Current Trend of Artificial Intelligence Patents in Digital Pathology: A Systematic Evaluation of the Patent Landscape

被引:22
|
作者
Ailia, Muhammad Joan [1 ]
Thakur, Nishant [1 ]
Abdul-Ghafar, Jamshid [1 ]
Jung, Chan Kwon [1 ]
Yim, Kwangil [1 ]
Chong, Yosep [1 ]
机构
[1] Catholic Univ Korea, Dept Hosp Pathol, Coll Med, Seoul 06591, South Korea
关键词
artificial intelligence; deep learning; digital pathology; intellectual property; patents; BREAST-CANCER;
D O I
10.3390/cancers14102400
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
Simple Summary The combination of digital pathology (DP) with artificial intelligence (AI) offers faster, more accurate, and more comprehensive diagnoses, resulting in more precise individualized treatment. As this technology is constantly evolving, it is critical to understand the current state of AI applications in DP. Thus, it is necessary to analyze AI patent applications, assignees, and leaders in the field. In this study, five major patent databases, namely, those of the USPTO, EPO, KIPO, JPO, and CNIPA, were searched using key phrases, such as DP, AI, machine learning, and deep learning, and 523 patents were shortlisted based on the inclusion criteria. Our data demonstrated that the key areas of the patents were whole-slide imaging, segmentation, classification, and detection. In the past five years, an increasing trend in patent filing has been observed, mainly in a few prominent countries, with a focus on the digitization of pathological images and AI technologies that support the critical role of pathologists. The integration of digital pathology (DP) with artificial intelligence (AI) enables faster, more accurate, and thorough diagnoses, leading to more precise personalized treatment. As technology is advancing rapidly, it is critical to understand the current state of AI applications in DP. Therefore, a patent analysis of AI in DP is required to assess the application and publication trends, major assignees, and leaders in the field. We searched five major patent databases, namely, those of the USPTO, EPO, KIPO, JPO, and CNIPA, from 1974 to 2021, using keywords such as DP, AI, machine learning, and deep learning. We discovered 6284 patents, 523 of which were used for trend analyses on time series, international distribution, top assignees; word cloud analysis; and subject category analyses. Patent filing and publication have increased exponentially over the past five years. The United States has published the most patents, followed by China and South Korea (248, 117, and 48, respectively). The top assignees were Paige.AI, Inc. (New York City, NY, USA) and Siemens, Inc. (Munich, Germany) The primary areas were whole-slide imaging, segmentation, classification, and detection. Based on these findings, we expect a surge in DP and AI patent applications focusing on the digitalization of pathological images and AI technologies that support the vital role of pathologists.
引用
收藏
页数:21
相关论文
共 50 条
  • [41] Integrating telepathology and digital pathology with artificial intelligence: An inevitable future
    Battazza, Alexandre
    Brasileiro, Felipe Cesar da Silva
    Tasaka, Ana Cristina
    Bulla, Camilo
    Ximenes, Pedro Pol
    Hosomi, Juliana Emi
    da Silva, Patricia Fernanda
    da Silva, Larissa Freire
    de Moura, Fernanda Barthelson Carvalho
    Rocha, Noeme Sousa
    VETERINARY WORLD, 2024, 17 (08) : 1667 - 1671
  • [42] Digital pathology and artificial intelligence as the next chapter in diagnostic hematopathology
    Lin, Elisa
    Fuda, Franklin
    Luu, Hung S.
    Cox, Andrew M.
    Fang, Fengqi
    Feng, Junlin
    Chen, Mingyi
    SEMINARS IN DIAGNOSTIC PATHOLOGY, 2023, 40 (02) : 88 - 94
  • [43] The use of digital pathology and artificial intelligence in the assessment of multiple myeloma
    McCabe, M.
    Sheehan, K.
    Glavey, S.
    VIRCHOWS ARCHIV, 2022, 481 (SUPPL 1) : S79 - S79
  • [44] Digital pathology and artificial intelligence in translational medicine and clinical practice
    Baxi, Vipul
    Edwards, Robin
    Montalto, Michael
    Saha, Saurabh
    MODERN PATHOLOGY, 2022, 35 (01) : 23 - 32
  • [45] Artificial Intelligence and Machine Learning in Pathology: The Present Landscape of Supervised Methods
    Rashidi, Hooman H.
    Tran, Nam K.
    Betts, Elham Vali
    Howell, Lydia P.
    Green, Ralph
    ACADEMIC PATHOLOGY, 2019, 6
  • [46] Identifying technological competition situations for artificial intelligence technology - a patent landscape analysis
    Li, Xin
    Fan, Mingjie
    Liang, Zheng
    INTERNATIONAL JOURNAL OF TECHNOLOGY MANAGEMENT, 2020, 82 (3-4) : 322 - 348
  • [47] Artificial Intelligence in pathology: current applications, limitations, and future directions
    Sajithkumar, Akhil
    Thomas, Jubin
    Saji, Ajish Meprathumalil
    Ali, Fousiya
    Hasin, E. K. Haneena
    Adampulan, Hannan Abdul Gafoor
    Sarathchand, Swathy
    IRISH JOURNAL OF MEDICAL SCIENCE, 2024, 193 (02) : 1117 - 1121
  • [48] Artificial Intelligence in pathology: current applications, limitations, and future directions
    Akhil Sajithkumar
    Jubin Thomas
    Ajish Meprathumalil Saji
    Fousiya Ali
    Haneena Hasin E.K
    Hannan Abdul Gafoor Adampulan
    Swathy Sarathchand
    Irish Journal of Medical Science (1971 -), 2024, 193 : 1117 - 1121
  • [49] Current and future applications of artificial intelligence in pathology: a clinical perspective
    Rakha, Emad A.
    Toss, Michael
    Shiino, Sho
    Gamble, Paul
    Jaroensri, Ronnachai
    Mermel, Craig H.
    Chen, Po-Hsuan Cameron
    JOURNAL OF CLINICAL PATHOLOGY, 2021, 74 (07) : 409 - 414
  • [50] Autonomous Vehicles: Evolution of Artificial Intelligence and the Current Industry Landscape
    Garikapati, Divya
    Shetiya, Sneha Sudhir
    BIG DATA AND COGNITIVE COMPUTING, 2024, 8 (04)