INTERVENTIONAL CARDIOLOGY: AN OVERVIEW OF CURRENT APPLICATIONS, CHALLENGES, AND FUTURE PATHWAYS – THE ERA OF AI-ASSISTED PCI?
Abstract
Background: Interventional cardiology, which began in 1977, has evolved from primitive balloon angioplasty to sophisticated drug-eluting stents, fundamentally altering the treatment of coronary artery disease. We have reached a plateau in stent performance, where successive generations do not bring spectacular leaps forward. Consequently, the focus is shifting away from finding a universal gold standard toward a tailored, patient-specific approach that integrates a variety of revascularization strategies.
Methods: A comprehensive search was conducted in the PubMed, Scopus, and IEEE Xplore databases to identify relevant publications from January 2005 to October 2025. The search strategy employed combinations of the following keywords: “Percutaneous Coronary Intervention”, “Fractional Flow Reserve”, “Intravascular Ultrasound”, “Artificial Intelligence”, “Optical Coherence Tomography”, “Coronary Artery Disease” and “Regulation”
Results: Interventional cardiology is moving away from purely mechanical innovations toward a future defined by information integration, advanced visualization, and data-driven procedures. However, widespread implementation faces significant obstacles: difficulties in scaling technology, funding constraints, and complex legal requirements for data collection for artificial intelligence algorithms.
Conclusion: It appears that despite regulatory and economic challenges, the field is inevitably moving from an era of simple tools to true partners, slowly considering at least partial autonomy in decision-making.
References
Castaldi, G., Zormpas, G., Frederiks, P., Adriaenssens, T., & Bennett, J. (2025). The Rise of Optical Coherent Tomography in Intracoronary Imaging: An Overview of Current Technology, Limitations, and Future Perspectives. Reviews in cardiovascular medicine, 26(8), 38123. https://doi.org/10.31083/RCM38123
Azzi, N., & Shatila, W. (2021). Update on coronary artery bioresorbable vascular scaffolds in percutaneous coronary revascularization. Reviews in cardiovascular medicine, 22(1), 137–145. https://doi.org/10.31083/j.rcm.2021.01.225
Arh, R., Balevski, I., Granda, S., & Bevc, S. (2025). Drug-Eluting Stent Use in Percutaneous Coronary Interventions-A Narrative Review. Journal of clinical medicine, 14(13), 4643. https://doi.org/10.3390/jcm14134643
Bacigalupi, E., Pizzicannella, J., Rigatelli, G., Scorpiglione, L., Foglietta, M., Rende, G., Mantini, C., Fiore, F. M., Pelliccia, F., & Zimarino, M. (2024). Biomechanical factors and atherosclerosis localization: insights and clinical applications. Frontiers in cardiovascular medicine, 11, 1392702. https://doi.org/10.3389/fcvm.2024.1392702
Ali, Z. A., Shin, D., Chaturvedi, A., & Waksman, R. (2024). We now have enough evidence to support systematic OCT in daily PCI practice: pros and cons. EuroIntervention : journal of EuroPCR in collaboration with the Working Group on Interventional Cardiology of the European Society of Cardiology, 20(9), 533–535. https://doi.org/10.4244/EIJ-E-24-00008
Collet, C., Miyazaki, Y., Ryan, N., Asano, T., Tenekecioglu, E., Sonck, J., Andreini, D., Sabate, M., Brugaletta, S., Stables, R. H., Bartorelli, A., de Winter, R. J., Katagiri, Y., Chichareon, P., De Maria, G. L., Suwannasom, P., Cavalcante, R., Jonker, H., Morel, M. A., Cosyns, B., … Serruys, P. W. (2018). Fractional Flow Reserve Derived From Computed Tomographic Angiography in Patients With Multivessel CAD. Journal of the American College of Cardiology, 71(24), 2756–2769. https://doi.org/10.1016/j.jacc.2018.02.053
Vrints, C., Andreotti, F., Koskinas, K. C., Rossello, X., Adamo, M., Ainslie, J., Banning, A. P., Budaj, A., Buechel, R. R., Chiariello, G. A., Chieffo, A., Christodorescu, R. M., Deaton, C., Doenst, T., Jones, H. W., Kunadian, V., Mehilli, J., Milojevic, M., Piek, J. J., Pugliese, F., … ESC Scientific Document Group (2024). 2024 ESC Guidelines for the management of chronic coronary syndromes. European heart journal, 45(36), 3415–3537. https://doi.org/10.1093/eurheartj/ehae177
Chen, B. X., Ma, F. Y., Luo, W., Xie, W. L., Zhao, X. Z., Sun, S. H., Wang, F., Guo, X. M., & Chu, X. W. (2006). Zhonghua xin xue guan bing za zhi, 34(2), 130–133.
Ugo, F., Franzino, M., Massaro, G., Maltese, L., Cavallino, C., Abdirashid, M., Benedetto, D., Costa, F., Rametta, F., & Sangiorgi, G. M. (2025). The Role of IVUS in Coronary Complications. Catheterization and cardiovascular interventions : official journal of the Society for Cardiac Angiography & Interventions, 105(5), 1171–1182. https://doi.org/10.1002/ccd.31433
Mitsis, A., Eftychiou, C., Kadoglou, N. P. E., Theodoropoulos, K. C., Karagiannidis, E., Nasoufidou, A., Ziakas, A., Tzikas, S., & Kassimis, G. (2024). Innovations in Intracoronary Imaging: Present Clinical Practices and Future Outlooks. Journal of clinical medicine, 13(14), 4086. https://doi.org/10.3390/jcm13144086
Xie, Y., Han, W., Wang, S., Jia, W., Wang, Y., Li, J., & Chen, B. (2025). Advantages of hybrid intravascular ultrasound-optical coherence tomography system in clinical practice. Frontiers in cardiovascular medicine, 12, 1595889. https://doi.org/10.3389/fcvm.2025.1595889
Hoang, V., Grounds, J., Pham, D., Virani, S., Hamzeh, I., Qureshi, A. M., Lakkis, N., & Alam, M. (2016). The Role of Intracoronary Plaque Imaging with Intravascular Ultrasound, Optical Coherence Tomography, and Near-Infrared Spectroscopy in Patients with Coronary Artery Disease. Current atherosclerosis reports, 18(9), 57. https://doi.org/10.1007/s11883-016-0607-0
Terashima, M., Kaneda, H., & Suzuki, T. (2012). The role of optical coherence tomography in coronary intervention. The Korean journal of internal medicine, 27(1), 1–12. https://doi.org/10.3904/kjim.2012.27.1.1
Erlinge, D., Maehara, A., Ben-Yehuda, O., Bøtker, H. E., Maeng, M., Kjøller-Hansen, L., Engstrøm, T., Matsumura, M., Crowley, A., Dressler, O., Mintz, G. S., Fröbert, O., Persson, J., Wiseth, R., Larsen, A. I., Okkels Jensen, L., Nordrehaug, J. E., Bleie, Ø., Omerovic, E., Held, C., … PROSPECT II Investigators (2021). Identification of vulnerable plaques and patients by intracoronary near-infrared spectroscopy and ultrasound (PROSPECT II): a prospective natural history study. Lancet (London, England), 397(10278), 985–995. https://doi.org/10.1016/S0140-6736(21)00249-X
Chandramohan, N., Hinton, J., O'Kane, P., & Johnson, T. W. (2024). Artificial Intelligence for the Interventional Cardiologist: Powering and Enabling OCT Image Interpretation. Interventional cardiology (London, England), 19, e03. https://doi.org/10.15420/icr.2023.13
Durand, E., & Eltchaninoff, H. (2024). Robotic-assisted percutaneous coronary intervention: the future or the past?. EuroIntervention : journal of EuroPCR in collaboration with the Working Group on Interventional Cardiology of the European Society of Cardiology, 20(1), 19–20. https://doi.org/10.4244/EIJ-E-23-00064
Khelimskii, D., Badoyan, A., Krymcov, O., Baranov, A., Manukian, S., & Lazarev, M. (2024). AI in interventional cardiology: Innovations and challenges. Heliyon, 10(17), e36691. https://doi.org/10.1016/j.heliyon.2024.e36691
Zhang, D. P. (2010). Coronary artery segmentation and motion modelling. https://doi.org/10.25560/6367
Chen, C., Qin, C., Qiu, H., Tarroni, G., Duan, J., Bai, W., & Rueckert, D. (2020). Deep Learning for Cardiac Image Segmentation: A Review. Frontiers in cardiovascular medicine, 7, 25. https://doi.org/10.3389/fcvm.2020.00025
Guo, B., Jiang, M., Guo, X., Tang, C., Zhong, J., Lu, M., Liu, C., Zhang, X., Qiao, H., Zhou, F., Xu, P., Xue, Y., Zheng, M., Hou, Y., Wang, Y., Zhang, J., Zhang, B., Zhang, D., Xu, L., Hu, X., … Zhang, L. (2024). Diagnostic and prognostic performance of artificial intelligence-based fully-automated on-site CT-FFR in patients with CAD. Science bulletin, 69(10), 1472–1485. https://doi.org/10.1016/j.scib.2024.03.053
Guan, X., Song, D., Li, C., Hu, Y., Leng, X., Sheng, X., Bao, L., Pan, Y., Dong, L., Jiang, J., Xiang, J., & Jiang, W. (2023). Functional Assessment of Coronary Artery Stenosis from Coronary Angiography and Computed Tomography: Angio-FFR vs. CT-FFR. Journal of cardiovascular translational research, 16(4), 905–915. https://doi.org/10.1007/s12265-023-10361-1
Collet, C., Onuma, Y., Sonck, J., Asano, T., Vandeloo, B., Kornowski, R., Tu, S., Westra, J., Holm, N. R., Xu, B., de Winter, R. J., Tijssen, J. G., Miyazaki, Y., Katagiri, Y., Tenekecioglu, E., Modolo, R., Chichareon, P., Cosyns, B., Schoors, D., Roosens, B., … Serruys, P. W. (2018). Diagnostic performance of angiography-derived fractional flow reserve: a systematic review and Bayesian meta-analysis. European heart journal, 39(35), 3314–3321. https://doi.org/10.1093/eurheartj/ehy445
Duarte, A., Llewellyn, A., Walker, R., Schmitt, L., Wright, K., Walker, S., Rothery, C., & Simmonds, M. (2021). Non-invasive imaging software to assess the functional significance of coronary stenoses: a systematic review and economic evaluation. Health technology assessment (Winchester, England), 25(56), 1–230. https://doi.org/10.3310/hta25560
Serruys, P. W., Kotoku, N., Nørgaard, B. L., Garg, S., Nieman, K., Dweck, M. R., Bax, J. J., Knuuti, J., Narula, J., Perera, D., Taylor, C. A., Leipsic, J. A., Nicol, E. D., Piazza, N., Schultz, C. J., Kitagawa, K., Bruyne, B., Collet, C., Tanaka, K., Mushtaq, S., … Onuma, Y. (2023). Computed tomographic angiography in coronary artery disease. EuroIntervention : journal of EuroPCR in collaboration with the Working Group on Interventional Cardiology of the European Society of Cardiology, 18(16), e1307–e1327. https://doi.org/10.4244/EIJ-D-22-00776
Celeng, C., Leiner, T., Maurovich-Horvat, P., Merkely, B., de Jong, P., Dankbaar, J. W., van Es, H. W., Ghoshhajra, B. B., Hoffmann, U., & Takx, R. A. P. (2019). Anatomical and Functional Computed Tomography for Diagnosing Hemodynamically Significant Coronary Artery Disease: A Meta-Analysis. JACC. Cardiovascular imaging, 12(7 Pt 2), 1316–1325. https://doi.org/10.1016/j.jcmg.2018.07.022
SCOT-HEART Investigators, Newby, D. E., Adamson, P. D., Berry, C., Boon, N. A., Dweck, M. R., Flather, M., Forbes, J., Hunter, A., Lewis, S., MacLean, S., Mills, N. L., Norrie, J., Roditi, G., Shah, A. S. V., Timmis, A. D., van Beek, E. J. R., & Williams, M. C. (2018). Coronary CT Angiography and 5-Year Risk of Myocardial Infarction. The New England journal of medicine, 379(10), 924–933. https://doi.org/10.1056/NEJMoa1805971
Cioffi, G. M., Pinilla-Echeverri, N., Sheth, T., & Sibbald, M. G. (2023). Does artificial intelligence enhance physician interpretation of optical coherence tomography: insights from eye tracking. Frontiers in cardiovascular medicine, 10, 1283338. https://doi.org/10.3389/fcvm.2023.1283338
Pinna, A., Boi, A., Mannelli, L., Balestrieri, A., Sanfilippo, R., Suri, J., & Saba, L. (2025). Machine Learning for Coronary Plaque Characterization: A Multimodal Review of OCT, IVUS, and CCTA. Diagnostics (Basel, Switzerland), 15(14), 1822. https://doi.org/10.3390/diagnostics15141822
Durand, E., Sabatier, R., Smits, P. C., Verheye, S., Pereira, B., & Fajadet, J. (2023). Evaluation of the R-One robotic system for percutaneous coronary intervention: the R-EVOLUTION study. EuroIntervention : journal of EuroPCR in collaboration with the Working Group on Interventional Cardiology of the European Society of Cardiology, 18(16), e1339–e1347. https://doi.org/10.4244/EIJ-D-22-00642
Gupta, R., Malik, A. H., Chan, J. S. K., Lawrence, H., Mehta, A., Venkata, V. S., Aedma, S. K., Ranchal, P., Dhaduk, K., Aronow, W. S., Vyas, A. V., Mehta, S. S., Combs, W. G., Frishman, W. H., & Patel, N. C. (2024). Robotic Assisted Versus Manual Percutaneous Coronary Intervention: Systematic Review and Meta-Analysis. Cardiology in review, 32(1), 24–29. https://doi.org/10.1097/CRD.0000000000000445
Ihdayhid, A. R., Tzimas, G., Peterson, K., Ng, N., Mirza, S., Maehara, A., & Safian, R. D. (2024). Diagnostic Performance of AI-enabled Plaque Quantification from Coronary CT Angiography Compared with Intravascular Ultrasound. Radiology. Cardiothoracic imaging, 6(6), e230312. https://doi.org/10.1148/ryct.230312
Liang, B., Song, C., Xia, S., Guo, W., Zhu, L., Wang, K., & Lu, Q. (2025). Unleashing the Potential: First-in-Human Evaluation of Automatic Robotic-Assisted Endovascular Aortic Repair for Standardized Therapies. MedComm, 6(12), e70489. https://doi.org/10.1002/mco2.70489
Thangaraj, P. M., Benson, S. H., Oikonomou, E. K., Asselbergs, F. W., & Khera, R. (2024). Cardiovascular care with digital twin technology in the era of generative artificial intelligence. European heart journal, 45(45), 4808–4821. https://doi.org/10.1093/eurheartj/ehae619
Gijsen, F. J., Migliavacca, F., Schievano, S., Socci, L., Petrini, L., Thury, A., Wentzel, J. J., van der Steen, A. F., Serruys, P. W., & Dubini, G. (2008). Simulation of stent deployment in a realistic human coronary artery. Biomedical engineering online, 7, 23. https://doi.org/10.1186/1475-925X-7-23
Cui, K., Liang, S., Hua, M., Gao, Y., Feng, Z., Wang, W., & Zhang, H. (2023). Diagnostic Performance of Machine Learning-Derived Radiomics Signature of Pericoronary Adipose Tissue in Coronary Computed Tomography Angiography for Coronary Artery In-Stent Restenosis. Academic radiology, 30(12), 2834–2843. https://doi.org/10.1016/j.acra.2023.04.006
Rudnicka, Z., Pręgowska, A., Glądys, K., Perkins, M., & Proniewska, K. (2024). Advancements in artificial intelligence-driven techniques for interventional cardiology. Cardiology Journal, 31(2), 321–341. https://doi.org/10.5603/cj.98650
Karim, M. R., Islam, T., Shajalal, M., Beyan, O., Lange, C., Cochez, M., Rebholz-Schuhmann, D., & Decker, S. (2023). Explainable AI for Bioinformatics: Methods, Tools and Applications. Briefings in bioinformatics, 24(5), bbad236. https://doi.org/10.1093/bib/bbad236
Ratwani, R. M., Sutton, K., & Galarraga, J. E. (2024). Addressing AI Algorithmic Bias in Health Care. JAMA, 332(13), 1051–1052. https://doi.org/10.1001/jama.2024.13486
Patel, D., Chetarajupalli, C., Khan, S., Khan, S., Patel, T., Joshua, S., & Millis, R. M. (2025). A narrative review on ethical considerations and challenges in AI-driven cardiology. Annals of medicine and surgery (2012), 87(7), 4152–4164. https://doi.org/10.1097/MS9.0000000000003349
Ueda, D., Kakinuma, T., Fujita, S., Kamagata, K., Fushimi, Y., Ito, R., Matsui, Y., Nozaki, T., Nakaura, T., Fujima, N., Tatsugami, F., Yanagawa, M., Hirata, K., Yamada, A., Tsuboyama, T., Kawamura, M., Fujioka, T., & Naganawa, S. (2024). Fairness of artificial intelligence in healthcare: review and recommendations. Japanese journal of radiology, 42(1), 3–15. https://doi.org/10.1007/s11604-023-01474-3
Ong, J. C. L., Chang, S. Y., William, W., Butte, A. J., Shah, N. H., Chew, L. S. T., Liu, N., Doshi-Velez, F., Lu, W., Savulescu, J., & Ting, D. S. W. (2024). Ethical and regulatory challenges of large language models in medicine. The Lancet. Digital health, 6(6), e428–e432. https://doi.org/10.1016/S2589-7500(24)00061-X
Zhao, Y., Guan, C., Wang, Y., Jin, Z., Yu, B., Fu, G., Chen, Y., Guo, L., Qu, X., Zhang, Y., Dou, K., Wu, Y., Yang, W., Tu, S., Escaned, J., Fearon, W. F., Qiao, S., Cohen, D. J., Krumholz, H. M., Xu, B., … FAVOR III China Study Group (2025). Cost-effectiveness of angiographic quantitative flow ratio-guided coronary intervention: A multicenter, randomized, sham-controlled trial. Chinese medical journal, 138(10), 1186–1193. https://doi.org/10.1097/CM9.0000000000003484
Oloruntoba, A., Ingvar, Å., Sashindranath, M., Anthony, O., Abbott, L., Guitera, P., Caccetta, T., Janda, M., Soyer, H. P., & Mar, V. (2024). Examining labelling guidelines for AI-based software as a medical device: A review and analysis of dermatology mobile applications in Australia. The Australasian journal of dermatology, 65(5), 409–422. https://doi.org/10.1111/ajd.14269
Yu, J., Zhang, J., & Sengoku, S. (2023). Innovation Process and Industrial System of US Food and Drug Administration-Approved Software as a Medical Device: Review and Content Analysis. Journal of medical Internet research, 25, e47505. https://doi.org/10.2196/47505
Parikh, P. M., & Venniyoor, A. (2024). Neuralink and Brain-Computer Interface-Exciting Times for Artificial Intelligence. South Asian journal of cancer, 13(1), 63–65. https://doi.org/10.1055/s-0043-1774729
Hedderich, D. M., Weisstanner, C., Van Cauter, S., Federau, C., Edjlali, M., Radbruch, A., Gerke, S., & Haller, S. (2023). Artificial intelligence tools in clinical neuroradiology: essential medico-legal aspects. Neuroradiology, 65(7), 1091–1099. https://doi.org/10.1007/s00234-023-03152-7
Prabhakar, B., Singh, R. K., & Yadav, K. S. (2021). Artificial intelligence (AI) impacting diagnosis of glaucoma and understanding the regulatory aspects of AI-based software as medical device. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society, 87, 101818. https://doi.org/10.1016/j.compmedimag.2020.101818
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Copyright (c) 2025 Kamil Jabłoński, Aleksandra Grygorowicz, Klaudia Baran, Michał Ględa, Michał Szyszka, Weronika Radecka, Weronika Kozak, Agnieszka Szreiber, Karol Grela, Karolina Nowacka, Anna Woźniak

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