PUBLISHER: QYResearch | PRODUCT CODE: 1872071
PUBLISHER: QYResearch | PRODUCT CODE: 1872071
The global market for AI-Assisted Diagnosis was estimated to be worth US$ 25100 million in 2024 and is forecast to a readjusted size of US$ 64565 million by 2031 with a CAGR of 16.0% during the forecast period 2025-2031.
AI-Assisted Diagnosis refers to the use of artificial intelligence technologies to detect, predict, and diagnose faults in industrial equipment. It integrates machine learning, deep learning, data mining, and sensor technologies to collect real-time data on equipment performance, analyze its condition, and identify potential faults using intelligent algorithms. The primary goal of this technology is to monitor the health status of equipment, detect potential issues in advance, reduce downtime, optimize maintenance schedules, and improve production efficiency.
AI-Assisted Diagnosis is widely applied in various sectors, particularly in industries such as manufacturing, energy, transportation, and aerospace. For equipment that requires high precision and reliability, such as wind turbines, aircraft engines, and robots, AI diagnostic technologies can monitor real-time data on vibration, temperature, pressure, and other parameters to predict potential faults and provide maintenance recommendations. This helps to avoid unexpected failures and extends the lifespan of equipment. The system typically includes sensors, data acquisition devices, data transmission systems, and diagnostic algorithm modules, generating real-time health reports and providing intelligent alerts.
As AI technologies continue to evolve, the accuracy and scope of AI-Assisted Diagnosis are expanding. More and more companies are adopting this technology to achieve intelligent and automated equipment maintenance and management, thereby enhancing the stability and efficiency of production lines.
The AI-Assisted Diagnosis market is rapidly growing, primarily driven by advancements in industrial automation, smart manufacturing, and Internet of Things (IoT) technologies. The increasing demand for efficiency and reliability in equipment maintenance across industries such as manufacturing, energy, and transportation has led to the widespread adoption of AI technologies for fault diagnosis. This is especially true for industries that require high-precision equipment operation, such as aerospace, energy, and automotive manufacturing, where AI fault diagnosis technologies help reduce human intervention and minimize downtime.
Key driving factors for the market include: First, the widespread adoption of the Industrial Internet of Things (IIoT) has enabled more equipment to collect real-time operational data, providing abundant data sources for AI diagnostics. Second, the advancement of smart manufacturing and automated production lines has made AI fault diagnosis systems essential for improving production efficiency and equipment management. Additionally, continuous progress in AI algorithms and computational power has significantly enhanced the accuracy and real-time performance of equipment fault diagnosis.
However, the market faces some challenges and risks. First, implementing AI-Assisted Diagnosis requires a large amount of high-quality data, and the acquisition, transmission, and storage of such data present technical and security challenges. Second, the complexity and diversity of equipment faults require AI algorithms to be highly adaptable, necessitating customized solutions for different equipment and operational conditions. Finally, the training of maintenance personnel and their acceptance of the technology are crucial factors for widespread adoption.
Regarding market concentration, large tech companies such as Siemens, GE, and ABB have made significant strides in the field and have expanded their market share through acquisitions and partnerships. As the technology matures, more innovative companies are expected to emerge. In terms of downstream demand, industries such as manufacturing, energy, and high-end equipment production are the primary drivers of AI-Assisted Diagnosis, particularly those sectors with critical equipment operation and high levels of automation, which will drive widespread adoption of this technology.
This report aims to provide a comprehensive presentation of the global market for AI-Assisted Diagnosis, focusing on the total sales revenue, key companies market share and ranking, together with an analysis of AI-Assisted Diagnosis by region & country, by Type, and by Application.
The AI-Assisted Diagnosis market size, estimations, and forecasts are provided in terms of sales revenue ($ millions), considering 2024 as the base year, with history and forecast data for the period from 2020 to 2031. With both quantitative and qualitative analysis, to help readers develop business/growth strategies, assess the market competitive situation, analyze their position in the current marketplace, and make informed business decisions regarding AI-Assisted Diagnosis.
Market Segmentation
By Company
Segment by Type
Segment by Application
By Region
Chapter Outline
Chapter 1: Introduces the report scope of the report, global total market size. This chapter also provides the market dynamics, latest developments of the market, the driving factors and restrictive factors of the market, the challenges and risks faced by manufacturers in the industry, and the analysis of relevant policies in the industry.
Chapter 2: Detailed analysis of AI-Assisted Diagnosis company competitive landscape, revenue market share, latest development plan, merger, and acquisition information, etc.
Chapter 3: Provides the analysis of various market segments by Type, covering the market size and development potential of each market segment, to help readers find the blue ocean market in different market segments.
Chapter 4: Provides the analysis of various market segments by Application, covering the market size and development potential of each market segment, to help readers find the blue ocean market in different downstream markets.
Chapter 5: Revenue of AI-Assisted Diagnosis in regional level. It provides a quantitative analysis of the market size and development potential of each region and introduces the market development, future development prospects, market space, and market size of each country in the world.
Chapter 6: Revenue of AI-Assisted Diagnosis in country level. It provides sigmate data by Type, and by Application for each country/region.
Chapter 7: Provides profiles of key players, introducing the basic situation of the main companies in the market in detail, including product revenue, gross margin, product introduction, recent development, etc.
Chapter 8: Analysis of industrial chain, including the upstream and downstream of the industry.
Chapter 9: Conclusion.