AI In Healthcare: Beyond Automation To Transformation

Current utilization and potential of AI highlight many opportunities, but there are many areas of knowledge gaps and risks. Personalized care requires treatment plans tailored to an individual’s genetic, environmental, and lifestyle factors. Agentic AI’s autonomous decision-making capabilities allow it to process vast patient datasets to identify unique health risks, predict disease progression, and recommend precise therapies. This approach enhances treatment effectiveness, patient satisfaction, and overall care quality.

Further, AI’s potential to redefine patient consent is highlighted through the introduction of intelligent consent management systems. Traditional consent mechanisms often fail to offer patients the flexibility they need to manage their data sharing preferences in a nuanced and accessible way. It showcases community-led data governance models from Canada, New Zealand and Australia, and urges governments to adopt legislation that empowers Indigenous Peoples to control and benefit from their data. Traditional, complementary and integrative medicine (TCIM) is practiced in 170 countries and is used by billions of people. The TCIM practices are increasingly popular globally, driven by a growing interest in holistic health approaches that emphasize prevention, health promotion and rehabilitation.

Agentic AI in healthcare refers to advanced autonomous artificial intelligence systems capable of making independent decisions and executing tasks without direct human intervention. Unlike traditional AI, which primarily delivers insights, agentic AI performs actions such as optimizing treatment plans, managing hospital workflows, conducting precision diagnostics, and enabling real-time clinical decisions. Its applications span personalized medicine, drug discovery, diagnostics, patient monitoring, and remote care management, addressing critical challenges like workforce shortages, operational inefficiencies, and escalating patient demands.

HIHI launches first AI healthcare competition

Sheahan says the large-scale data warehouses that power large language models are also enhancing the value of traditional predictive modeling. “Over the past year, MedStar Health’s AI governance has matured from a more exploratory, ad-hoc process into a structured and proactive system,” he said. In a HealthLeaders story last December, Sheahan described how the Maryland-based health system was taking a slow and steady approach to AI, with a particular focus on change management. The plan will build upon the region’s strong startup ecosystem and workforce development programs, and engage urban, suburban and rural communities — making the use-inspired solutions it develops easily replicable nationwide. Recognizing that not everyone can afford costly news subscriptions, we are dedicated to delivering meticulously researched, fact-checked news that remains freely accessible to all.

Personalized Medicine: Tailoring Treatment With AI

And as a Y Combinator alum, I am seeing more and more recent healthcare companies coming out of the recent Y Combinator batch using AI to transform all different aspects of the workflow in the healthcare space. The plan calls for harnessing AI to address critical health care challenges, such as chronic disease management, workforce shortages, rising costs, delivery inefficiencies, system fragmentation and more. A UB-led coalition’s plan to transform health care by tapping the transformative power of artificial intelligence has advanced to the semifinals of a major federal grant program.

UB and its partners are uniquely positioned to apply AI to the health care sector, Govindaraju said. This includes Empire AI, the statewide research consortium whose supercomputing infrastructure is housed at UB, as well as hundreds of UB researchers employing AI for the public good, many of whom are working in health-related fields. Launched under the Global Initiative on AI for Health, this brief offers a roadmap harnessing this potential responsibly while safeguarding cultural heritage and data sovereignty. Launched under the Global Initiative on AI for Health, this brief offers a roadmap harnessing this potential responsibly while safeguarding cultural heritage and data sovereignty. As trust in AI grows, most decision-makers are turning to established RCM providers like Waystar for scalable, secure integration. “We’re committed to pushing the boundaries of preventive care, improving both life expectancy and quality of life for people around the world,” added Vargas.

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The news was welcomed at UB, where officials say the plan will build upon the university’s expertise in AI and data science, as well as medical research and health care delivery, to improve patient outcomes and grow the region’s biomedical economy. For healthcare leaders eager to embrace AI, I recommend starting with a strategic, phased approach. Begin by identifying specific areas where AI can deliver immediate value, such as diagnostics or patient management, and launch pilot projects to test and refine these tools. It’s essential to build a multidisciplinary team that includes clinicians, data scientists and ethicists to ensure that AI solutions are both effective and ethically sound. By focusing on small, manageable projects, you can gradually scale AI implementation while minimizing disruption. Artificial Intelligence (AI) is rapidly transforming healthcare, evolving from a conceptual tool to a practical one with real-world applications.

“The advancement of our NSF Engines proposal is a recognition of Western New York’s dynamic innovation ecosystem. At the AI for Good Global Summit, the World Health Organization (WHO), the International Telecommunication Union (ITU), and the World Intellectual Property Organization (WIPO) released a new technical brief, Mapping the application of artificial intelligence in traditional medicine. AI’s integration into diagnostic tools enhances detection accuracy for conditions such as cancers, cardiovascular diseases, and neurological disorders. Agentic AI elevates this by not just identifying anomalies but autonomously recommending treatment pathways, scheduling follow-ups, and coordinating care teams, leading to improved clinical outcomes. According to Accenture, artificial intelligence could unlock an additional $461 billion in value across healthcare by 2035—on top of a sector already projected to surpass $2.26 trillion.

This combination creates a robust foundation for AI systems that can better predict patient needs and streamline clinical decisions. In a rapidly advancing digital healthcare landscape, artificial intelligence (AI) is playing a pivotal role in reshaping patient data management and enhancing patient autonomy. In an insightful article authored by Santosh Ratna Deepika Addagalla, the author delves into the transformative potential of AI-enhanced frameworks for patient data ownership and trust networks. These innovations offer new pathways for creating more secure, efficient, and patient-centered health ecosystems, especially when integrated with frameworks like TEFCA (Trusted Exchange Framework and Common Agreement) and FHIR (Fast Healthcare Interoperability Resources).

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The UB proposal features more than 50 partner organizations, including health care providers, industry, nonprofits, education and workforce development, government entities and business incubators. Safeguard traditional knowledge through AI-powered digital repositories and benefit-sharing models. The new document calls for urgent action to uphold Indigenous Data Sovereignty (IDSov) and ensure that AI development is guided by free, prior, and informed consent (FPIC) principles. It showcases community-led data governance models from Canada, New Zealand, and Australia, and urges governments to adopt legislation that empowers Indigenous Peoples to control and benefit from their data.

• “Exploration of EHR data is under way, utilizing internal tools to extract and code notes and radiology reports to drive workflows for incidental findings and quality.”
• HIHI’s AI in healthcare competition will select the best AI powered products or services aimed at improving healthcare.
• The UB proposal features more than 50 partner organizations, including health care providers, industry, nonprofits, education and workforce development, government entities and business incubators.
• TEFCA addresses the long-standing challenge of fragmented healthcare data by promoting a unified national data exchange network.

One of the core innovations explored in this article is the seamless integration of AI with interoperability frameworks like TEFCA and FHIR, which enable standardized health information exchange across disparate systems. TEFCA addresses the long-standing challenge of fragmented healthcare data by promoting a unified national data exchange network. It establishes clear governance and operational protocols for participating entities, helping them communicate effectively across multiple health information networks. Complementing TEFCA, FHIR provides the technical architecture that facilitates modern data exchange through a resource-oriented approach. By using standardized APIs, FHIR allows for granular data access, which is essential for AI to generate meaningful insights from diverse data sources.