醫療保健產生人工智慧的成長機會

藥物研發發現與開發、醫學影像分析、數位雙胞胎、合成資料、電子健康記錄管理和虛擬助理等領域的新應用

生成式人工智慧在醫療保健中的應用是一個快速成長的領域。近年來，藥物研發和開發、醫學影像分析、數位雙胞胎、合成資料、電子健康記錄管理和虛擬助理等新應用引起了人們的廣泛關注，因為它們有可能徹底改變這一領域。這為技術開發人員（包括軟體公司和醫療保健行業的新興企業）創造了一個充滿希望的機會，將這些新興技術置於其產品開發研發工作的中心。

本報告對新興應用的審查範圍是多方面的，包括技術、臨床決策支援、安全性和經濟等領域的考察。該研究的重點是確定整個醫療保健行業採用的傳統護理方法之外的新差距領域。

頂級軟體公司正在將自己定位為技術開發商，而生成式人工智慧新興企業則專注於小而重要的方面，以減輕臨床負擔並提供高效的臨床決策支援系統。由於這些公司及其合作，本研究深入探討了潛在的好處和結果，突出了競爭行業和相應相關人員的前景，並探討了未來五年藥物、診斷和治療計劃的未來性效益和結果，揭示了生成式人工智慧的演變。

本報告回答的問題

• 1.醫療保健領域生成式人工智慧的成長動力和抑制因素是什麼？
• 2.生成式人工智慧模型在藥物研發和開發、醫學影像分析、數位雙胞胎、合成資料、電子健康記錄管理和虛擬助理方面有哪些類型和優勢？
• 3.生成AI醫療產業主要企業有何動作？
• 4.業界主要的合作夥伴和資金籌措有哪些？
• 5.主要的生成式人工智慧法規有哪些？

目錄

戰略問題

• 為什麼成長如此困難？策略要務 8 (TM)：阻礙成長的因素
• 戰略要務8（略）
• 關鍵策略激勵措施對產生人工智慧產業的影響
• Growth Pipeline Engine（TM）：加速成長機會
• 調查方法

成長機會分析

• 生成式人工智慧的演進：過去與現在
• 生成式人工智慧透過多模式方法在醫療保健中發揮新作用
• 在醫療保健資料上實施多模式生成人工智慧
• 醫療保健產業的各個方面都採用了生成式人工智慧
• 生成式人工智慧在醫療保健中的應用：應用範圍
• 生成式人工智慧在醫療保健中的應用：細分
• 生長促進因子
• 成長抑制因素

生成式人工智慧在醫療保健中的技術應用簡介

• 用於藥物研發發現和藥物開發的生成式人工智慧
• 用於醫學影像分析的生成式人工智慧
• 用於合成資料和數位雙胞胎創建的生成式人工智慧
• 用於 EHR 和虛擬助理管理的生成式 AI
• 公司對醫療保健領域生成式人工智慧的見解：簡介
• 行業簡介
• 醫療保健中的生成式人工智慧：比較技術採用和技術成熟度

創新生態系統

• 生成式人工智慧與雲端運算基礎設施共同開發
• 製藥公司提供大量資金來開發基於人工智慧的藥物研發平台
• 醫院公司為醫療保健法學碩士的新興市場提供資金
• 醫療保健世代人工智慧組織的地理分佈
• 部分主要國家生成式人工智慧法規概況

充滿成長機會的世界

• 成長機會1：輕量級雲端即時實現生成式AI
• 成長機會 2：虛擬臨床試驗的綜合資料創建
• 成長機會 3：醫療互通性

附錄

• 技術成熟度等級 (TRL)：說明

下一步

Emerging Applications in Drug Discovery and Development, Medical Imaging Analysis, Digital Twins, Synthetic Data, Electronic Health Record Management, and Virtual Assistants

The application of Generative AI (Gen AI) in healthcare is a rapidly growing field. In recent years, emerging applications in drug discovery and development, medical imaging analysis, digital twins, synthetic data, and electronic health records management, and virtual assistants have garnered significant interest thanks to their potential to revolutionize the field. This creates a promising opportunity for technology developers-such as software companies and startups-in the healthcare industry to center their R&D activities on these emerging technologies for their product development.

This report's scope of analysis for emerging applications is multifaceted and involves considerations across technical, clinical decision support, safety, and economic domains. The primary focus of the study is to identify the emerging gap areas being adopted across the healthcare industry that will surpass conventional care methodologies.

With top-tier software companies positioning themselves as technology developers and Gen AI startups focusing on small but crucial aspects toward reducing the clinical burden and providing efficient clinical decision support systems and their collaborations, this study will delve into the potential benefits and outcomes, highlighting prospective and corresponding stakeholders in the competitive industry, and the evolution of generative AI across drugs, diagnostics, and treatment planning in the next five years.

Questions that this report answers:

• 1. What are the growth drivers and restraints of Gen AI in healthcare?

• 2. What are the types of models and benefits of Gen AI in drug discovery and development, medical imaging analysis, digital twins, synthetic data, electronic health record management, and virtual assistants?

• 3. What are the key companies to action in the Gen AI healthcare industry?

• 4. What are key collaborations and funding in the industry?

• 5. What are the key Gen AI regulations?

Table of Contents

Strategic Imperatives

• Why Is It Increasingly Difficult to Grow?The Strategic Imperative 8™: Factors Creating Pressure on Growth
• The Strategic Imperative 8™
• The Impact of the Top 3 Strategic Imperatives on the Generative Artificial Intelligence Industry
• Growth Opportunities Fuel the Growth Pipeline Engine™
• Research Methodology

Growth Opportunity Analysis

• The Evolution of Gen AI: Then and Now
• Emerging Roles of Gen AI in Medicine to Incorporate the Multimodal Approach
• Implementing Multimodal Generative AI on Healthcare Data
• Various Facets of the Healthcare Industry That Employ Gen AI
• Applications of Gen AI in Healthcare: Scope
• Applications of Gen AI in Healthcare: Segmentation
• Growth Drivers
• Growth Restraints

Tech Snapshot: Gen AI Applications in Healthcare

• Gen AI for Drug Discovery and Development
• Gen AI for Medical Image Analysis
• Gen AI for Synthetic Data and Digital Twin Creation
• Gen AI for Managing EHRs and Virtual Assistants
• Company Insights for Gen AI in Healthcare: A Snapshot
• Industry Snapshot
• Gen AI in Healthcare: Tech Adoption versus Tech Maturity

Innovation Ecosystem

• Collaboration for Developing Gen AI and Cloud Computing Infrastructure
• Pharmaceutical Companies Significantly Funding Gen AI-based Platform Development for Drug Discovery
• In-hospital Enterprises Significantly Funding the Emerging Market of Healthcare LLMs
• Geographical Distribution of Organizations in Healthcare Gen AI
• Overview of Gen AI Regulations in a Few Prominent Countries

Growth Opportunity Universe

• Growth Opportunity 1: Lighter Clouds to Implement Gen AI in Real Time
• Growth Opportunity 2: Synthetic Data Creation for Virtual Clinical Trials
• Growth Opportunity 3: Healthcare Interoperability Essential to Implement Generative AI

Appendix

• Technology Readiness Levels (TRL): Explanation

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