The Global Artificial Intelligence In Cardiology Market Size is valued at 1.22 billion in 2023 and is predicted to reach 11.5 billion by the year 2031 at a 32.70% CAGR during the forecast period for 2024-2031.
Cardiology's application of machine learning techniques, computer vision, and other AI technologies is called artificial intelligence (AI). Internet of Things (IoT), Precision medicine, and other cutting-edge digital technologies will play a significant role in the future of cardiology.
The development of techniques for the detection of malignant arrhythmias through wearables, precise CVD prediction of results, diagnosis, treatment options, and outcome prediction for patients with heart failure (HF), and non-intrusive examination of coronary artery disease (CAD) all serve as examples of the potential of AI in future cardiology. Furthermore, owing to the high prevalence of cardiovascular disorders, the widespread use of minimally invasive procedures, the availability of reimbursements, the growing senior population, and the strong demand for ongoing, at-home monitoring.
Several of the nation's major market players are also creating new items and technologies to rival those already on the market. Others are purchasing and collaborating with other hot commodities businesses. This is brought on by several elements, including the region's developed healthcare system, quick adoption of cutting-edge technology, and rising regulatory approvals of AI products.
The high illness load and expanding healthcare system in the area are anticipated to accelerate the use of AI in cardiac diagnosis and therapy. The variables listed above are all anticipated to assist the region. AI can also identify a patient's propensity for developing chronic heart diseases, enabling earlier diagnosis and better care planning. The market has benefited from recent large industry transactions and alliances in AI cardiology that have been made, as well as recent significant investments in AI-powered cardiology technologies.
Artificial intelligence in the cardiology market is segmented based on components and applications. Artificial intelligence in the cardiology market is divided into software, hardware and services based on components. By application, the market is segmented into cardiac arrhythmias, stroke, ischemic heart disease /CAD and others.
The software category is expected to hold a major share in the global artificial intelligence in cardiology market in 2024. This surge can be ascribed to the increased demand for AI-enabled diagnostic tools that diagnose cardiac patients exceptionally accurately. Additionally, this software improves heart analysis and boosts productivity. The demand for AI software in cardiology is increasing due to these causes. Through data analysis, AI-based software improves physician decision-making. The ECG, aided by AI algorithms, has shortened the time it takes to diagnose patients and can detect anomalies. In addition, several software companies have utilized tactics including investment, alliances, and others to strengthen their overall market position.
The cardiac arrhythmias segment is projected to grow rapidly in the global artificial intelligence in cardiology market. One of the key drivers of the market's development is AI's high accuracy in recognizing arrhythmias. The deployment of AI in arrhythmia applications should increase as a result. Blockages in the arteries, stress, high blood pressure, and sleep apnea are just a few of the causes of arrhythmia. The demand for AI for detecting and treating arrhythmias is further anticipated to increase due to the huge number of people who suffer from these conditions, especially in countries such as the US, Germany, the UK, China, and India.
The North American artificial intelligence in the cardiology market is expected to report the highest market share in revenue shortly. The continuous investment in creating AI-based cardiology solutions reflects the potential that manufacturers and cardiologists see in AI, which will favor the market. Significant studies will be required to support its implementation into practice because the advantages to patients must be established, and the diagnostic accuracy in this area must be extraordinarily high.
In addition, Asia Pacific is projected to increase at a rapid rate. Geriatrics are becoming more prevalent, and diseases like cancer and stroke are becoming more common, which has resulted in a rise in the use of AI technology to identify and diagnose ailments. Another key factor propelling the development of AI in cardiology in this region is the rise in investment in startups using artificial intelligence (AI) in healthcare.
| Report Attribute | Specifications |
| Market size value in 2023 | USD 1.22 Bn |
| Revenue Forecast in 2031 | USD 11.54 Bn |
| Growth rate CAGR | CAGR of 32.70% from 2024 to 2031 |
| Quantitative units | Representation of revenue in US$ Million, and CAGR from 2024 to 2031 |
| Historic Year | 2019 to 2023 |
| Forecast Year | 2024-2031 |
| Report coverage | The forecast of revenue, the position of the company, the competitive market statistics, growth prospects, and trends |
| Segments covered | Components And Applications |
| Regional scope | North America; Europe; Asia Pacific; Latin America; Middle East & Africa |
| Country scope | U.S.; Canada; U.K.; Germany; China; India; Japan; Brazil; Mexico; The UK; France; Italy; Spain; China; Japan; India; South Korea; Southeast Asia; South Korea; Southeast Asia |
| Competitive Landscape | IDOVEN; Cardia; Optronics Limited.; Artery’s Inc.; Cardiology; Ultralight; Dial Imaging Analysis; Vista AI; Viz ai; RSIP Vision; Clearly, Inc. |
| Customization scope | Free customization report with the procurement of the report, Modifications to the regional and segment scope. Particular Geographic competitive landscape. |
| Pricing and available payment methods | Explore pricing alternatives that are customized to your particular study requirements. |
Artificial Intelligence in Cardiology Market By Component
Artificial Intelligence in Cardiology Market By Application
Artificial Intelligence in Cardiology Market By Region-
North America-
Europe-
Asia-Pacific-
Latin America-
Middle East & Africa-
This study employed a multi-step, mixed-method research approach that integrates:
This approach ensures a balanced and validated understanding of both macro- and micro-level market factors influencing the market.
Secondary research for this study involved the collection, review, and analysis of publicly available and paid data sources to build the initial fact base, understand historical market behaviour, identify data gaps, and refine the hypotheses for primary research.
Secondary data for the market study was gathered from multiple credible sources, including:
These sources were used to compile historical data, market volumes/prices, industry trends, technological developments, and competitive insights.
Primary research was conducted to validate secondary data, understand real-time market dynamics, capture price points and adoption trends, and verify the assumptions used in the market modelling.
Primary interviews for this study involved:
Interviews were conducted via:
Primary insights were incorporated into demand modelling, pricing analysis, technology evaluation, and market share estimation.
All collected data were processed and normalized to ensure consistency and comparability across regions and time frames.
The data validation process included:
This ensured that the dataset used for modelling was clean, robust, and reliable.
The bottom-up approach involved aggregating segment-level data, such as:
This method was primarily used when detailed micro-level market data were available.
The top-down approach used macro-level indicators:
This approach was used for segments where granular data were limited or inconsistent.
To ensure accuracy, a triangulated hybrid model was used. This included:
This multi-angle validation yielded the final market size.
Market forecasts were developed using a combination of time-series modelling, adoption curve analysis, and driver-based forecasting tools.
Given inherent uncertainties, three scenarios were constructed:
Sensitivity testing was conducted on key variables, including pricing, demand elasticity, and regional adoption.