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  1. NTU Theses and Dissertations Repository
  2. 管理學院
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請用此 Handle URI 來引用此文件: http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/90730
完整後設資料紀錄
DC 欄位值語言
dc.contributor.advisor陳思寬zh_TW
dc.contributor.advisorShi-Kuan Chenen
dc.contributor.author廖漢聰zh_TW
dc.contributor.authorHan-Tsung Liaoen
dc.date.accessioned2023-10-03T17:22:15Z-
dc.date.available2023-11-09-
dc.date.copyright2023-10-03-
dc.date.issued2023-
dc.date.submitted2023-08-02-
dc.identifier.citation1. Abhishek Gupta,Prakash Kharbanda, Viren Sardana, Rajiv Balachandran, Harish Kumar SardanaOm. (2015年Nov月). A knowledge-based algorithm for automatic detection of cephalometric landmarks on CBCT images. Int J Comput Assist Radiol Surg, 頁 10(11):1737-52.
2. Bingjiang QiuGuo, Joep Kraeima, Haye H Glas, Ronald J H Borra, Max J H Witjes, Peter M A van OoijenJiapan. (2019 年Sep 月). Automatic segmentation of the mandible from computed tomography scans for 3D virtual surgical planning using the convolutional neural network. Phys Med Biol, 頁 5;64(17):175020.
3. Bradley J EricksonKorfiatis , Zeynettin Akkus , Timothy L KlinePanagiotis. (2017年3-4月). Machine Learning for Medical Imaging. Radiographics, 頁 37(2):505-515.
4. Cheng EJ, Yang J, Deng H, Wu Y, Megalooikonomou V,Chen. (2011). Automatic Dent-landmark detection in 3-D CBCT dental volumes. Conf Proc IEEE Eng Med Biol Soc , 頁 : 6204–7.
5. Dimitris Visvikis,Cheze Le Rest , Vincent Jaouen , Mathieu HattCatherine. (2019年Dec月). Artificial intelligence, machine (deep) learning and radio(geno)mics: definitions and nuclear medicine imaging applications. 頁 46(13):2630-2637.
6. Iansiti and Levien, R.M. (2004b.). The Keystone Advantage: What the New Dynamics of Business Ecosystems #Mean for Strategy, Innovation, and Sustainability, , Boston, MA:. Harvard Business School Press.
7. Iansiti and Levien, RM. (2004a). Strategy as Ecology. Harvard Business Review, 82, 3, pp.68–78.
8. Ju-Won Kim , Chun-Gi Jeong , Kyeong-Jun Cheon , Seoung-Won Cho, In-Young Park , Byoung-Eun YangJong-Cheo. (2019年Jan 15;19(1)月). The accuracy and stability of the maxillary position after orthognathic surgery using a novel computer-aided surgical simulation system. BMC Oral Health, 頁 18.
9. Montúfar JM, Scougall-Vilchis RJ, Scougall VRJ.Romero. (2018). Automatic 3-dimensional cephalometric landmarking based on active shape models in related projections. . Am J Orthod Dentofacial Orthop , 頁 153: 449–58.
10. MooreJ.F. (1993). Predators and prey: A new ecology of competition. Harvard Business Review,, 71(3), 1993, pp.75–86.
11. Park SHK.Han. (2018). Methodologic guide for evaluating clinical performance and effect of artificial intelligence technology for medical diagnosis and prediction. Radiology , 頁 286: 800–9.
12. Raphael Olszewski. (2012). Surgical Engineering in Cranio-Maxillofacial Surgery: A Literature Review. Journal of Healthcare Engineering, 頁 Vol. 3 · No. 1, Page 53-86.
13. Shahidi SE, Soltanimehr E, Zamani A, Oshagh M,Moattari M, Mehdizadeh ABahrampour. (2014). The accuracy of a designed software for automated localization of craniofacial landmarks on CBCT images. BMC Med Imaging , 頁 14(1):1471–2342.
14. 長陽生醫國際. 擷取自 醫療3D 列印技術產業聯盟: /http://www.med3d.com.tw/upload/web/images/Enews/第一期_數位醫療大邁進季刊.pdf
15. 張彥文. (2020年10月28日). 哈佛商業評論. 擷取自 https://today.line.me/tw/v2/article/R51RRe
16. 維基百科. (無日期). 擷取自 https://zh.m.wikipedia.org/zh-tw/%E7%94%9F%E6%80%81%E7%B3%BB%E7%BB%9F
17. 藍梅恩. (2021年3月24日). Mymkc.com管理知識中心. 擷取自 https://mymkc.com/article/content/24419
18. HDX WILL North America & HDX ACADEMY, 3D Orthodontic evaluation - HDX WILL E-Learning series 4th session; https://www.youtube.com/watch?v=bGCtFUFW0oQ
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dc.identifier.urihttp://tdr.lib.ntu.edu.tw/jspui/handle/123456789/90730-
dc.description.abstract近年來,人工智慧和電腦輔助技術的迅速發展為醫療領域帶來了革命性的變革。其中,人工智慧及電腦輔助精準顱顏面手術的應用已成為引人注目的領域。這項技術利用先進的影像分析、虛擬現實和模擬技術,通過精確的手術規劃和導航系統,幫助醫生實現更準確、更自然的手術結果。本研究旨在探討人工智慧及電腦輔助精準顱顏面手術的商業模式和生態系建立。首先,我們回顧了這一技術在醫療領域中的應用和發展趨勢。隨後,通過分析市場需求和競爭環境,我們提出了一個具體的商業模式,包括產品、服務和價值創造策略。
在商業模式中,我們強調了提供精準手術解決方案和個體化服務的重要性。透過虛擬現實和模擬技術,客戶能夠預測和確定手術後的外貌結果,減少客戶對手術結果的不確定感。同時,我們強調了與客戶的密切合作,讓客戶參與手術計劃的制定,確保其需求和期望得到充分考慮。此外,我們探討了痛點解方,包括解決客戶對手術結果不確定性的問題、降低手術風險和併發症的可能性,以及提高手術的精確性。這些解決方案可以通過人工智慧和電腦輔助技術的導航系統、風險評估和教育來實現。最後,我們討論了獲利引擎,包括手術服務收費、增值服務和技術授權等方面。這些獲利模式將有助於確保商業模式的可持續發展和經濟回報。
然而,要實現真正的成功,單單依靠技術本身是不夠的,需要建立一個完整的生態系統,涉及多個參與者,包括醫療機構、醫生、技術供應商、患者和保險公司等。本文探討了人工智慧及電腦輔助精準顱顏面手術的生態系統建立,以實現協同合作、資源整合和價值共創。
首先,醫療機構在生態系統中扮演著關鍵角色。他們提供手術設施和基礎設施,組織醫療團隊,並與技術供應商合作,共同推進精準顱顏面手術的應用。同時,醫療機構需要制定相應的政策和流程,確保手術安全和質量控制。 其次,醫生作為生態系統中的關鍵參與者,需要熟練掌握人工智慧和電腦輔助技術,並運用這些技術進行手術規劃和執行。他們應該與技術供應商緊密合作,不斷學習和更新手術技術,以確保手術的精準度和效果。
此外,技術供應商在生態系統中扮演著技術創新和解決方案提供的角色。他們開發和提供人工智慧和電腦輔助技術工具,包括影像處理、模擬和規劃系統等,支持醫生進行精準顱顏面手術。技術供應商需要與醫療機構和醫生合作,根據實際需求開發定制化解決方案。另外,患者也是生態系統中不可或缺的一部分。他們需要獲得適當的資訊和教育,了解人工智慧及電輔輔助精準顱顏面手術的優勢和風險。患者的參與和反饋對於改進手術結果和提升滿意度至關重要。最後,保險公司在生態系統中也發揮著重要作用。他們需要評估人工智慧及電輔輔助精準顱顏面手術的效果和成本效益,制定相應的保險政策和支付模式。保險公司的參與能夠促進手術的普及和可持續發展。
總之,人工智慧及電輔輔助精準顱顏面手術的成功實施需要建立一個健全的生態系統,涉及醫療機構、醫生、技術供應商、患者和保險公司等多個參與者。通過協同合作、資源整合和價值共創,我們可以實現更準確、更安全、更有效的顱顏面手術,從而改善患者的生活質量和醫療服務的可持續發展。
本研究的結果將對人工智慧及電腦輔助精準顱顏面手術的商業化和生態系建立提供有價值的參考。這一領域的發展將不僅為醫療行業帶來巨大的商機,同時也將改善患者的手術體驗和結果。我們期待未來更多的研究和實踐能夠推動這一領域的進一步發展和應用。
zh_TW
dc.description.abstractIn recent years, the rapid development of artificial intelligence and computer-assisted technologies has brought revolutionary changes to the medical field. Among them, the application of artificial intelligence and computer-assisted precision craniofacial surgery has become a remarkable area. This technology utilizes advanced image analysis, virtual reality, and simulation techniques to assist surgeons in achieving more accurate and natural surgical outcomes through precise surgical planning and navigation systems. This study aims to explore the business model and ecosystem establishment of artificial intelligence and computer-assisted precision craniofacial surgery. First, we review the applications and development trends of this technology in the medical field. Subsequently, through analyzing market demands and the competitive environment, we propose a specific business model, including product, service, and value creation strategies.
In the business model, we emphasize the importance of providing precise surgical solutions and personalized services. Through virtual reality and simulation techniques, customers can predict and determine the postoperative appearance, reducing uncertainty about the surgical outcomes. At the same time, we emphasize close collaboration with customers, involving them in the surgical planning process to ensure their needs and expectations are fully considered. Additionally, we address pain points, including resolving customer uncertainty about surgical outcomes, reducing surgical risks and potential complications, and improving surgical precision. These solutions can be achieved through navigation systems, risk assessment, and education using artificial intelligence and computer-assisted technologies. Finally, we discuss revenue engines, including surgical service fees, value-added services, and technology licensing. These profit models will contribute to ensuring the sustainable development and economic returns of the business model.
However, to achieve true success, relying solely on the technology itself is not enough. It is necessary to establish a complete ecosystem involving multiple participants, including healthcare institutions, surgeons, technology providers, patients, and insurance companies. This article explores the establishment of the ecosystem for artificial intelligence and computer-assisted precision craniofacial surgery to achieve synergistic cooperation, resource integration, and value co-creation.
Firstly, healthcare institutions play a crucial role in the ecosystem. They provide surgical facilities and infrastructure, organize medical teams, and collaborate with technology providers to promote the application of precision craniofacial surgery. Healthcare institutions also need to develop corresponding policies and procedures to ensure surgical safety and quality control.
Secondly, surgeons, as key participants in the ecosystem, need to master artificial intelligence and computer-assisted technologies and utilize them for surgical planning and execution. They should closely collaborate with technology providers, continuously learn and update surgical techniques to ensure surgical accuracy and effectiveness.
Additionally, technology providers play a role in technological innovation and solution provision in the ecosystem. They develop and provide artificial intelligence and computer-assisted technology tools, including image processing, simulation, and planning systems, to support surgeons in precision craniofacial surgery. Technology providers need to collaborate with healthcare institutions and surgeons to develop customized solutions based on actual needs.
Furthermore, patients are an indispensable part of the ecosystem. They need to receive appropriate information and education to understand the advantages and risks of artificial intelligence and computer-assisted precision craniofacial surgery. Patient involvement and feedback are crucial for improving surgical outcomes and satisfaction.
Lastly, insurance companies play an important role in the ecosystem. They need to assess the effectiveness and cost-effectiveness of artificial intelligence and computer-assisted precision craniofacial surgery and formulate corresponding insurance policies and payment models. The involvement of insurance companies can promote the popularization and sustainable development of the surgery.
In conclusion, the successful implementation of artificial intelligence and computer-assisted precision craniofacial surgery requires the establishment of a sound ecosystem involving healthcare institutions, surgeons, technology providers, patients, and insurance companies. Through collaborative cooperation, resource integration, and value co-creation, we can achieve more accurate, safer, and more effective craniofacial surgery, thereby improving the quality of life for patients and the sustainability.
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dc.description.provenanceSubmitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-10-03T17:22:15Z
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dc.description.provenanceMade available in DSpace on 2023-10-03T17:22:15Z (GMT). No. of bitstreams: 0en
dc.description.tableofcontents口試委員會審定書 ii
誌 謝 iii
中文摘要 iv
THESIS ABSTRACT vi
圖目錄 x
第一章 研究背景及方法 1
第一節、研究背景 1
第二節、研究設計 4
第三節、研究方法 5
第二章 電腦輔助於顱顏手術應用介紹 7
第一節、傳統正顎手術設計 7
第二節、電腦輔助之正顎手術設計 9
第三章 人工智慧於顱顏手術應用文獻分析 11
第一節、人工智慧歷史沿革 11
第二節、機器學習於顱顏面手術的應用 13
第三節、人工智慧,機器學習及深層學習於正顎手術的應用 15
第四節、ChatGPT 在顱顏面手術的應用 23
第四章 價值主張地圖及商業模式九宮格分析 24
第一節、價值主張 (Value Proposition) 24
第二節、商業模式九宮格 27
第三節、人工智慧及電腦輔助精準顱顏面手術之價值主張及商業模式 29
第五章 生態系建立探討 37
第一節、生態系簡介: 37
第二節、生態系於商業模式的應用 38
第三節、成功的商業生態系範例 40
第四節、探討人工智慧及電腦輔助精準顱顏手術商業生態系的建立 42
第六章 結論 53
參考文獻 55
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dc.language.isozh_TW-
dc.subject生態系zh_TW
dc.subject電腦輔助技術zh_TW
dc.subject商業模式zh_TW
dc.subject人工智慧zh_TW
dc.subject價值主張zh_TW
dc.subjectecosystemen
dc.subjectartificial intelligenceen
dc.subjectcomputer-assisted technologyen
dc.subjectbusiness modelen
dc.subjectvalue propositionen
dc.title人工智慧及電腦輔助精準顱顏面手術: 商業模式與生態系建立之探討zh_TW
dc.titleThe Application of Artificial Intelligence and Computer-Assisted Precision Craniofacial Surgery: The Potential of Business Model and Ecosystemen
dc.typeThesis-
dc.date.schoolyear111-2-
dc.description.degree碩士-
dc.contributor.coadvisor陳聿宏zh_TW
dc.contributor.coadvisorYu-Hung Chenen
dc.contributor.oralexamcommittee林世銘;洪茂蔚zh_TW
dc.contributor.oralexamcommitteeSu-Ming Lin;Mao-Wei Hungen
dc.subject.keyword人工智慧,電腦輔助技術,商業模式,價值主張,生態系,zh_TW
dc.subject.keywordartificial intelligence,computer-assisted technology,business model,value proposition,ecosystem,en
dc.relation.page56-
dc.identifier.doi10.6342/NTU202302355-
dc.rights.note同意授權(全球公開)-
dc.date.accepted2023-08-04-
dc.contributor.author-college管理學院-
dc.contributor.author-dept碩士在職專班國際企業管理組-
dc.date.embargo-lift2028-07-28-
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