Toward Artificial Intelligence in Oncology and Cardiology: A Narrative Review of Systems, Challenges, and Opportunities

Vela, Visar; Sonay, Ali Yasin; Limani, Perparim; Graf, Lukas; Sabani, Besmira; Gjermeni, Diona; Rroku, Andi; Zela, Arber; Gorica, Era; Rodriguez Cetina Biefer, Hector; Berdica, Uljad; Hasanaj, Euxhen; Trnjanin, Adisa; Muka, Taulant; Dzemali, Omer

doi:10.3390/jcm14217555

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Toward Artificial Intelligence in Oncology and Cardiology: A Narrative Review of Systems, Challenges, and Opportunities

by

Visar Vela

^1,*,†

,

Ali Yasin Sonay

^2,†

,

Perparim Limani

^3,*

,

Lukas Graf

⁴,

Besmira Sabani

⁵,

Diona Gjermeni

^6,7,

Andi Rroku

^8,9

,

Arber Zela

¹⁰,

Era Gorica

^3,11,

Hector Rodriguez Cetina Biefer

^3,11,12

,

Uljad Berdica

¹³

,

Euxhen Hasanaj

¹⁴

,

Adisa Trnjanin

⁵,

Taulant Muka

^2,*,†

and

Omer Dzemali

^{3,11,12,*,†}

¹

Centre Suisse de Contrôle de Qualité, 1225 Geneva, Switzerland

²

Epistudia, 3008 Bern, Switzerland

³

Department of Cardiac Surgery, University Hospital Zurich, 8091 Zurich, Switzerland

⁴

Center for Laboratory Medicine, Hemostasis and Hemophilia Center, 9008 St. Gallen, Switzerland

⁵

Institute of Chemistry and Biotechnology, Zurich University of Applied Sciences (ZHAW), 8820 Wädenswil, Switzerland

⁶

Department of Cardiology and Angiology, University Heart Center Freiburg-Bad Krozingen, Faculty of Medicine, University of Freiburg, 79085 Freiburg, Germany

⁷

Stanford Center for Clinical Research, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA

⁸

Department of Cardiology, Angiology and Intensive Care Medicine, Deutsches Herzzentrum der Charité, Charité-Universitätsmedizin Berlin (Campus Benjamin Franklin), 12203 Berlin, Germany

⁹

DZHK German Center for Cardiovascular Research, Partner Site Berlin, 13353 Berlin, Germany

¹⁰

Department of Computer Science, University of Freiburg, 79085 Freiburg, Germany

¹¹

Center for Translational and Experimental Cardiology (CTEC), Department of Cardiology, University Hospital Zurich, University of Zürich, Wagistrasse 12, 8952 Schlieren, Switzerland

¹²

Department of Cardiac Surgery, City Hospital Zurich-Triemli, 8063 Zurich, Switzerland

¹³

Oxford Robotics Institute, Department of Engineering Science, University of Oxford, Oxford 1096, UK

¹⁴

GenBio AI, Palo Alto, CA 94301, USA

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^*

Authors to whom correspondence should be addressed.

^†

These authors contributed equally to this work.

J. Clin. Med. 2025, 14(21), 7555; https://doi.org/10.3390/jcm14217555 (registering DOI)

Submission received: 11 September 2025 / Revised: 17 October 2025 / Accepted: 21 October 2025 / Published: 24 October 2025

(This article belongs to the Section Clinical Research Methods)

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Abstract

Background: Artificial intelligence (AI), the overarching field that includes machine learning (ML) and its subfield deep learning (DL), is rapidly transforming clinical research by enabling the analysis of high-dimensional data and automating the output of diagnostic and prognostic tests. As clinical trials become increasingly complex and costly, ML-based approaches (especially DL for image and signal data) offer promising solutions, although they require new approaches in clinical education. Objective: Explore current and emerging AI applications in oncology and cardiology, highlight real-world use cases, and discuss the challenges and future directions for responsible AI adoption. Methods: This narrative review summarizes various aspects of AI technology in clinical research, exploring its promise, use cases, and its limitations. The review was based on a literature search in PubMed covering publications from 2019 to 2025. Search terms included “artificial intelligence”, “machine learning”, “deep learning”, “oncology”, “cardiology”, “digital twin”. and “AI-ECG”. Preference was given to studies presenting validated or clinically applicable AI tools, while non-English articles, conference abstracts, and gray literature were excluded. Results: AI demonstrates significant potential in improving diagnostic accuracy, facilitating biomarker discovery, and detecting disease at an early stage. In clinical trials, AI improves patient stratification, site selection, and virtual simulations via digital twins. However, there are still challenges in harmonizing data, validating models, cross-disciplinary training, ensuring fairness, explainability, as well as the robustness of gold standards to which AI models are built. Conclusions: The integration of AI in clinical research can enhance efficiency, reduce costs, and facilitate clinical research as well as lead the way towards personalized medicine. Realizing this potential requires robust validation frameworks, transparent model interpretability, and collaborative efforts among clinicians, data scientists, and regulators. Interoperable data systems and cross-disciplinary education will be critical to enabling the integration of scalable, ethical, and trustworthy AI into healthcare.

Keywords: artificial intelligence; machine learning; deep learning; oncology; cardiology; clinical diagnostics

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MDPI and ACS Style

Vela, V.; Sonay, A.Y.; Limani, P.; Graf, L.; Sabani, B.; Gjermeni, D.; Rroku, A.; Zela, A.; Gorica, E.; Rodriguez Cetina Biefer, H.; et al. Toward Artificial Intelligence in Oncology and Cardiology: A Narrative Review of Systems, Challenges, and Opportunities. J. Clin. Med. 2025, 14, 7555. https://doi.org/10.3390/jcm14217555

AMA Style

Vela V, Sonay AY, Limani P, Graf L, Sabani B, Gjermeni D, Rroku A, Zela A, Gorica E, Rodriguez Cetina Biefer H, et al. Toward Artificial Intelligence in Oncology and Cardiology: A Narrative Review of Systems, Challenges, and Opportunities. Journal of Clinical Medicine. 2025; 14(21):7555. https://doi.org/10.3390/jcm14217555

Chicago/Turabian Style

Vela, Visar, Ali Yasin Sonay, Perparim Limani, Lukas Graf, Besmira Sabani, Diona Gjermeni, Andi Rroku, Arber Zela, Era Gorica, Hector Rodriguez Cetina Biefer, and et al. 2025. "Toward Artificial Intelligence in Oncology and Cardiology: A Narrative Review of Systems, Challenges, and Opportunities" Journal of Clinical Medicine 14, no. 21: 7555. https://doi.org/10.3390/jcm14217555

APA Style

Vela, V., Sonay, A. Y., Limani, P., Graf, L., Sabani, B., Gjermeni, D., Rroku, A., Zela, A., Gorica, E., Rodriguez Cetina Biefer, H., Berdica, U., Hasanaj, E., Trnjanin, A., Muka, T., & Dzemali, O. (2025). Toward Artificial Intelligence in Oncology and Cardiology: A Narrative Review of Systems, Challenges, and Opportunities. Journal of Clinical Medicine, 14(21), 7555. https://doi.org/10.3390/jcm14217555

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Toward Artificial Intelligence in Oncology and Cardiology: A Narrative Review of Systems, Challenges, and Opportunities

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