AI in Respiratory Diseases Market Size is predicted to witness a 29.4% CAGR during the forecast period for 2025-2034.
AI is revolutionizing the field of respiratory diseases by aiding in early diagnosis, personalized treatment, and remote monitoring. It can analyze medical images, predict disease progression, and even assist in drug discovery. AI also helps patients manage conditions like asthma and COPD while monitoring air quality and providing real-time insights. These advancements hold promise for improving patient care and outcomes in respiratory health.
Clinical data, chest scans, lung pathology, and pulmonary function testing can all be used by AI to aid in the diagnosis and prognosis of pulmonary disorders. The advent of new digital tools drives market expansion, and fields like genomics, medical imaging, and electronic health records are contributing to an unprecedented explosion in the volume and complexity of healthcare-related data. Because of this explosive expansion, more and more clinically relevant applications based on AI have been developed.
Expert pulmonologists who have studied AI theory and practice will be in a strong position to exploit emerging career prospects in both clinical practice and academic research. This review aims to educate pulmonologists and other interested readers about artificial intelligence's potential applications in this field of medicine. The advancement of healthcare technology has led to an anticipated uptick in the use of AI in respiratory diseases.
However, several negative aspects seriously restrain the expansion of AI in the respiratory diseases market. Lack of knowledge and high cost are hindering the AI in respiratory diseases market growth. In addition, the COVID-19 pandemic has had a major effect on the market for providers of artificial intelligence in respiratory diseases medicine. Several factors have contributed to this effect. Genomics, diagnostics, drug development, and individualized treatment are just some of the areas where artificial intelligence technologies like deep learning and the processing of natural language have found use. During the recent COVID-19 epidemic, the value of AI in responding to medical emergencies and improving our understanding of complex diseases became clear.
The AI in respiratory diseases market is segmented based on the indication, imaging type, and end-use. As per the indication, the market is segmented into chronic obstructive pulmonary disease, interstitial lung disease, and pulmonary infection. By imaging type, the market is segmented into MRI, CT Scan, and ePRO. According to end-use segment, the market is segmented into hospitals, diagnostic centres, ambulatory surgical centers, and others.
The CT Scan AI in the respiratory diseases category is expected to hold a maximum global market share because it helps in the diagnosis of lung problems such as pneumonia, cancer, blood clots, and smoking-related damage.
The diagnostic centers are expected to grow rapidly in the global AI in respiratory diseases market. The crucial role of diagnostic centers in the diagnosis of diseases at an early stage cannot be overstated. Early diagnosis is solution to the successful treatment of different diseases, including cancer, cardiovascular disease. Screening tests and preventative medical examinations are available at medical clinics that specialize in diagnosing and treating such conditions, especially in countries like the US, Germany, the UK, China, and India.
The North American AI in respiratory diseases market activity is anticipated to record the largest market share in revenue in the near future. It can be attributed to the expansion of financial resources and time spent on R&D. As the need for innovative healthcare services grows, governments, institutions, and investors are pouring more money into projects that aim to apply artificial intelligence to the practice of respiratory disease medicine. The need for AI in Respiratory Diseases is anticipated to increase as a result of this region.
| Report Attribute | Specifications |
| Growth Rate CAGR | CAGR of 29.4% from 2025 to 2034 |
| Quantitative Units | Representation of revenue in US$ Million and CAGR from 2025 to 2034 |
| Historic Year | 2021 to 2024 |
| Forecast Year | 2025-2034 |
| Report Coverage | The forecast of revenue, the position of the company, the competitive market structure, growth prospects, and trends |
| Segments Covered | By Indication, Imaging Type, End-Use |
| 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; South East Asia |
| Competitive Landscape | Siemens Healthineers, VIDA Diagnostics Inc,THIRONA, Infervision, icometrix. GE Healthcare, Philips Healthcare, Others |
| 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. |
AI in Respiratory Diseases Market By Indication-
AI in Respiratory Diseases Market By Imaging Type -
AI in Respiratory Diseases Market By End-use -
AI in Respiratory Diseases 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.