市場調查報告書
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1574144
放射學人工智慧 (AI) 市場:2024-2029 年預測Artificial Intelligence (AI) in Radiology Market - Forecasts from 2024 to 2029 |
預計放射學市場中的人工智慧在預測期內將以 30.45% 的複合年成長率成長,從 2024 年的 22.7561 億美元市場規模成長到 2029 年的 85.96802 萬美元。
人工智慧 (AI) 中的深度學習演算法在基於視覺的應用中得到了完善。隨著變分自動編碼器和卷積類神經網路技術的實現,醫學影像分析領域正在迅速擴展。由於測量的方便性,X光相片品質的傳統定性評估有所不同。此外,人工智慧技術在分析影像資訊中包含的機械上困難的資訊模式方面變得越來越先進。例如,在放射學中,人工智慧演算法可以被設計來測量特定的放射線攝影特性,例如腫瘤的 3D 形狀、每個像素的紋理以及腫瘤內的像素強度。
X 光照相術可以讓有執照的醫生研究臨床圖片和套件說明,以及識別和統一疾病,從而使他們能夠檢測、識別和監測疾病。其評估需要大量的專業知識和經驗,有時容易受到意見的影響。與這種定性和主觀評估相反,人工智慧非常擅長自動執行客觀的數值分析,同時識別影像資料中的微妙模式。部署人工智慧來支援和協助乳房X光攝影醫生將實現更準確和可重複的放射學評估。該應用程式為未來幾年和幾十年放射學市場人工智慧的進一步開拓打開了大門。
基於人工智慧的體積腫瘤分割可以提高所有腦腫瘤和其他神經系統癌症的識別和檢測,具有極高的準確性和一致性。該系統還可以透過 MRI 掃描自動識別腦腫瘤。這些策略對於以可重複和公正的方式提供準確的診斷和評估腫瘤對治療的反應將具有無價的價值。這種神經治療的另一個使用案例是使用人工智慧來預測治療結果,這有助於利用最佳策略。背景 機器學習已被用來根據 MR 影像的血液容積分佈資料來預測病患的生存率。
例如,2023 年 2 月,放射篩檢人工智慧平台 Avicenna 籌集了 700 萬歐元的 A 輪融資,使其資本基礎達到 1,000 萬歐元。此階段採用基於影像的深度學習來識別和評估放射學研究設施中危及生命的疾病。 電腦斷層掃描影像用於在診斷前優先考慮有症狀的患者。 Avicenna.AI 提供兩種變體。一是心臟病發作的跡象和可能性,二是腦損傷和中風的可能性。該平台幫助放射科醫師確定患者的生命是否受到威脅。
由於醫療和生物技術行業的研究支出和開發不斷增加,預計亞太地區將在放射學市場的人工智慧中佔據重要佔有率。此外,這些地區預計的大量患者數量將增加對更好的癌症治療基礎設施的需求,從而推動醫療保健行業的成長並促進區域層級的市場開拓。亞太地區也正在投資醫療保健以推動新技術,特別是在新興經濟體。
該地區的經濟體越來越注重建立健康的醫療保健系統,以實現患者的早期診斷和早期治療。此外,隨著各大主要企業都計劃在亞太國家擴大和建設設施,該地區的市場在未來幾年可能會成長。例如,2023年5月,該領域的世界領導者、基於人工智慧的放射學公司Annalise.ai在印度清奈設立了第一個中心。透過這項策略舉措,Annalize 繼續滲透到世界舞台,在亞洲等成熟市場和新興市場開展業務。該中心專注於研究和商業化新產品,將先進的成像資料和電腦科學相結合,提供完整的人工智慧解決方案來支援臨床決策。
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The AI in radiology market is projected to grow at a CAGR of 30.45% during the forecast period, reaching a total market size of US$8,596.802 million by 2029, up from US$2,275.610 million in 2024.
Deep learning algorithms in artificial intelligence (AI) have been perfected in vision-based applications. The medical image analysis domain is expanding rapidly with the realization of variational autoencoders and convolutional neural network techniques. Traditional qualitative assessments for radiographic qualities differ since the measure is easy to perform. Moreover, AI techniques are more advanced at analyzing mechanically difficult information patterns in imaging information. For instance, in radiology, AI algorithms could be designed to measure specific radiographic characteristics, such as the 3D shape of a tumor, every pixel's texture, and pixel intensity within the tumor.
X-ray radiography is when licensed medical doctors study clinical photos and kit a statement or identify and single out diseases that allow disorders to be detected, identified, and monitored. That assessment requires a lot of expertise and experience, which is sometimes susceptible to opinion. In contrast to this qualitative, subjective evaluation, AI is extremely good at identifying subtle patterns in imaging data while automatically providing an objective numerical analysis. Implementing AI to support and assist mammogram physicians can result in more precise and reproducible radiological assessments. This application is opening the door to further development into AI in the radiology market in years or decades to come.
Working off of volumetric tumor segmentation, AI can improve identification and detection across all brain tumors and other neurological cancers with superior accuracy and consistency. The system will also automatically identify brain tumors on MRI scans. These strategies can be extremely valuable in providing precise diagnoses and assessing the tumor response to treatment in a reproducible and unbiased manner. Another use case in this neurological treatment is the prediction of outcomes using AI, which can assist in utilizing the best strategy. Background Machine learning has been used to predict survival among patients based on blood volume distribution data from MR imaging.
For instance, in February 2023, Avicenna, an AI platform for radiology screening, raised €7 million in a series A venture, bringing its capital base to €10 million. The stage employs image-trained profound learning to recognize and evaluate life-threatening ailments in radiology research facilities. It uses CT scan imaging to prioritize patients with symptoms before diagnosis. Avicenna.AI offers two variations: one for heart attack indications and chance and another for brain damage and stroke chance. The platform assists radiologists in deciding if a patient's life is threatened.
Asia Pacific is projected to hold substantial shares of AI in the radiology market owing to a rise in research spending and development in the medical and biotech industries. Moreover, the expected large number of patients in these areas will increase the demand for better cancer treatment infrastructure, propelling healthcare sector growth and aiding market development at a regional level. Asia Pacific has also seen investment in the healthcare sector, especially in emerging economies, to advance newer technologies.
The region's economies are increasingly focusing on creating a sound healthcare system for early patient diagnosis and treatment. In addition, various key companies are focusing on advancing their reach to Asia Pacific countries by building their facilities, leading to the regional market's growth in the coming years. For instance, in May 2023, Annalise. ai, an AI-based radiology company that is a global leader in the field, established its first Indian center in Chennai. Through this strategic move, Annalise continues penetrating the world arena with its presence in established and emerging markets like Asia. This translates into a center that specializes in the research and commercialization of new products containing advanced imaging data coupled with computer science that together results in complete AI solutions to support clinical decision-making.
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