[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117832-en":3,"doc-seo-117832-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117832,962075006959,"Anda","https://ap-avatar.wpscdn.com/avatar/e0002397efbe92a78e?_k=1776741047341049297",8,"Research & Report","Mapping the Patent Landscape of Medical Machine Learning","Patent office data indicate a strong and growing wave of AI patenting in medicine, challenging concerns that medical machine learning patents would be scarce after major eligibility decisions in the United States and limited activity in recent years in the US and Europe. The work addresses how frequently patents issue, which inventors and organizations obtain them, claim types used for protection, and the medical applications and input signals targeted. Using global patent data with a primary focus on US and European offices, the study empirically characterizes patenting patterns, leading entities, invention categories, and claim strategies to inform policy debates.","PATENTS  \nMapping the Patent Landscape of Medical Machine Learning  \nMateo Aboy, W. Nicholson Price II, Seth Raker  \nPatent office data show robust and rising patenting of AI inventions in the medical field, contrary to fears that medical machine learning patents might be largely unavailable due to post-Alice/Mayo challenges to their subject-matter eligibility and over a decade of limited MML patenting activity in the US and Europe.  \nArtificial intelligence (AI) is rapidly entering the field of medicine, but the role of patents in this process remains relatively opaque. Regulators report hundreds of machine learning (ML) medical devices that have passed regulatory oversight1, including systems involved inr a d i o l o g y , c a r d i o l o g y , ophthalmology, and many other fields. Major hospitals and academic m e di c al sy s t e m s h ave b o th developed and deployed AI/ML systems, and some AI tools have been embedded in electronic health records used by health systems covering millions of patients. Nevertheless, despite this wave of innovation in medical machine learning (MML), the influence of patents on that process has only b e e n s k e t c h e d r a th e r th a n interrogated in detail.  \nAbsent a detailed picture of patenting in the field of medical AI, legislators, patent offices, scholars, and other stakeholders are operating in the dark when determining how to use various policy levers to shape  \nMateo Aboy is with Centre for Law, Medicine, and Life Sciences (LML), Faculty of Law, University of Cambridge, Cambridge, UK; Nicholson Price and Seth Raker are with University of Michigan La w School. C o r r e s p o n d i n g A u t h o r E-m a i l :  \n[ma608@cam.ac.uk](ma608@cam.ac.uk)  \nthe innovative landscape for medical AI. How common are patents on medical AI inventions, and at what speed are they being issued? Who is obtaining patents on medical AI—is it a diverse group of inventors, or is it dominated by large entities in either the medical device space orthe software space? What kind of claims are used to protect medical AI inventions? Answers to these more basic questions are essential before tackling more complex policy issues, such as whether patents provide adequate or necessary incentives to overcome regulatory hurdles, the extent to which patents are fulfilling their disclosure function and enabling cumulative innovation, an d h ow p at ent s sh ap e th e availability of inventions to a broad set of potential users.  \nMany scholars, including one of us, h ave b e en skepti c al o f th e availability and strength of patents for ar ti fi cial intellig en c e in medicine2,3 . In the United States, pat e nt able s u bje c t mat t e r jurisprudence after the 2012 Mayo v. Prometheus, 2013 Myriad v. AMP and 2014 Alice v. CLS Bank cases raised doctrinal hurdles to obtaining patents for AI inventions that seem like they could easily rely on abstract ideas or laws of nature4. Adequate disclosure of AI systems also creates potential challenges for patentability,  \nsince AI system complexity or opacity could theoretically lead to inadequately descriptive functional disclosur e s or insufficient enablement.  \nEmpirical observations of patenting in medical AI can begin to clarify the role of patents in the field. Although the optimal level of patenting in any field is largely unknowable 5 , significant rates of patent issuance on medical AI inventions would suggest that patentability concerns are at least not blocking patenting activity. Similarly, if patenting has remained stable or at a low level while the field has seen increasing development and use, that would suggest that patents are not a particularly important incentive compared to, for instance, secrecy, proprietary training data, first-mover advantage, or forms of technological lock-in. If, on the other hand, patents are increasingly issued in the field to a range of inventors, they are likely to be playing some broader role in creating incentives for medical ","cbCaigfiAVUWAvg1","https://ap.wps.com/l/cbCaigfiAVUWAvg1","pdf",851489,1,9,"English","en",105,"# Research Questions\n## Search Strategy & Landscaping","[{\"question\":\"What gap does the paper identify in understanding medical AI patents?\",\"answer\":\"The paper notes that regulators and stakeholders see innovation in medical machine learning, but the role of patents in shaping that process has remained largely unexamined in detail.\"},{\"question\":\"Which patent datasets and offices does the study primarily use?\",\"answer\":\"It uses global patent data, focusing principally on the United States Patent and Trademark Office (PTO) and the European Patent Office (EPO).\"},{\"question\":\"What research questions guide the patent landscape analysis?\",\"answer\":\"The paper examines patenting trends over time, leading organizations and preferred patent offices, claim strategies and their prevalence, and the medical applications and input signals targeted by MML patents.\"}]","Mapping the Patent Landscape of Medical Machine Learning | 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gap does the paper identify in understanding medical AI patents?","Question",{"text":75,"@type":76},"The paper notes that regulators and stakeholders see innovation in medical machine learning, but the role of patents in shaping that process has remained largely unexamined in detail.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which patent datasets and offices does the study primarily use?",{"text":80,"@type":76},"It uses global patent data, focusing principally on the United States Patent and Trademark Office (PTO) and the European Patent Office (EPO).",{"name":82,"@type":73,"acceptedAnswer":83},"What research questions guide the patent landscape analysis?",{"text":84,"@type":76},"The paper examines patenting trends over time, leading organizations and preferred patent offices, claim strategies and their prevalence, and the medical applications and input signals targeted by MML 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