A radiologist receives a priority alert. An AI model has identified a subtle pulmonary embolism that would have otherwise gone undetected in the immediate review queue. In a neighboring department, a resident uses AI-guided ultrasound for real-time cardiac assessment at bedside. Meanwhile, intelligent automation handles patient intake, fielding inquiries and updating medical records seamlessly. Forty miles away, a mother monitors her high-risk pregnancy from the comfort of her living room using AI-powered tools guided by an OB-GYN at the hospital.
These are not a vision of the future but current examples of AI’s evolution from experimental technology to essential clinical infrastructure. And we are only at the beginning.
The AI Healthcare Landscape
Healthcare has experienced a significant surge in artificial intelligence adoption over the past five years, reshaping everything from diagnostic imaging and surgical precision to patient engagement and therapeutic discovery. AI now influences virtually every healthcare domain.
More than 4,500 AI-healthcare ventures currently operate across the United States, attracting $14 billion in investment during 2024 alone and accounting for 58 percent of total digital health funding. Academic research mirrors this momentum, with AI healthcare studies rising from 5,885 in 2020 to 28,180 in 2024.
AI’s transformative power is far from fully realized. As algorithmic sophistication improves and applications expand, we must shift from exploring what AI can do to maximizing its practical value. If the first chapter of AI in healthcare focused on invention, the next must focus on implementation.
Despite thousands of promising solutions and substantial investment, AI’s influence on daily clinical practice remains limited. Healthcare institutions are filled with pilot programs that rarely evolve into meaningful, sustainable change. Given constrained budgets and already stressed healthcare infrastructure, the promise of AI too often fails to become tangible progress.
From Development to Deployment
Realizing AI’s full potential requires healthcare leaders to rebalance priorities. The focus must move from pure innovation to strategic implementation. It is no longer enough to build better models. Healthcare systems must embed proven technologies where they can deliver the greatest clinical benefit. Around the world, this means shifting from developing and identifying the best solutions to planning in detail how to integrate them into real clinical workflows.
The first step is finding the right balance between human and artificial intelligence. Over the next decade, that balance will become clearer. Some tasks will routinely move to AI. Others will remain with clinicians. A strategic collaboration will emerge. AI will become another essential member of the care team, enabling better quality, greater efficiency, and more effective healthcare delivery.
Identifying tasks that no longer require human intelligence and assigning them to AI will maximize the capabilities of healthcare providers. Leading hospitals around the world are already demonstrating how well-integrated AI tools drive better outcomes while reducing workload and stress on clinical teams.
At Sheba and peer institutions, platforms such as Aidoc are transforming radiology with real-time image analysis and alerts on critical findings, accelerating life-saving interventions and improving patient outcomes. AISAP’s AI-guided cardiac ultrasound technology enables non-specialists to perform sophisticated bedside assessments with real-time guidance and automated measurements. This allows faster and more accurate diagnoses in demanding environments.
Healthcare systems are also adopting platforms such as Belong.Life, which uses specialized LLMs to streamline patient onboarding, deliver personalized education and support, and automate documentation. This frees clinicians to focus on the human dimensions of care. Remote monitoring solutions such as Nuvo’s pregnancy tracking tools allow patients to stay home while maintaining full clinical oversight.
The Decade of Human-AI Collaboration
These advancements show how AI can elevate care quality while optimizing clinicians’ time and attention. Every health organization needs to examine where AI can create synergy between human care teams and their digital counterparts.
Striking the right balance between human and artificial intelligence will be one of the central challenges of the coming decade. Health systems that master it early will lead. We can expect research to focus heavily on refining this collaborative dynamic. The success of that work will determine how effectively we can use AI to transform healthcare.
Hospital innovation centers will also need to evolve. Their mission is no longer to develop algorithms in isolation. It is to translate existing technologies into tools that can be deployed, adopted, and scaled. Organizations that take smart risks will see the benefits of AI earlier and will shape the transformation of modern medicine.
Prof Zimlichman is the Founder and Director of ARC, and Chief Transformation, Innovation, and AI Officer at Sheba Medical Center as well as the co-chairs of the Future of Health (FOH) community. He is a leader of global healthcare transformation, driving the integration of innovation, AI, and new care models across health systems worldwide.
