A
recent state-of-the-art review highlighted that despite significant advances,
Pulmonary Hypertension (PH) patients continue to face substantial morbidity and
mortality. The problem is compounded by the massive disease burden, the
heterogeneity of PH, and a lack of resources, particularly in remote regions.
This review advocated for personalized
precise treatment for every PH patient with the aid of modern technologies,
arguing that Artificial Intelligence (AI) shows potential to provide a simple
but multifaceted approach for early identification and improved risk
stratification of PH.

This
state-of-the-art review is published in the American Heart Journal Plus: Cardiology Research and Practice
in December 2025.

The Challenge of Delayed PH Diagnosis

All-cause pulmonary hypertension (PH)
is a major public health challenge linked to increased mortality. As a complex
hemodynamic condition involving the heart, lungs, and pulmonary vasculature,
PH—most commonly due to left heart disease (WHO Group 2)—often goes
unrecognized because of its varied causes and diagnostic difficulty. Delayed or
missed diagnosis allows progression to right heart failure and early death.
Early identification and timely disease-modifying treatment are therefore
essential to reduce hospitalizations and improve quality of life. The burden of disease is particularly
high in individuals living in remote and disadvantaged regions, who also have
the least access to the current specialized, tertiary-centric PH model of care.

Reviewing AI’s Role in Clinical
Practice

This state-of-the-art review explored
how emerging technologies, including artificial intelligence, can enhance the
diagnosis and management of pulmonary hypertension (PH). Drawing on literature
from 2015–2025 across major databases, the authors focused on clinically
relevant and methodologically transparent studies applying AI to PH. Rather
than a systematic review, the goal was to illustrate how AI can support
multiple steps in the PH care pathway, offering more efficient and effective
triage than current manual approaches.

Demonstrable Accuracy in AI-Enhanced
Imaging

•
Smarter Triage: AI systems can
extract clinical information from Electronic Medical Records (EMR), such as
diagnostic codes and major health issues, to create a “breathlessness triage” system that identifies
individuals requiring further PH investigation.

•
Echocardiography Enhancement:
Echocardiography is the central investigation for many forms of PH. AI is
poised to improve its efficiency,
accuracy, and reproducibility. AI can guide non-expert operators, such
as community health workers, to obtain diagnostic-quality images, which is
critical for democratizing access in rural and inaccessible locations.

•
Phenotype Prediction:
Proof-of-concept studies using deep neural networks have demonstrated AI’s
ability to predict all-cause PH (defined as TRV >2.8 m/s) with
reasonable accuracy, even when key PH-related measurements were withheld.
Moreover, simple algorithms, such as the echocardiographic pulmonary to left atrial ratio (ePLAR),
demonstrate good capacity to differentiate pre-capillary (non-left heart) PH
from post-capillary (left heart) PH.

Advancing Prognosis and Risk
Stratification

Beyond
diagnosis, AI models offer significant promise in risk assessment and
prognostication:

•
Mortality Prediction: AI models
trained on paired electrocardiogram (ECG) and echocardiogram data have
demonstrated strong prognostic value in predicting mortality in PH patients.

•
Personalized Risk: AI can
integrate various sources of clinical data (demographics, comorbidities, lab
results) to stratify patients into different risk categories. This enables
healthcare providers to tailor
treatment plans and interventions accordingly.

•
Objective Outputs: AI algorithms
can analyze multiple parameters simultaneously, and their outputs are free of
the subjectivity typical of human interpretation and are not subject to
fatigue.

Optimal Care Delivery and Future
Strategy

Optimal
care for complex PH patients requires a dedicated
multidisciplinary team, including specialist physicians, nurses, allied
health members, and community care providers. While AI enhances technological
capabilities, the sources emphasize that compassion and human interaction are essential attributes which only
human clinicians can provide.

•
Individualized Treatment:
Treatment must be individualized
based on the background and expectations of each patient, with shared decision
making facilitated by well-informed patients and families. PH clinicians must
use their clinical acumen to avoid unnecessary, costly, and invasive tests.

•
Administrative Efficiency:
AI-based speech recognition can provide automated generation of medical notes
and correspondence, which reduces the administrative burden on clinicians. This
frees up more clinician time to spend with patients.

•
Monitoring and Follow-up:
AI-based wearable technologies
(such as smartwatches) have potential utility for the efficient monitoring and
follow-up of PH patients.

The
collective integration of these AI systems holds the potential to improve
diagnostic accuracy, efficiency, and speed, leading to more timely and targeted
interventions that improve patient outcomes.

Clinical
Inference for Cardiologists & Cardiology Teams

AI offers a
powerful opportunity to enhance and personalize Pulmonary Hypertension (PH)
care by improving early detection and risk stratification. Because PH diagnosis
is often delayed and expertise is unevenly distributed, AI can strengthen the
care pathway by increasing the efficiency, accuracy, and reproducibility of
echocardiography—the key initial test. AI tools can guide non-expert operators
to obtain high-quality images and help distinguish PH due to left heart disease
from other forms using metrics such as ePLAR. In addition, AI-driven models
show strong potential for prognostication, enabling more precise risk
stratification and individualized treatment planning. By automating
labor-intensive tasks like measurements and report generation, AI frees clinicians
to focus on compassionate, patient-centered decision making within
multidisciplinary PH teams.

Reference: Naing
P, Scalia GM, Murdoch D, Ranasinghe I, Forrester DL, Strange G, Playford D.
Artificial intelligence enhanced contemporary pulmonary hypertension care.
American Heart Journal Plus: Cardiology Research and Practice. 2025 Nov
12:100673.

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