Artificial Intelligence (AI) In Predictive Healthcare Analytics Market Size, Share, Opportunities, And Trends By Deployment Mode (Cloud-Based, On-Premise), By Application (Patient Risk Stratification, Disease Diagnosis And Prognosis, Population Health Management, Fraud Detection, Supply, Chain Management, Others), By End-User (Hospitals And Clinics, Healthcare Payers, Pharmaceutical And Biotechnology Companies, Research Institutes And Academic Centers, Others), And By Geography - Forecasts From 2023 To 2028

The AI in predictive healthcare analytics market is estimated to grow at a CAGR of 41.76% during the forecast period.

The AI in predictive healthcare analytics market is a transformational and exciting topic within the healthcare industry. Predictive healthcare analytics uses the power of AI and data analytics to estimate future health outcomes, detect possible dangers, and optimize patient treatment. AI algorithms can forecast disease development, patient readmissions, and treatment responses by analysing massive amounts of patient data, including electronic health records, genetics, and lifestyle variables. This predictive skill allows healthcare practitioners to intervene and personalize therapies proactively, improving patient outcomes and lowering healthcare expenditures. The AI in predictive healthcare analytics market has enormous potential to transform healthcare decision-making, improve preventative measures, and usher in a new age of precision medicine.

Advancements in Artificial Intelligence (AI) Technologies in the AI in Predictive Healthcare Analytics Market.

Artificial Intelligence (AI) technological advancements have been a prominent development factor in the AI in predictive healthcare analytics market. AI has advanced rapidly, including machine learning, natural language processing, and computer vision, among other things. These developments allow AI systems to analyse large and complicated healthcare datasets such as patient records, genetic data, and medical imaging. Artificial intelligence (AI) can forecast illness outcomes, detect possible dangers, and offer personalised treatment regimens, revolutionising healthcare decision-making. AI-powered prediction models' improving accuracy and efficiency have boosted their acceptance by healthcare providers and academics. As artificial intelligence (AI) technologies advance, they have the potential to revolutionise healthcare analytics, enhance patient outcomes, and pave the way for more proactive and data-driven healthcare practices.

Growing Emphasis on Personalized and Precision Medicine Enhances the AI in Predictive Healthcare Analytics Market Growth.

The growing emphasis on personalised and precision medicine has emerged as a key driver in the AI in predictive healthcare analytics market. Individual genetic and behavioural variables that impact individual health outcomes are increasingly being recognised by healthcare practitioners. Predictive models may analyse patient-specific data, such as genomes, medical history, and lifestyle decisions, thanks to developments in technology and data analytics. This transition away from a one-size-fits-all approach towards personalized and precision medicine enables more tailored medicines, better disease prevention, and better patient outcomes. In this setting, using predictive analytics is critical for optimizing treatment regimens and providing patient-centred care.

Demand for Efficient Patient Risk Stratification and Disease Prediction in the AI in Predictive Healthcare Analytics Market.

The desire for accurate patient risk assessment and illness prediction is a key driver of growth in the AI in predictive healthcare analytics market. Healthcare practitioners are looking for techniques to identify high-risk patients and forecast illness development early on in order to deliver timely therapies and avert negative consequences. AI and data analytics are used in predictive healthcare analytics to analyse large patient datasets, detecting patterns and risk factors related to certain diseases. Healthcare professionals may adjust treatment approaches, manage resources more effectively, and improve patient care by precisely predicting disease trajectories and outcomes. The capacity to address health hazards proactively improves patient safety, lowers healthcare costs, and accelerates the adoption of predictive analytics solutions in the healthcare business.

North America is the Market Leader in the AI in Predictive Healthcare Analytics Market.

North America was regarded as the market leader in AI in the predictive healthcare analytics market. The United States has been at the forefront of adopting and implementing artificial intelligence (AI) technology in healthcare analytics and predictive modelling. North America's position as a leader may be due to its well-established healthcare infrastructure, significant expenditures in AI research and development, and the presence of prominent healthcare technology businesses. Furthermore, the region's emphasis on precision medicine and patient-centric care has increased demand for AI-powered predictive analytics solutions to improve patient outcomes, optimise resource allocation, and improve overall healthcare efficiency. North America is projected to maintain its dominant position in the AI in predictive healthcare analytics market as AI technologies advance.

Focus on Preventive Healthcare and Population Health Management in the AI in Predictive Healthcare Analytics Market Size.

A prominent driver in the AI in predictive healthcare analytics market is the increased emphasis on preventative healthcare and population health management. Healthcare systems throughout the world are rapidly moving their emphasis away from reactive treatment methods and towards proactive preventative strategies. Predictive healthcare analytics is critical for detecting health hazards, forecasting illness patterns, and determining population health requirements. AI-powered prediction models may identify at-risk groups, focus interventions, and optimise resource allocation for preventive care by analysing complete patient data and demographic information. Preventive measures not only improve health outcomes but also cut healthcare costs and promote population well-being, making predictive analytics a critical component in current healthcare practices.

Key Developments:

• In October 2022, Enlitic, a prominent healthcare IT firm, announced a new collaboration with MULTI Inc., a provider of healthcare technology, genuine parts, equipment, and services. The two firms will collaborate to provide the Enlitic Curie platform to healthcare providers across the United States to assist radiology departments in driving operational efficiencies.
• In June 2023, Health Catalyst, Inc., a leading provider of data and analytics technology and services to healthcare organisations, announced an expanded partnership with the Ohio Health Information Partnership (The Partnership), which operates CliniSync, a nonprofit Health Information Exchange that facilitates the sharing of patient records across Ohio. 
• In October 2023, Innovations MUUTAA Inc., an emerging creator of AI deep learning solutions for healthcare supply chains focused on patient-driven demand, announced partnering up with Mila-Quebec AI Institute, which is the world’s largest academic Deep Learning (DL) research center.

Company Products:

• HCC Profiler: The Apixio HCC Profiler is an AI-powered system that analyses clinical documentation and claims data to reliably identify Hierarchical Condition Category (HCC) codes. Risk adjustment and establishing Medicare Advantage payment rates need HCC coding.
• Ayasdi Clinical Variation Management: The AI platform developed by Ayasdi supports healthcare practitioners in recognising and controlling clinical variance among patient groups. To standardise care and enhance patient outcomes, the platform employs AI algorithms to analyse patient data, treatment outcomes, and best practices.
• SAS Healthcare Analytics: SAS offers a complete healthcare analytics platform that incorporates AI and machine learning. The platform allows healthcare organisations to analyse massive amounts of data from electronic health records, claims data, and other sources in order to acquire insights about patient demographics, treatment trends, and healthcare outcomes.
• Watson for Oncology: To give personalised therapy recommendations for cancer patients, IBM Watson Health's AI-driven platform analyses huge quantities of medical literature, clinical trial data, and patient information. Oncologists may use the platform to make evidence-based decisions and optimise precision oncology therapy.

Segmentation:

• By Deployment Mode
  • Cloud-Based
  • On-Premise      
• By Application
  • Patient Risk Stratification
  • Disease Diagnosis And Prognosis
  • Population Health Management
  • Fraud Detection
  • Supply Chain Management
  • Others    
• By End-User
  • Hospitals And Clinics
  • Healthcare Payers
  • Pharmaceutical And Biotechnology Companies
  • Research Institutes And Academic Centers
  • Others                
•  By Geography
  • North America
    • United States
    • Canada
    • Mexico
  • South America
    • Brazil
    • Argentina
    • Others
  • Europe
    • United Kingdom
    • Germany
    • France
    • Italy
    • Spain    
    • Others
  • Middle East and Africa
    • Saudi Arabia
    • UAE
    • Others
  • Asia Pacific
    • Japan
    • China
    • India
    • South Korea
    • Indonesia
    • Taiwan
    • Others