Curated Papers > Recent high-impact research on "AI Drug Discovery" (IF≥30, past 5 years)

Artificial intelligence in cardiovascular pharmacotherapy: applications and perspectives.

Literature Information

DOI	10.1093/eurheartj/ehaf474
PMID	40662528
Journal	European heart journal
Impact Factor	35.6
JCR Quartile	Q1
Publication Year	2025
Times Cited	1
Keywords	Artificial intelligence, Cardiovascular pharmacotherapy, Coronary artery disease, Diabetes, Hypertension
Literature Type	Journal Article, Review
ISSN	0195-668X
Pages	3616-3627
Issue	46(37)
Authors	Francesco Costa, Juan Jose Gomez Doblas, Arancha Díaz Expósito, Marianna Adamo, Fabrizio D'Ascenzo, Lukasz Kołtowski, Luca Saba, Guiomar Mendieta, Felice Gragnano, Paolo Calabrò, Lina Badimon, Borja Ibañez, Roxana Mehran, Dominick J Angiolillo, Thomas Lüscher, Davide Capodanno

TL;DR

This paper reviews the potential of artificial intelligence (AI) in enhancing cardiovascular pharmacotherapy by optimizing drug selection and personalizing treatment, thereby improving patient outcomes and streamlining clinical trials. It emphasizes the need for robust validation, addressing data quality and bias concerns, and establishing standardized frameworks to ensure the safe integration of AI into clinical practice.

Search for more papers on MaltSci.com

Artificial intelligence · Cardiovascular pharmacotherapy · Coronary artery disease · Diabetes · Hypertension

Abstract

Recent advances in artificial intelligence (AI) have shown great potential in improving cardiovascular pharmacotherapy by optimizing drug selection, predicting therapeutic efficacy and adverse effects, ultimately improving patient outcomes. Leveraging techniques like machine learning and in silico modelling, AI can identify populations likely to benefit from specific treatments, expedite novel drug discovery and reduce costs. Computational methods can also facilitate the detection of drug interactions and tailor interventions based on real-world data, supporting personalized care. Artificial intelligence-based approaches also show promise in streamlining clinical trial design and execution, leveraging on real-time data on patient responsiveness, enhancing recruitment efficiency. However, in order to fully realize these benefits, robust validation across diverse patient populations is necessary to ensure accuracy and generalizability. In addition, addressing concerns regarding data quality, privacy, and bias is equally critical to avoid exacerbating existing healthcare disparities. Scientific societies and regulatory agencies must ultimately establish standardized frameworks for data management, model certification, and transparency, to enable safe and effective integration of AI into clinical practice. This manuscript aims at systematically reviewing the current state-of-the-art applications of AI in cardiovascular pharmacotherapy, describing their current potential in guiding treatment decisions, refine trial methodologies and support drug discovery.

MaltSci.com AI Research Service

Intelligent ReadingAnswer any question about the paper and explain complex charts and formulas

Locate StatementsFind traces of a specific claim within the paper

Add to KBasePerform data extraction, report drafting, and advanced knowledge mining

Primary Questions Addressed

1. How can AI be utilized to improve patient stratification in cardiovascular pharmacotherapy?
2. What are the ethical considerations in using AI for personalized treatment plans in cardiovascular diseases?
3. In what ways can machine learning algorithms enhance the prediction of adverse drug reactions in cardiovascular treatments?
4. How does the integration of real-world data into AI models impact the effectiveness of cardiovascular pharmacotherapy?
5. What role do regulatory agencies play in ensuring the safety and efficacy of AI applications in cardiovascular drug development?

Key Findings

Research Background and Objectives

Recent advancements in artificial intelligence (AI) have opened new avenues for enhancing cardiovascular pharmacotherapy. The primary objective of this manuscript is to systematically review the current applications of AI in this field, focusing on its potential to optimize drug selection, predict therapeutic efficacy and adverse effects, and ultimately improve patient outcomes.

Main Methods/Materials/Experimental Design

The study utilizes a comprehensive review approach to evaluate various AI techniques, particularly machine learning and in silico modeling, in the context of cardiovascular pharmacotherapy. The following key processes are highlighted:

1. AI Techniques: The study focuses on machine learning and in silico modeling.
2. Drug Selection Optimization: Identifying patient populations that benefit from specific treatments.
3. Predicting Therapeutic Efficacy and Adverse Effects: Using AI to foresee outcomes and side effects.
4. Novel Drug Discovery: Accelerating the discovery process and reducing costs.
5. Detection of Drug Interactions: Using computational methods for better intervention strategies.
6. Clinical Trial Design: Leveraging real-time patient data to enhance trial methodologies and recruitment efficiency.

Key Results and Findings

• AI can significantly enhance the precision of drug selection, tailoring therapies to individual patient needs.
• Machine learning algorithms are effective in predicting patient responses to treatments, thereby improving therapeutic efficacy.
• In silico modeling contributes to faster drug discovery processes, enabling quicker market access for new therapies.
• AI-based approaches have the potential to improve clinical trial designs by optimizing recruitment and utilizing real-time data for better patient monitoring.

Main Conclusions/Significance/Innovation

The integration of AI into cardiovascular pharmacotherapy presents innovative solutions for personalizing treatment, enhancing drug discovery, and improving clinical trial methodologies. However, for these benefits to be fully realized, there is a need for robust validation of AI applications across diverse patient populations to ensure accuracy and generalizability. Establishing standardized frameworks for data management, model certification, and transparency is crucial for the safe and effective incorporation of AI into clinical practice.

Research Limitations and Future Directions

• Limitations: The manuscript acknowledges potential challenges, including concerns about data quality, privacy, and bias, which could exacerbate healthcare disparities.
• Future Directions: Emphasis is placed on the need for ongoing research to validate AI applications across varied demographics and clinical settings. Additionally, collaboration among scientific societies and regulatory agencies is essential to develop standardized practices that support the ethical use of AI in healthcare.

Aspect	Description
Research Objective	Review AI applications in cardiovascular pharmacotherapy.
Key AI Techniques	Machine learning, in silico modeling.
Benefits	Optimized drug selection, improved efficacy predictions, accelerated drug discovery.
Challenges	Data quality, privacy, bias, and the need for robust validation.
Future Needs	Standardized frameworks for data management and ethical AI integration in clinical practice.

References

1. Using artificial intelligence to identify anti-hypertensives as possible disease modifying agents in Parkinson's disease. - Naomi P Visanji;Piyush Madan;Alix M B Lacoste;Italo Buleje;Yanyan Han;Scott Spangler;Lorraine V Kalia;Sharon Hensley Alford;Connie Marras - Pharmacoepidemiology and drug safety (2021)
2. Evaluation of machine learning algorithms for renin-angiotensin-aldosterone system inhibitors associated renal adverse event prediction. - Alper Tuna Güven;Murat Özdede;Yusuf Ziya Şener;Ali Osman Yıldırım;Sabri Engin Altıntop;Berkay Yeşilyurt;Oğuz Abdullah Uyaroğlu;Mine Durusu Tanrıöver - European journal of internal medicine (2023)
3. Effect of Home Blood Pressure Monitoring via a Smartphone Hypertension Coaching Application or Tracking Application on Adults With Uncontrolled Hypertension: A Randomized Clinical Trial. - Stephen D Persell;Yaw A Peprah;Dawid Lipiszko;Ji Young Lee;Jim J Li;Jody D Ciolino;Kunal N Karmali;Hironori Sato - JAMA network open (2020)
4. Evaluating eligibility criteria of oncology trials using real-world data and AI. - Ruishan Liu;Shemra Rizzo;Samuel Whipple;Navdeep Pal;Arturo Lopez Pineda;Michael Lu;Brandon Arnieri;Ying Lu;William Capra;Ryan Copping;James Zou - Nature (2021)
5. Machine Learning Applied to Cholesterol-Lowering Pharmacotherapy: Proof-of-Concept in High-Risk Patients Treated in Primary Care. - Andrew J Krentz;Gabe Haddon-Hill;Xiaoyan Zou;Natalie Pankova;André Jaun - Metabolic syndrome and related disorders (2023)
6. Using deep learning-based natural language processing to identify reasons for statin nonuse in patients with atherosclerotic cardiovascular disease. - Ashish Sarraju;Jean Coquet;Alban Zammit;Antonia Chan;Summer Ngo;Tina Hernandez-Boussard;Fatima Rodriguez - Communications medicine (2022)
7. Demographic and epidemiologic drivers of global cardiovascular mortality. - Gregory A Roth;Mohammad H Forouzanfar;Andrew E Moran;Ryan Barber;Grant Nguyen;Valery L Feigin;Mohsen Naghavi;George A Mensah;Christopher J L Murray - The New England journal of medicine (2015)
8. Detection of left ventricular systolic dysfunction from single-lead electrocardiography adapted for portable and wearable devices. - Akshay Khunte;Veer Sangha;Evangelos K Oikonomou;Lovedeep S Dhingra;Arya Aminorroaya;Bobak J Mortazavi;Andreas Coppi;Cynthia A Brandt;Harlan M Krumholz;Rohan Khera - NPJ digital medicine (2023)
9. New artificial intelligence prediction model using serial prothrombin time international normalized ratio measurements in atrial fibrillation patients on vitamin K antagonists: GARFIELD-AF. - Shinichi Goto;Shinya Goto;Karen S Pieper;Jean-Pierre Bassand;Alan John Camm;David A Fitzmaurice;Samuel Z Goldhaber;Sylvia Haas;Alexander Parkhomenko;Ali Oto;Frank Misselwitz;Alexander G G Turpie;Freek W A Verheugt;Keith A A Fox;Bernard J Gersh;Ajay K Kakkar - European heart journal. Cardiovascular pharmacotherapy (2020)
10. Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. - Varun Gulshan;Lily Peng;Marc Coram;Martin C Stumpe;Derek Wu;Arunachalam Narayanaswamy;Subhashini Venugopalan;Kasumi Widner;Tom Madams;Jorge Cuadros;Ramasamy Kim;Rajiv Raman;Philip C Nelson;Jessica L Mega;Dale R Webster - JAMA (2016)

Literatures Citing This Work

1. Myocardial infarction, stroke and arterial stenosis: time to reassess a major misunderstanding. - Luca Saba;Peter Libby - Nature reviews. Cardiology (2025)

---

© 2025 MaltSci - We reshape scientific research with AI technology