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市場調查報告書

通過使用商業 AI 模型進行輻射的增長機會

Growth Opportunities Arising due to the Use of Commercial AI Models in Radiology

出版商 Frost & Sullivan 商品編碼 1024351
出版日期 內容資訊 英文 35 Pages
商品交期: 最快1-2個工作天內
價格
通過使用商業 AI 模型進行輻射的增長機會 Growth Opportunities Arising due to the Use of Commercial AI Models in Radiology
出版日期: 2021年08月04日內容資訊: 英文 35 Pages
簡介

隨著人工智能 (AI) 解決方案被成像和醫療系統領域的高級領導者所接受,我們正在為希望在競爭加劇時增加競爭並保持增長的公司製定差異化戰略。我需要它。軟件即服務 (SaaS) 市場(主要基於雲)幫助醫學影像提供商簡化對來自各種獨立軟件供應商的應用程序的訪問,而無需單獨與每個供應商簽訂合同或接觸。我的目標是。

本報告研究和分析輻射 AI 市場,並提供有關市場概況、用例、定價策略和增長機會的系統信息。

目錄

戰略要求

市場概覽 - 招聘促進者和用例

  • 市場細分
  • 輻射人工智能的主要趨勢和演進
  • 多個進入者在 AI 開發生態系統和部署堆棧中的角色
  • 生長促進劑
  • 成像工作流程中輻射的 AI 用例
  • 成像工作流程中的 AI 用例或功能

部署、招聘標準、價值驅動因素

  • 部署類型、價值、進度
  • 有效性水平及其對採用 AI 進行成像的影響
  • 採用 AI 成像 - 主要最終用戶評估標準
  • AI 輻射型價值創造
  • 用於輻射殘疾和建議的人工智能

輻射價格策略的人工智能

  • AI for Radiation-Evolution/Advance of Price Model
  • 影響價格模式轉變的需求側因素
  • 定位各種價格模型的價值
  • 釋放輻射堆棧的價值並減少價格侵蝕

輻射的增長機會區-AI

  • 用於輻射增長機會矩陣的人工智能
  • 增長機會 1:在 OEM 平台和市場上銷售的針對特定疾病的包裝推動了採用
  • 增長機會 2:與 MedTech 和生物製藥公司的戰略合作夥伴關係成為新的收入來源
  • 增長機會 3:使推薦人和付款人受益的高利潤解決方案
  • 免責聲明
目錄
Product Code: K653-50

The Intensely Competitive Landscape Necessitates the Repositioning of Value Through the Acceptance of New Business Models

As artificial intelligence (AI) solutions gain acceptance in the imaging fraternity and among senior leaders in the health systems space, competition has intensified and necessitated the creation of differentiation strategies for companies that want to improve their revenue and sustain growth. The rapid proliferation of medical imaging AI companies has led to the availability of a plethora of solutions in the market; however, hospitals are not able to access these solutions (and vice versa). The launch of platforms and marketplaces by imaging original equipment manufacturers (OEMs) and others has paved the way for new distribution channels and commercial models. The software-as-a-service (SaaS) marketplace (mainly cloud-based) is meant to simplify medical imaging providers' access to various independent software vendors' applications without having to contract and engage individually with each vendor.

Owing to the shift from fee-for-service models to value-based reimbursement, intrinsic factors that drive adoption (improved sensitivity and specificity and reduced reporting and interpretation time) will become less important to end users. AI vendors should design their solutions and their pricing strategies to align with the value delivered in the overall care pathway. Predictive solutions and the identification of new protocol or indications for imaging will decrease readmissions and enable optimum resource utilization (equipment, manpower, and financial) and align with the goals of value-based care. As pressure on radiologists increases due to high scan volumes and complex cases, vendors should focus on condition-based packages that can be integrated with operational processes. Vendors can offer comprehensive condition-specific packages by partnering with OEM platforms or marketplace to decrease the cost of sale, improve market access, and benefit from new pricing models and integration facilities.

Research Highlights:

This Frost & Sullivan study examines the following:

  • Strategic imperatives for vendors
  • Key market trends and impact of AI on radiology
  • Scenarios driving the adoption of AI among providers
  • Types of value creation of AI in radiology
  • Adoption of AI and functional prioritization by radiologists
  • Evolution/progression of pricing models for AI tools
  • Demand-side factors that influence the shift in pricing models
  • Value positioning of various pricing models
  • Top growth opportunities

Table of Contents

Strategic Imperatives

  • Why Is It Increasingly Difficult to Grow?
  • The Strategic Imperative 8™
  • The Impact of the Top Three Strategic Imperatives on the Radiology AI Market
  • Growth Opportunities Fuel the Growth Pipeline Engine™

Market Overview-Drivers of Adoption and Use-cases

  • Market Segmentation
  • Key Trends and the Evolution of AI in Radiology
  • AI Development Ecosystem and the Role of Multiple Participants in the Deployment Stack
  • Growth Drivers
  • Radiology AI Use-Cases in Imaging Workflows
  • AI Use-Cases or Functionalities in Imaging Workflows

Deployment, Adoption Criteria, and Value Drivers

  • Types of Deployment, Value, and State of Play
  • Efficacy Levels and their Impact on the Adoption of AI in Imaging
  • AI Adoption in Imaging-Key Evaluation Criteria for End Users
  • AI in Radiology-Types of Value Creation
  • AI in Radiology-Roadblocks and Recommendations

AI in Radiology-Pricing Strategies

  • AI in Radiology-Evolution/Progression of Pricing Models
  • Demand-side Factors that Influence the Shift in Pricing Models
  • Value Positioning of Various Pricing Models
  • Unlocking Value across the Radiology Stack to Abate Price Erosion

Growth Opportunity Universe, AI in Radiology

  • AI in Radiology-Growth Opportunity Matrix
  • Growth Opportunity 1: Disease-specific Packages Sold Through OEM Platforms and Marketplaces Will Drive Adoption
  • Growth Opportunity 1: Disease-specific Packages Sold Through OEM Platforms and Marketplaces Will Drive Adoption (continued)
  • Growth Opportunity 2: Strategic Partnerships with MedTech and Biopharma Companies Will Create New Revenue Streams
  • Growth Opportunity 2: Strategic Partnerships with MedTech and Biopharma Companies Will Create New Revenue Streams (continued)
  • Growth Opportunity 3: Solutions that Benefit Referrers and Payers Will Yield High Revenue
  • Growth Opportunity 3: Solutions that Benefit Referrers and Payers Will Yield High Revenue (continued)
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