Exploring AI-healthcare innovation: natural language processing-based patents analysis for technology-driven roadmapping

• Published in: Kybernetes Vol. 52; no. 4; pp. 1173 - 1189
• Main Authors: Wang, Yu-Hui, Lin, Guan-Yu
• Format: Journal Article
• Language: English
• Published: London Emerald Publishing Limited 24.03.2023; Emerald Group Publishing Limited
• Subjects: Artificial intelligence; Automation; Commercialization; Computed tomography; Computers; Costs; Deep learning; Disease; Domains; Health care; Health care industry; Machine learning; Manufacturing engineering; Medical imaging; Natural language processing; R&D; Research & development; Robotics; Speech; Technological change; Technology; Trends; Voice recognition; United States > US; Natural language processing; AI healthcare; Artificial intelligence; Patent analysis
• ISSN: 0368-492X; 1758-7883
• DOI: 10.1108/K-03-2021-0170

PurposeThe purposes of this paper are (1) to explore the overall development of AI technologies and applications that have been demonstrated to be fundamentally important in the healthcare industry, and their related commercialized products and (2) to identify technologies with promise as the basis of useful applications and profitable products in the AI-healthcare domain.Design/methodology/approachThis study adopts a technology-driven technology roadmap approach, combined with natural language processing (NLP)-based patents analysis, to identify promising and potentially profitable existing AI technologies and products in the domain of AI healthcare.FindingsRobotics technology exhibits huge potential in surgical and diagnostics applications. Intuitive Surgical Inc., manufacturer of the Da Vinci robotic system and Ion robotic lung-biopsy system, dominates the robotics-assisted surgical and diagnostic fields. Diagnostics and medical imaging are particularly active fields for the application of AI, not only for analysis of CT and MRI scans, but also for image archiving and communications.Originality/valueThis study is a pioneering attempt to clarify the interrelationships of particular promising technologies for application and related products in the AI-healthcare domain. Its findings provide critical information about the patent activities of key incumbent actors, and thus offer important insights into recent and current technological and product developments in the emergent AI-healthcare sector.