Empowering doctors with AI-powered healthcare

Of late, the fearmongering around artificial intelligence (AI) and how it can easily replace doctors has been very prominent. However, while AI has the potential to revolutionize healthcare, it cannot completely replace human doctors and the care they provide. This is mainly due to a lack of embodiment, limited understanding of empathy, and the inability to exercise judgment and clinical reasoning independently. 

To address the core mission of healthcare and provide optimal treatment and the right care to all patients, the industry needs to synergize human-AI collaborations. Luckily, the marketplace has a number of AI tools and capabilities. Take, for instance, the articulate medical intelligence explorer (AMIE), a research system based on a large language model (LLM) introduced by Google back in 2024, which has shown equal, even better diagnostic and conversational capabilities than most doctors. 

The need for AI in healthcare

The doctor shortage

Many regions, especially low- and middle-income countries, face a shortage of qualified doctors, which contributes to the widespread health inequities and avoidable deaths. Even in wealthier countries like the U.S., access to primary care is limited, and hospitals sometimes even stop accepting new patients because of the shortage of primary care physicians (PCPs). 

The data deluge

Hospitals generate a large amount of patient data annually, from electronic health records, diagnostics, wearables, genomics, imaging, sensors, and patient-reported outcomes. But, despite the advancement in technology, this data has not translated into better insights. The problem is not the lack of data, but the lack of intelligence extracted from it. 

Even the most experienced doctors find it exhausting to manually segregate information and apply it to patient care. Many physicians report being overwhelmed — the volume of clinical, diagnostic, imaging and patient-generated data makes it difficult to derive meaningful insights, let alone apply them to patient care.

The prevention gaps

Despite rising global health spending, the healthcare system remains focused on treating sickness rather than preventing it, largely due to misplaced priorities, misaligned incentives and scarcity of qualified doctors. 

Particularly in the U.S. and Europe, governments delay or skip preventive screening simply because of the lack of investment. Not to mention that with too few doctors, managing too many patients, preventive care comes secondary in favor of urgent, episodic treatment. Because the benefits of prevention may not be realized by those who pay for it, investments remain limited, making the prevention gap a structural issue. It reflects how healthcare systems are built and funded. 

These challenges combine to threaten the equity, quality, and sustainability of healthcare worldwide. In a world where half of the global population lacks essential health-service access, and even in well-developed nations, the U.S. struggles under surging data and demand, AI offers a way to deliver scalable, proactive and personalized care. By working together with top healthcare consulting firms, organizations can understand the shift AI brings to the landscape and the benefits they can reap. 

By continuously analyzing health data, predicting risks, nudging patients toward preventive actions, and freeing clinicians from repetitive tasks, AI helps shift care from late-stage, high-cost interventions to early, low-cost, high-impact prevention.

Barriers to the use of AI in healthcare

Even though AI holds promise, several obstacles, including insufficient resources and underdeveloped technological and data infrastructure, stand in the way of utilizing its full potential. 

Lack of trust

Many patients are still skeptical of AI-driven care, as compared to the care provided by human doctors. This mistrust is largely influenced by the doctor’s hesitation towards integrating AI in everyday care. Many physicians feel that existing AI tools lack transparency, clinical validation and clarity. Many others fear that AI adoption might reduce their autonomy, disrupt their tried and tested workflow and as I mentioned earlier, completely replace them. Healthcare is deeply personal, and when these fears are guiding providers, it becomes a significant reason to avoid AI, even when the said AI solutions are accurate, scalable and beneficial. 

Regulatory and reimbursement challenges

Regulatory ambiguity creates hesitation. The healthcare system is complex, and doctors sit at the center of this tightly woven network. And an innovation that challenges doctors’ authority can disrupt entrenched interests and is bound to face resistance from healthcare organization. But these innovations are often introduced slowly. 

Many AI tools do not fit the neatly laid out traditional regulatory framework without clear policies and organization’s hesitation to invest. Reimbursement is another barrier. The healthcare system across the world still pays for human-delivered services, not AI-enabled efficiencies. In the U.S., reimbursement for a healthcare service depends on the insurance company and whether they’re willing to pay for the given service. 

Whereas, in several other countries, doctors are overworked and lack the bandwidth to incorporate new tools in their practice. Healthcare providers also judge digital health and AI tools based on their perceived clinical and operational value as well as a desire to improve patient experiences. Without clear medical billing codes and reimbursement rules, providers have little financial incentive to adopt AI-based care. 

Asking providers to invest heavily in AI before foundational needs are met is unrealistic. But there are ways to prepare stakeholders for the gradual shift.

Solutions

Offer free, AI-based primary care 

One of the most powerful applications of AI lies in large-scale triage. Generative AI and large multimodal model (LMM), when integrated efficiently, can help screen patients, recommend basic care and recommend whether specialty care is needed or not, by understanding patient data, external medical knowledge and healthcare recommendations. By delivering first-mile care through AI, providers can reduce pressure on human doctors and ensure value-driven care is delivered to those who have limited access to it. 

Reform medical education by incorporating AI and machine learning

To make more doctors with AI, the industry needs to incorporate AI tools right from the start. Many medical schools around the world are only beginning to embed AI and machine learning (ML) in their curricula. The doctors of tomorrow should be comfortable using AI to predict the future course of disease based on medical imaging, discovering and diagnosing using these highly intelligent interfaces, much like today’s doctors who use modern diagnostic instruments.

Create an environment for safe and trustworthy AI

The industry is rife with AI tools, but without a uniform framework for validating these tools, it becomes difficult for doctors and patients to trust them. A trustworthy AI requires strong foundations, from representative data, transparent design, and mechanisms to ensure privacy and avoid bias. It’s important to rethink how these tools are delivered, financed, and regulated. Working with healthcare and AI consulting firms, organizations can understand the regulatory framework and define clear pathways for equipping doctors with AI. 

About Author

The author has worked with leading companies in the pharmaceutical and healthcare industries. She has cited multiple articles on AI and its impact on healthcare. More recently, her work has focused on understanding new AI technologies and how they’re changing care in the evolving healthcare ecosystem.