The Gradual AI Revolution in Healthcare: What to Expect

AI is gradually revolutionizing healthcare, but its full potential remains untapped due to regulatory barriers, outdated systems, and trust issues among professionals.

Editorial Team (ET)May 8, 2025

Artificial intelligence (AI) has rapidly transformed many industries, from finance to manufacturing, but healthcare, despite its enormous potential, has been slower to embrace the full capabilities of AI. However, that is set to change as generative AI and machine learning technologies slowly find their place in this intricate, highly regulated industry. Healthcare, notorious for its slow adaptation to new technology, could be on the verge of a significant AI-driven disruption that has the potential to enhance patient care, optimize drug discovery, and streamline diagnostic processes.

The Current State of Healthcare and AI

Healthcare is a complex and heavily regulated sector, often burdened by legacy systems that make it difficult for new technologies to take root. As noted by Abby Yoder, a U.S. equity strategist at JPMorgan Private Bank, healthcare has underperformed seven of the last eight years in the stock market, even though it remains the only sector in the S&P 500 with consistent annual earnings growth over the past 21 years. The underperformance is largely due to the sector's slow integration of cutting-edge technologies like AI, which could help address inefficiencies in insurance approvals, claims management, and patient record-keeping.

Why AI Adoption in Healthcare Has Been Slow

Healthcare’s slow adoption of AI is a product of several challenges. First, the industry is encumbered by a complex web of players—hospitals, insurance companies, government regulators—all of whom have their own systems and protocols. This creates barriers for integrating new technologies. Second, the technological infrastructure within healthcare is outdated, particularly electronic medical records (EMRs) that were not designed for AI integration. These factors contribute to the slow rollout of AI, even as other sectors fully embrace its capabilities.

AI's Potential in Transforming Healthcare

Despite these challenges, AI holds incredible promise to revolutionize healthcare. AI can enhance the efficiency of numerous administrative tasks, such as reducing the time required for insurance approvals and streamlining manual record-keeping processes. More importantly, AI has the potential to empower healthcare professionals by providing them with advanced tools that improve diagnostic accuracy and patient outcomes.

AI is already making significant strides in diagnostic applications. For instance, AI-powered tools can assist in medical imaging, helping doctors identify patterns in complex data faster and more accurately. This doesn’t mean AI will replace human radiologists; instead, it serves as a tool to support and enhance their capabilities, allowing them to make faster, better-informed decisions in patient care.

AI and Drug Discovery

Perhaps one of the most exciting areas where AI could have a transformative impact is drug discovery. The process of developing new medications is both time-consuming and expensive, often taking years of research before a drug can be approved. However, AI has the potential to accelerate this process by analyzing large datasets to identify potential drug candidates faster than traditional methods. According to Morgan Stanley’s head of U.S. biopharma research, Terence Flynn, even a small improvement in preclinical development success rates—just 2.5%—could lead to over 30 new drug approvals within a decade, representing nearly $70 billion in economic impact.

Challenges to AI Adoption in Healthcare

Despite AI’s promise, there are significant challenges that must be overcome before the technology can be fully integrated into healthcare. Trust is one of the most significant barriers. According to a GE Healthcare survey of 7,500 clinicians worldwide, 55% of healthcare professionals believe AI is not yet ready for use in medical settings, and only 26% of U.S. clinicians trust AI, compared to 42% globally. With the high stakes involved in medical decision-making, healthcare professionals remain cautious about relying on AI for life-or-death decisions.

In addition to trust issues, technical challenges also stand in the way. Many hospitals still use outdated systems, such as EMRs, that are not designed to handle the complex algorithms AI relies on. Updating these systems will be essential for AI to fully deliver on its promise in healthcare.

AI in Surgery and Preventative Care

While fully autonomous surgeries may still be far off, AI is already playing a role in assisting with surgeries. AI-powered robots are used to assist surgeons in performing complex procedures with greater precision. These robots can help minimize human error, although human surgeons still control the operations. AI is also making waves in preventative care, particularly in the treatment of chronic conditions like diabetes.

For example, GLP-1 drugs, which are used to treat diabetes, have performed exceptionally well in the market. AI is helping model how these drugs might be applied to broader preventative care scenarios, particularly in reducing the risk of type 2 diabetes. Early data from clinical trials suggests that AI-driven treatments can prevent the progression to type 2 diabetes in over 98% of cases.

The Future of AI in Healthcare

The future of AI in healthcare is both promising and challenging. AI's potential to transform diagnostics, streamline drug discovery, and assist in surgeries could revolutionize the industry, but only if trust and infrastructure challenges are addressed. Healthcare professionals will need to be convinced of AI's reliability and safety before they fully adopt the technology, and outdated systems will need to be updated to accommodate the new tools.

In the long term, AI could offer a competitive edge for healthcare companies that are willing to invest in the technology. With AI-driven innovations, these companies could see improved patient outcomes, reduced costs, and faster drug development. As AI continues to evolve, its role in healthcare will only grow, offering new possibilities for how care is delivered and managed.

Conclusion

AI is gradually making its way into healthcare, bringing with it the promise of revolutionizing patient care, drug discovery, and diagnostics. However, the road to full adoption will be slow, hampered by regulatory challenges, outdated technology, and a cautious medical community. Despite these barriers, AI is poised to be a game-changer in healthcare, offering new ways to enhance the efficiency and effectiveness of medical care.

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