Artificial Intelligence (AI) in Healthcare Market Trends

Artificial Intelligence (AI) in Healthcare Market Drivers

Surging Healthcare Data Volume & the Growing Demand for AI-Driven Analytics Fueling Market Growth

• Healthcare data is expected to have an exponential growth curve - to over 10 zettabytes by 2025 - creating the demand for AI-enabled analytics. Traditional analytics will not be able to encompass the sheer volume and the complexity of data from EHR, medical imaging, genomics, wearables, and real-world evidence. AI will also enhance speed in diagnosis and predictive risk scores and personalized treatment decisions; an example is the Mayo Clinic and Google Health partnership that allows clinicians to improve early diagnosis of diseases. AI has also delivered 30% fewer hospital readmissions since it leverages predictive analytics. Digital health is seeing an increase of users forecast to top 1.3 billion by 2024 which will ultimately see an increase in data from AI-enabled wearables from Apple and Fitbit. AI will enable more efficiency in multiple ways, such as a reduced time to transition from the point of care to the billing process by 50%. AI will, in all, synthesize healthcare data into actionable insight that improves patient outcomes and the workflow of the healthcare operation.

Predictive Analytics for Disease Prevention​​

• AI-based predictive analytics is shifting the perspective on how we prevent diseases. AI is enabling us to detect the early stages of outbreaks for quicker intervention as well as distributing resources in preparation for possible spread, a great example is BlueDot which in its AI platform scanned global health reports, airlines, and social media to identify COVID-19 as a potential outbreak nine days before WHO announced it, it also predicted Zika and Ebola which describes just how strong AI can be in global health surveillance. The ability of AI models also allowed policy makers to respond to pandemics by providing the information necessary to make targeted lockdowns and more efficient use of provided resources for treatment. Real-time dashboards, like John's Hopkins COVID tracking tool, allow healthcare providers and policymakers to predict patient surges for staffing levels at hospitals. AI will reduce some of the time in drug and vaccine development processes like analyzing genomic data and predicting protein structures for directions in development and testing process. AI will be critical as more governments and health systems begin to understand the power of AI for early intervention and preparedness response to combating outbreaks and epidemics.

Artificial Intelligence (AI) in Healthcare Market Restraints

Data Privacy & Security Concerns

• While AI supports faster and better diagnosis and efficiencies in healthcare, they will have significant challenges with data privacy and security. AI consumes a myriad of patient health data, so the healthcare industry is a prime target for cyberattacks. Data breaches and ransomware grew by 94% from 2022-2023! Regulatory bodies (ex. HIPAA, GDPR) impose very complex compliance requirements, while approaches to anonymization are risky and susceptible to re-identification. It seems a significant issue is raised, on how evidence of adherence is verified across borders. To further labour our challenges, there exists a significant amount of chronic distrust in the system, leading to at least 57% of patients unwilling to share data due to fears of how it might be misused. The challenge moving forward appears clear, for use of stronger encryption, blockchain, federated learning approach, and what governance exists to clarify and provide ethical guidelines, AI privacy is critical understandings come to define the ability to innovate AI in healthcare.

Integration Challenges of AI in Healthcare Systems

• However, the obstacles associated with implementation are growing with AI application in healthcare. Whether it is interoperability with legacy EHR, deploying generations of electronic systems, data compatibility, or interoperability and data exchange barriers, the principal outcome is fragmentation: as an example, over 55% of hospitals have interoperability and data exchange barriers. Another issue is the high costs associated with deployment (hardware, cybersecurity, training), especially for hospitals with fewer resources, since hospitals run on budgets. There is also some reluctance related to the workforce in healthcare; 43% physicians have expressed some concern regarding reliability of the AI, validity of the algorithms, and continuity with workflows. Finally, incomplete data and duplicate data has a negative impact on AI performance. Interoperability standards (e.g., FHIR), staff training, and physicians' engagement must be recognized as critical priorities. These barriers and challenges will need to be eradicated if healthcare is to effectively utilize the advantages of AI to better and enhance patient care, and healthcare operations.