Artificial Intelligence in Medical Diagnostics Market by Component, Technology, Application, End-User

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[189 Pages Report] The Artificial Intelligence in Medical Diagnostics Market size was estimated at USD 1.00 billion in 2023 and expected to reach USD 1.16 billion in 2024, at a CAGR 17.23% to reach USD 3.04 billion by 2030.

Artificial intelligence (AI) in the medical diagnostics market encompasses the development, implementation, and application of AI-based technologies and systems to analyze clinical data, identify patterns, and derive insights for improved diagnostic accuracy and patient care. The increasing prevalence of chronic disease conditions has surged the need for enhanced imaging analysis in diagnostic applications. Rising government initiatives to promote the integration of AI/ML technologies in precision medicine and wearable devices have enhanced product development, significantly contributing to market growth. However, increasing incidences of product failures and the difficulty of AI integration with existing diagnostic systems may limit the market adoption of AI-enabled diagnostic solutions. Data privacy and security breach issues have emerged as concerning factors for market growth. Moreover, the introduction of diagnostic robotics and advanced AI technologies for medical diagnosis has created attractive opportunities for market growth. The advancing start-up ecosystem and expansion of smart hospitals are expected to leverage AI technology in medical diagnostics to bolster the growth of the market.

KEY MARKET STATISTICS
Base Year [2023]	USD 1.00 billion
Estimated Year [2024]	USD 1.16 billion
Forecast Year [2030]	USD 3.04 billion
CAGR (%)	17.23%

Component: Availability of a diverse range of software components to offer enhanced diagnostics decision

Hardware is a key component of AI in medical diagnostics which refers to physical devices such as embedded systems, sensors, and medical imaging devices necessitated for AI computation. Sensors and IoT devices are major hardware used to collect patient data and transmit it to AI systems for analysis. Hardware environments require hardware for data encryption, access control, and compliance with data protection regulations. Services include training, maintenance, installation, and customization of AI medical diagnostics, which offer tele-monitoring and tele-consultation using the technology. Telemonitoring includes remote diagnostics and continuous monitoring of the patient's health, particularly beneficial for chronically ill patients, elderly people, and individuals residing in remote areas. Tele-consultation democratizes access to expert medical consultation irrespective of geographical barriers and is predominantly useful for follow-ups, preliminary diagnoses, and rural healthcare. Software forms an integral part of AI in medical diagnostics, leveraging sophisticated algorithms, machine learning, and deep learning models to analyze complex medical data. It helps to interpret scans, identify anomalies, and predict patient prognosis and treatment responses. This software may include image analysis tools, diagnostics decision support systems, genome analysis software, and pathology & microscopy analysis, among others.

Technology: Extensive advancements in computer vision technologies for improved image analysis

Computer vision involves training artificial intelligence (AI) to interpret and understand the visual world. In medical diagnostics, this technology has revitalized procedures such as image-guided surgeries and automated reading of radiology reports. Computer vision is crucial in radiology and pathology, where large volumes of image data are interpreted. Machine Learning platforms enable computer systems to improve with experience, and they excel in predicting disease progression and diagnosing conditions at early stages. This technology is used in the diagnosis process and management of chronic diseases such as diabetes or heart disease, which require continuous monitoring and timely interventions. Natural language processing (NLP) allows AI to understand and interpret human language. It is effective in streamlining administrative tasks and extracting essential information from medical records for patient care. Robotic process automation (RPA) is leveraging software robots to automate routine tasks and is efficient in automating laboratory results, updating patient records, and booking appointments. RPA can automate the entire laboratory process in large-scale hospitals, eliminating errors and speeding up diagnoses.

Application: Adoption of AI in cardiology segment to enhance diagnostic accuracy

Artificial intelligence has shown promising results in cardiology, including the early detection and treatment of heart diseases. Using AI algorithms, medical professionals can predict a patient's risk of cardiac arrest, strokes, and heart disease based on their health records and cardiac images. It has also been successful in flagging anomalies in electrocardiogram (ECG) data, aiding doctors in diagnosing rhythmic heart disorders more accurately. Neurological disorders, often complex and difficult to diagnose, significantly benefit from AI's capacity to recognize patterns in voluminous data. AI is pivotal in the early detection of conditions such as Alzheimer's, Parkinson's, and multiple sclerosis by analyzing brain imaging scans and identifying minute changes that the human eye may overlook. Using pattern recognition, AI can identify abnormalities in radiology images that can indicate cancer, often catching early-stage tumors before they become more life-threatening. AI models can also be utilized to formulate personalized treatment plans based on individual cancer genetic makeup. AI has revolutionized pathology by speeding up disease diagnostics with the surge of computational pathology, as AI-driven algorithms can instantaneously analyze tissue samples to detect abnormalities, diseases, and infections. Utilizing deep learning techniques, AI can evaluate medical images such as X-rays, CT scans, and MRI scans to detect signs of diseases, including pneumonia, brain tumors, and fractures. In pulmonology, AI is used to predict and manage chronic conditions such as asthma and COPD, and it helps with the early detection of lung cancer via the analysis of CT scans and interpretation of pulmonary function tests. Ophthalmology uses AI algorithms for diagnosing various eye diseases. Deep learning models can analyze retinal photos to detect diabetic retinopathy in its early stages, significantly reducing the risk of blindness.

End-User: Utilization of AI for large data set diagnosis in hospitals and clinics

Within academic institutions and research centers, AI is a focal point of exploration and innovation. Scientists and researchers leverage AI to devise new methodologies for early disease detection, facilitating faster and more efficient diagnosis and, in turn, enabling timely intervention. In diagnostic centers, AI is revolutionizing patient care with machine learning models and image recognition software, enabling enhanced diagnostic imaging. AI algorithms can analyze MRI scans, X-rays, and CT scans to detect and classify anomalies; this includes even minor abnormalities that can often escape unaided human interpretation. These tools facilitate more accurate diagnoses and reduce the scope of manual errors, supporting the timely beginning of an appropriate course of treatment. Hospitals, integral parts of the frontline healthcare system, are witnessing an impactful integration of AI in various capacities. Predominantly, it assists physicians in disease diagnosis by analyzing patient data and presenting key insights to the physician in real-time. By adopting AI-powered tools, hospitals can improve upon traditional patient care models, expedite the diagnosis process, and ultimately deliver improved treatment outcomes.

Regional Insights

Significant investments in AI for healthcare in the United States have led to groundbreaking research and advancements in medical diagnostics. Major countries such as the United States and Canada have the strong presence of key players equipped with technological capabilities to revolutionize medical diagnostics services through artificial intelligence integration. Europe has witnessed the emergence of several startups and established companies producing AI-driven solutions for research & development in medical diagnostics. Ongoing collaboration activities between governments, researchers, and industry players across the EMEA region are playing a crucial role to drive innovation market growth in the medical diagnostics sector. Significant countries in the APAC region, including China, Japan, and India, support regional players to leverage their expertise in robotics and advanced technologies to develop AI-driven diagnostic tools. Artificial Intelligence (AI) in medical diagnostics in the APAC has witnessed significant growth owing to its large population base, evolving healthcare infrastructure, and increasing adoption of advanced technologies.

FPNV Positioning Matrix

The FPNV Positioning Matrix is pivotal in evaluating the Artificial Intelligence in Medical Diagnostics Market. It offers a comprehensive assessment of vendors, examining key metrics related to Business Strategy and Product Satisfaction. This in-depth analysis empowers users to make well-informed decisions aligned with their requirements. Based on the evaluation, the vendors are then categorized into four distinct quadrants representing varying levels of success: Forefront (F), Pathfinder (P), Niche (N), or Vital (V).

Market Share Analysis

The Market Share Analysis is a comprehensive tool that provides an insightful and in-depth examination of the current state of vendors in the Artificial Intelligence in Medical Diagnostics Market. By meticulously comparing and analyzing vendor contributions in terms of overall revenue, customer base, and other key metrics, we can offer companies a greater understanding of their performance and the challenges they face when competing for market share. Additionally, this analysis provides valuable insights into the competitive nature of the sector, including factors such as accumulation, fragmentation dominance, and amalgamation traits observed over the base year period studied. With this expanded level of detail, vendors can make more informed decisions and devise effective strategies to gain a competitive edge in the market.

Key Company Profiles

The report delves into recent significant developments in the Artificial Intelligence in Medical Diagnostics Market, highlighting leading vendors and their innovative profiles. These include 3M Company, AiCure, LLC, Aidoc Medical Ltd., Butterfly Network, Inc., Cera Care Limited, Cisco Systems, Inc., Corti - AI, Digital Diagnostics Inc., Edifecs, Inc., Enlitic, Inc., Epredia by PHC Holdings Corporation, Freenome Holdings, Inc., GE HealthCare Technologies, Inc., General Vision, Inc., Google LLC by Alphabet Inc., Hewlett Packard Enterprise Development LP, Imagen Technologies, Inc., Intel Corporation, International Business Machines Corporation, Johnson & Johnson Services, Inc., Kantify, Koninklijke Philips N.V., Medtronic PLC, Microsoft Corporation, Nano-X Imaging Ltd., NEC Corporation, NVIDIA Corporation, Persistent Systems Limited, Qure.ai Technologies Private limited, Siemens Healthineers AG, SigTuple Technologies Private Limited, Stryker Corporation, Tempus Labs, Inc., and VUNO Inc..

Market Segmentation & Coverage

This research report categorizes the Artificial Intelligence in Medical Diagnostics Market to forecast the revenues and analyze trends in each of the following sub-markets:

Component
- Hardware
- Services
  - Tele Monitoring
  - Tele-Consultation

Software

Technology
- Computer Vision
- Machine Learning Platforms
- Natural Language Processing
- Robotic Processes Automation

Application
- Cardiology
- Neurology
- Oncology
- Ophthalmology
- Pathology
- Pulmonology

End-User
- Academic Institutions & Research Center
- Diagnostic Center
- Hospitals

Region
- Americas
  - Argentina
  - Brazil
  - Canada
  - Mexico
  - United States
    - California
    - Florida
    - Illinois
    - New York
    - Ohio
    - Pennsylvania
    - Texas
- Asia-Pacific
  - Australia
  - China
  - India
  - Indonesia
  - Japan
  - Malaysia
  - Philippines
  - Singapore
  - South Korea
  - Taiwan
  - Thailand
  - Vietnam

Europe, Middle East & Africa
- Denmark
- Egypt
- Finland
- France
- Germany
- Israel
- Italy
- Netherlands
- Nigeria
- Norway
- Poland
- Qatar
- Russia
- Saudi Arabia
- South Africa
- Spain
- Sweden
- Switzerland
- Turkey
- United Arab Emirates
- United Kingdom

The report offers valuable insights on the following aspects:

1. Market Penetration: It presents comprehensive information on the market provided by key players.

2. Market Development: It delves deep into lucrative emerging markets and analyzes the penetration across mature market segments.

3. Market Diversification: It provides detailed information on new product launches, untapped geographic regions, recent developments, and investments.

4. Competitive Assessment & Intelligence: It conducts an exhaustive assessment of market shares, strategies, products, certifications, regulatory approvals, patent landscape, and manufacturing capabilities of the leading players.

5. Product Development & Innovation: It offers intelligent insights on future technologies, R&D activities, and breakthrough product developments.

The report addresses key questions such as:

1. What is the market size and forecast of the Artificial Intelligence in Medical Diagnostics Market?

2. Which products, segments, applications, and areas should one consider investing in over the forecast period in the Artificial Intelligence in Medical Diagnostics Market?

3. What are the technology trends and regulatory frameworks in the Artificial Intelligence in Medical Diagnostics Market?

4. What is the market share of the leading vendors in the Artificial Intelligence in Medical Diagnostics Market?

5. Which modes and strategic moves are suitable for entering the Artificial Intelligence in Medical Diagnostics Market?

Product Code: MRR-43492DACC312

1. Preface

1.1. Objectives of the Study
1.2. Market Segmentation & Coverage
1.3. Years Considered for the Study
1.4. Currency & Pricing
1.5. Language
1.6. Stakeholders

2. Research Methodology

2.1. Define: Research Objective
2.2. Determine: Research Design
2.3. Prepare: Research Instrument
2.4. Collect: Data Source
2.5. Analyze: Data Interpretation
2.6. Formulate: Data Verification
2.7. Publish: Research Report
2.8. Repeat: Report Update

3. Executive Summary

4. Market Overview

5. Market Insights

5.1. Market Dynamics
- 5.1.1. Drivers
  - 5.1.1.1. Prevalence of chronic diseases and need for enhanced imaging analysis
  - 5.1.1.2. Government initiatives to promote the integration of AI/ML technologies in healthcare
  - 5.1.1.3. Adoption of AI technologies in precision medicine and wearable devices
- 5.1.2. Restraints
  - 5.1.2.1. Chances of failures and difficulty of integration with existing systems
- 5.1.3. Opportunities
  - 5.1.3.1. Introduction of diagnostic robotics and advanced AI technologies for medical diagnosis
  - 5.1.3.2. Advancing start-up ecosystem and expansion of smart hospitals
- 5.1.4. Challenges
  - 5.1.4.1. Concerns regarding data privacy and security breaches
5.2. Market Segmentation Analysis
- 5.2.1. Component: Availability of a diverse range of software components to offer enhanced diagnostics decision
- 5.2.2. Technology: Extensive advancements in computer vision technologies for improved image analysis
- 5.2.3. Application: Adoption of AI in cardiology segment to enhance diagnostic accuracy
- 5.2.4. End-User: Utilization of AI for large data set diagnosis in hospitals and clinics
5.3. Market Trend Analysis
- 5.3.1. Increasing adoption of AI-based medical diagnosis tools in the Americas region due to supportive government policies and strong market presence
- 5.3.2. Favorable government initiatives and evolving healthcare infrastructure show immense opportunity for AI in healthcare diagnostics in APAC
- 5.3.3. Strong public-private investment scenario for AI in medical diagnostics across EMEA, backed by substantial R&D activities by emerging and established startups in the region
5.4. Cumulative Impact of High Inflation
5.5. Porter's Five Forces Analysis
- 5.5.1. Threat of New Entrants
- 5.5.2. Threat of Substitutes
- 5.5.3. Bargaining Power of Customers
- 5.5.4. Bargaining Power of Suppliers
- 5.5.5. Industry Rivalry
5.6. Value Chain & Critical Path Analysis
5.7. Regulatory Framework Analysis

6. Artificial Intelligence in Medical Diagnostics Market, by Component

6.1. Introduction
6.2. Hardware
6.3. Services
6.4. Software

7. Artificial Intelligence in Medical Diagnostics Market, by Technology

7.1. Introduction
7.2. Computer Vision
7.3. Machine Learning Platforms
7.4. Natural Language Processing
7.5. Robotic Processes Automation

8. Artificial Intelligence in Medical Diagnostics Market, by Application

8.1. Introduction
8.2. Cardiology
8.3. Neurology
8.4. Oncology
8.5. Ophthalmology
8.6. Pathology
8.7. Pulmonology

9. Artificial Intelligence in Medical Diagnostics Market, by End-User

9.1. Introduction
9.2. Academic Institutions & Research Center
9.3. Diagnostic Center
9.4. Hospitals

10. Americas Artificial Intelligence in Medical Diagnostics Market

10.1. Introduction
10.2. Argentina
10.3. Brazil
10.4. Canada
10.5. Mexico
10.6. United States

11. Asia-Pacific Artificial Intelligence in Medical Diagnostics Market

11.1. Introduction
11.2. Australia
11.3. China
11.4. India
11.5. Indonesia
11.6. Japan
11.7. Malaysia
11.8. Philippines
11.9. Singapore
11.10. South Korea
11.11. Taiwan
11.12. Thailand
11.13. Vietnam

12. Europe, Middle East & Africa Artificial Intelligence in Medical Diagnostics Market

12.1. Introduction
12.2. Denmark
12.3. Egypt
12.4. Finland
12.5. France
12.6. Germany
12.7. Israel
12.8. Italy
12.9. Netherlands
12.10. Nigeria
12.11. Norway
12.12. Poland
12.13. Qatar
12.14. Russia
12.15. Saudi Arabia
12.16. South Africa
12.17. Spain
12.18. Sweden
12.19. Switzerland
12.20. Turkey
12.21. United Arab Emirates
12.22. United Kingdom

13. Competitive Landscape

13.1. Market Share Analysis, 2023
13.2. FPNV Positioning Matrix, 2023
13.3. Competitive Scenario Analysis
- 13.3.1. XtalPi and CK Extend Partnership to Develop AI Cancer Diagnostic Models
- 13.3.2. VUNO Secures FDA 510(k) Clearance for VUNO Med-DeepBrain
- 13.3.3. Aidoc Expands its Partnership with GLEAMER to Enhance its Chest Imaging AI Suite
- 13.3.4. Enlitic and Blackford Join Forces to Revolutionize Medical Imaging Data Standardization
- 13.3.5. GE HealthCare Signs USD 44 Million Contract with BARDA to Develop Artificial Intelligence-Augmented Ultrasound Technology to Aid Clinicians in Diagnosing and Treating Traumatic Injury
- 13.3.6. Nanox Announces Strategic Manufacturing Agreement with Varex Imaging Corporation to Advance the Nanox.ARC Medical Imaging System
- 13.3.7. Corti's AI-Powered Healthcare Solution Attracts USD 60 Million Investment
- 13.3.8. Aidoc Launches AI-based Full Brain Solution for Stroke Detection
- 13.3.9. Butterfly Network Announces New FDA-Cleared AI-Enabled Lung Tool
- 13.3.10. SigTuple Raises USD 4.3 Million in Series C Round
- 13.3.11. GE HealthCare to Acquire Caption Health, Expanding Ultrasound to Support New Users Through FDA-Cleared, AI-Powered Image Guidance
- 13.3.12. Microsoft Strikes Partnership with Paige to Advance Cancer Diagnostics and Treatment

14. Competitive Portfolio

14.1. Key Company Profiles
14.2. Key Product Portfolio