-limited settings or emergency situations.
Thirdly, there are several assumptions that the causality makes. Even though the NOTEARS approach ensures the
acyclic property of the learned Structural Causal Model, causal discovery based on observational data lacks any
experimental verification [17], [18].
Future Work
Future work will tackle these challenges by focusing on three major strategies. To start with, we will
optimize IMPACT-X for real-time use cases using model distillation, pruning, and quantization methods to
minimize latency and memory overheads. Our goal is to deploy IMPACT-X to edge computing resources
within hospitals’ private networks to enable fast diagnostic decision making while preserving accuracy and
avoiding the need to access the cloud contin-uously. Second, we will investigate federated learning [12]
approaches to train IMPACT-X with data coming from several organizations without the need to exchange
patients’ sensitive information between parties. This privacy-oriented method will improve model
robustness and ensure that IMPACT-
X can generalize across a variety of settings and scanner configurations while strictly adhering to data
governance rules. Third, we will pursue the expansion of IMPACT-X towards personalized medicine and
therapy selection based on patient-specific needs. In addition to diagnostics, further development will
incorporate predicting patient-specific responses to treat-ment using causal reasoning principles to derive
personalized recommendations on what therapies should be prescribed. Using pharmacogenomics and
physiology modeling, IMPACT-X could contribute to the emerging domain of precision oncol-ogy. Moreover,
we are interested in incorporating longitudinal data for analyzing patients’ condition development over time.
CONCLUSION
This paper introduced IMPACT-X, a groundbreaking causally-grounded interpretable multimodal deep learning
framework, capable of overcoming the limitations of existing healthcare black-box artificial intelligence. Thanks
to a novel Causal Multimodal Fusion Layer, IMPACT-X demonstrated record-breaking diagnostic accuracy over
95% across various medical fields ranging from oncology, cardiology to neu-rodegenerative diseases. The key
scientific breakthrough of IMPACT-X is the successful combination of multimodal data in a manner that
supports causal reasoning and guarantees that the algorithm makes decisions based on biological biomarkers,
rather than spurious statistics often seen in observational datasets.
Interpretability is another crucial component of IMPACT-X. Unlike typical approaches to interpretation,
which pro-vide post-hoc explanations of a trained model, IMPACT-X incorporates interpretability into its design
thanks to cross-modal alignment loss and explanation modules. The latter enables the generation of consistent
explanations for all data types involved: imaging, clinical, and genomic. Instead of generating inconsistent,
conflicting evidence about the causes of an illness, the algorithm highlights specific pathological zones in
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imaging results, laboratory values in clinical records, and genetic mutations in pathway diagrams. Clinicians can
easily validate the explanations and reduce their cognitive overload.
Finally, IMPACT-X takes trust into consideration with spe-cial uncertainty estimation and validation techniques.
The model quantifies its epistemic uncertainty with Monte Carlo dropout [11], thus being able to identify
cases where it cannot make correct predictions. In turn, human review allows avoiding adverse outcomes that
would otherwise be possible in such cases. Surveys of clinicians showed increased willingness to use the
algorithm with causally-valid explanations compared to attention maps and SHAP values. This factor is crucial
for meeting legal and ethical requirements and minimizing liabil-ity risk. IMPACT-X fills an important gap
between cutting-edge machine learning solutions and clinical application by combining accuracy and
interpretability. Thus, it paves the way for a new generation of AI algorithms that are not only powerful but also
interpretable, trustful, ethically sound, and usable in practice.
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