The Future of Enterprise Health AI: Key Trends and Predictions for 2025
Velatura Public Benefit Corporation
Aligning people and organizations with technology, ideas, and information to improve healthcare.
Prashant Natarajan | Chief AI Officer
Artificial Intelligence (AI) is gaining prominence in the everyday consumer space with applications in content creation, image and video generation, and even assisting with children's homework. In the Enterprise/B2B sector, AI is being utilized in various ways, such as LLM-powered agent assistants and chatbots, document intelligence/conversations using Retrieval Augmented Generation (RAG), as well as in sales and marketing. However, these applications only begin to explore the potential of enterprise AI.
In healthcare, AI offers numerous opportunities to create transformative experiences, enhance education, and empower individuals. From using computer vision for the timely detection of Traumatic Brain Injury to employing machine learning for census prediction and leveraging Generative AI for patient education and document understanding, AI is revolutionizing the healthcare landscape. These advancements enable healthcare professionals, including patients, caregivers, providers, researchers, and policymakers, to integrate technology into their roles more effectively.
What does 2025 look like for enterprise health AI? Here are 4 predictions based on the state of industry and state-of-art:
Interoperability AI
The widespread availability of advanced LLMs and fine-tuned healthcare operations and biomedical models are revolutionizing interoperability. These technologies enable the creation of new products, efficiencies, and value by leveraging diverse workflows and the extensive data managed by Health Information Exchanges (HIEs) and Trusted Data Sharing Organizations (TDSOs). Generative AI models facilitate RLHF-driven auto-mapping between systems, reconciliation of cross-industry standards, real-life data generation, and the development of self-service applications and agents for business and operational users.
Prediction for 2025: We will see a significant increase in the adoption of AI-driven interoperability solutions, leading to more seamless data exchange and integration across healthcare systems. This will enhance patient care coordination and reduce administrative burdens.
Multimodal AI
For far too long, we have been limited by the availability and expenses related to tech that brings together medical/health data for individuals and populations.? Multimodal AI refers to machine & deep learning models capable of processing and integrating information from multiple modalities or types of data, such as text, images, audio, and video. This approach allows for a more comprehensive understanding and robust outputs by combining different data sources to create unified insights, applications, and workflows. Multimodal AI applies data + AI fidelity at the point of need while reducing costs of data storage, transfer, and management. In healthcare, where privacy and patient safety are paramount, AI multimodal models across data in their original/native formats reduces the risks and exposure faced by covered entities and business associates today.
Prediction for 2025: Multimodal AI will become a standard practice in healthcare, enabling more accurate diagnostics, personalized treatment plans, and improved patient outcomes through the integration of diverse data sources.
IT Efficiencies
AI is significantly enhancing the development of tech products and services, maintenance of legacy applications, data migration, and coding. Tools like Cursor AI, GitHub Copilot, and AWS Q are assisting software development teams, leading to measurable cost and operational efficiencies. Encourage your IT teams to experiment with these AI tools and integrate them into production after expert review. Collaborate with your IT teams and vendors to fund, train, measure, and govern the use of AI in your code.
Prediction for 2025: AI-driven IT efficiencies will lead to faster development cycles, reduced costs, and more innovative healthcare solutions, allowing organizations to stay competitive and responsive to market demands.
Governance:
The convergence of data and AI governance is becoming increasingly important as AI applications proliferate in healthcare. Ensuring that Personally Identifiable Information (PII), Protected Health Information (PHI), and confidential data are used ethically, responsibly, and transparently by AI-ML models during training and inference is crucial. Establishing robust ML and LLM operations (MLOps) frameworks to streamline the deployment, monitoring, and maintenance of AI models, implementing comprehensive evaluation protocols to assess model performance, fairness, and bias are important. These measures will help identify and mitigate any issues promptly, ensuring models operate within ethical and regulatory boundaries.
Prediction for 2025: We will see the emergence of comprehensive AI governance frameworks, ensuring that healthcare organizations can leverage AI technologies while maintaining compliance with regulatory standards and protecting patient privacy. Advancements in AI governance will include enhanced transparency features and high-fidelity, personalized AI models that evolve with new needs & technologies.
In conclusion, the future of enterprise health AI in 2025 looks promising with advancements in interoperability, multimodal AI, IT efficiencies, and governance. These innovations will drive seamless data integration, enhance diagnostic accuracy, and streamline IT operations, ultimately improving patient care and outcomes. As AI technologies continue to evolve, robust governance frameworks will ensure ethical and responsible use, safeguarding patient privacy and compliance. The healthcare enterprise stands on the brink of a transformative era, empowered by AI-driven solutions.
AI is the buzz word. How will AI help clinicians who do not have access to the data? Until healthcare organizations and providers have access to and are incentivized to locate and review patients healthcare data, we will continue to have duplication of services and fragmented healthcare.
Manager, User Experience Lead @ H2O.ai | MSc, AI Design
1 个月Insightful article Prashanth
Prashant, great choice of initiatives for 2025. I’ll comment on the ones I’m familiar with. I totally agree with number 1. The key will be incorporating RLHF into the AI. The truth is that most integrations are ignored post go live besides the binary is it up or down. But AI partnered with RLHF can improve the integration exponentially. And that can be applied to new integrations (lowering the cost of one the costliest parts of healthcare IT) and existing integrations (increasing the value of the data especially for some of your other predictions). I look forward to this area both decreasing costs and improving the data. For number 2, I agree in theory but have to defer to your expertise. For number 3, yes. AI can assist with basic with coding. Some of the things you mentioned like GitHub CoPilot can assist with basic things. I look forward to additional advances. As we’ve had coding patterns and algorithms as a topic of conversation, these play into the AI spectrum as a way to take 1990’s code automation to a much higher realm. Higher than just completing coding syntax. Your number 4 is the most important. I’m running out of characters. Ethically. Trustworthy. With consent. Yes! Must be.
Principal at VOLUNTAS Consulting Services LLC specializing in AI/ML and genAI Model/Application Development and Deployments
1 个月Great foresight Prashant Natarajan. AI Agents are timely and positioned well to drive the progress on interoperability.
Seamless information exchange, improved diagnostics, operational efficiency, and governance, what a terrific article and well-informed predictions! Thanks for sharing this!