The Journal of Cardiac Failure is pleased to announce a Call For Papers for a special Focus Issue:
Artificial Intelligence and Heart Failure: A Critical Juncture. Guest Editors Drs. Ankeet S. Bhatt, Jonathan Cunningham, and Nir Uriel will oversee this Focus Issue, to be published in the Fall of 2026.

We invite submission for all JCF paper types, including:

• Original Research Papers
• State of the Art Reviews
• Perspectives
• Brief Reports
• Research Letters
• Early Career/Trainee Spotlight
• Patient and Caregiver Perspectives

*Please note that JCF does not accept Case Reports.
 

The editors are looking for papers in the following (but not limited to) topic areas:

• AI for diagnosis and phenotyping of heart failure and cardiomyopathy
  Studies and reviews on AI-enabled diagnosis for HF and cardiomyopathies (e.g., amyloidosis, hypertrophic cardiomyopathy, inflammatory cardiomyopathy, dilated cardiomyopathy), using ECG, imaging, biomarkers, and EHR-derived signals, etc.
• AI to advance guideline-based heart failure care delivery at scale
  Approaches that improve implementation of guideline-based HF care, including GDMT initiation/titration, optimal utilization of device therapy (e.g., ICD/CRT), and population management strategies across inpatient and outpatient settings.
• Remote monitoring, wearables, and home-based heart failure management supported by AI
  Work leveraging wearables, implantables, and home monitoring (e.g. activity, rhythm, BP, weight, hemodynamics, voice.) with AI to detect decompensation, guide management, reduce admissions, and improve patient-centered outcomes.
• AI in advanced heart failure: ICU, mechanical circulatory support, VADs, and transplantation
  Applications of AI to hemodynamic assessment and shock phenotyping, decision escalate to advanced therapies, VAD management, transplant candidacy and allocation, and post-transplant outcomes/complications.
• AI tools to enhance the patient’s experience
  AI tools to increase access to specialist HF providers to patients, decrease friction for access to care and services, improve ability to manage/stay adherent to medications, improve backend workflows.
• Clinician / HF program guidance for evaluating and adopting AI tools
  Pragmatic frameworks and case studies addressing what HF clinicians and health systems should require when partnering with AI vendors—evidence standards and generalizability, safety and monitoring, bias/equity, data security/privacy, regulatory posture, and fit with real-world clinical workflows.
• Education and workforce development for AI-enabled HF care
  Submissions on what HF clinicians and trainees need to know to use AI effectively and responsibly in practice, including curricula/competency frameworks, teaching tools (e.g., cases/simulations), and approaches to assessment and continuing professional development.

In line with our core values at JCF, we encourage multi-disciplinary collaborations (e.g., pharmacists, nurses, physicians) and emphasize inclusion of the patient voice/perspective.
 
We also consider inclusion of diverse and representative co-authors a priority. Along these lines, JCF aims to feature, promote and highlight women, underrepresented minorities, and early career individuals.