PCPs, nurses, nurse practitioners, physician assistants, community health workers – these are the people on the frontlines of the health system tasked with diagnosing and managing a vast array of conditions, often acting as the first point of contact for patients seeking medical attention. However, the sheer breadth of their responsibilities presents a challenge: no single clinician can be an expert in every field of medicine. However, no single clinician can be an expert in every field of medicine.
The result is a system where some conditions go undiagnosed or are referred too late, leading to poorer outcomes for patients and increased strain on both PCPs and specialists.
Take, for example, a seemingly simple but crucial skill: differentiating between various lung sounds. While pulmonologists and respiratory specialists are well-trained to detect and interpret these sounds, many clinicians – particularly those who are often interacting with patients at the earliest signs of respiratory distress – may not have the experience or expertise to do so reliably, especially when wheezing and other lung sounds are hard to detect or are sporadic. This diagnostic gap means that conditions like asthma, chronic obstructive pulmonary disease (COPD), or early-stage respiratory infections might go unnoticed or misdiagnosed, delaying treatment.
Another example is the assessment of a wound’s healing progression. Wound care specialists, with their specialized training, can identify patterns that require further care, but a clinician may err on the side of caution or miss critical signs of color or size, leading to delays in care.
Similarly, heart conditions often present diagnostic challenges in primary care. A PCP conducting a routine examination may not be able to discern the early signs of atrial fibrillation (AFib) and other subtle changes in an electrocardiogram (ECG) that indicate underlying cardiovascular issues.
AI as a game-changer in primary care
Artificial intelligence (AI) has the potential to bring specialist-level expertise directly into primary care by bringing specialist-level expertise directly into the primary care setting as a tool for clinicians, providing expert “second opinions” at hand and empowering the clinicians. AI models, trained by specialists in various fields, can assist clinicians by providing real-time analysis and diagnostic suggestions. These models can analyze patient data, listen for abnormal lung sounds, interpret medical imaging, and even flag high-risk symptoms that warrant further investigation. For instance, AI-powered wound analysis tools can improve assessments, and software can analyze heart rhythms in real-time, alerting clinicians to potential concerns before they become serious.
By embedding AI into primary care workflows, clinicians can validate their decisions and make more accurate and timely diagnoses, ensuring that patients receive the care they need without unnecessary delays. It serves as an always-available second opinion, reducing uncertainty and boosting confidence in diagnostic decisions.
Making clinicians’ lives easier and better
The goal of integrating AI into primary care is not just to improve patient outcomes but also to reduce clinician workload. As patient loads and administrative demands grow, AI can automate routine tasks, assist with documentation, and provide quick insights. Medical practitioners are often overworked, facing increasing patient loads and administrative demands. According to the Association of American Medical Colleges (AAMC), the nation could experience a shortage of up to 86,000 physicians by 2036. AI can streamline their work by automating routine tasks, assisting with documentation, and providing quick insights that otherwise might take hours of consultation or referrals. Using AI will essentially strengthen physicians’ capabilities.
Furthermore, AI can help bridge the gap in healthcare accessibility. In rural or underserved areas where specialist access is limited, AI-enabled diagnostic tools can empower clinicians to make more informed decisions, reducing the need for unnecessary referrals and ensuring that only the most complex cases reach specialists. Additionally, AI-driven solutions significantly reduce patient travel time: patients travel up to three hours each way to attend to a specialist. Clinicians using AI-assisted specialist diagnostics can allow individuals to receive timely and effective care without the burden of long-distance commutes.
AI in virtual care to relieve pressure on clinicians
While AI has the potential to enable PCPs to practice elements of specialty care, empower clinician decision-making, and enhance diagnostic accuracy, adoption remains a challenge due to the overwhelming number of AI tools and digital health fatigue. Successful adoption will require not only seamless technological integration into PCP workflows but also a fundamental shift in mindset. AI is more accessible than ever — often easier to reach than a doctor — but that does not mean widespread trust and acceptance will happen overnight. Building confidence in AI tools demands education and a gradual behavioral shift. Clinicians must view AI as a collaborator, not a disruption, while patients must recognize its value in improving care. The transition will require thoughtful implementation, ongoing engagement, and a cultural shift within healthcare to fully embrace AI’s role in the future of medicine.
Coupling AI with virtual care may extend diagnostic capabilities remotely. For example, AI can analyze ECG readings, skin images, or respiratory sounds captured via remote AI tools and provide real-time insights based on specialist-trained models. This ensures that clinicians can offer precise evaluations without requiring immediate specialist input, better identify when specialists need to be in the loop, and provide a platform for clinicians to connect with patients more meaningfully and impactfully without requiring increased time or energy. By embedding AI into virtual care, clinicians can extend their diagnostic capabilities, validate their decisions, reduce unnecessary referrals, and provide high-quality specialist-informed care, all while reinforcing their abilities.
The future of AI-assisted primary care
AI has already begun making strides in medical imaging, dermatology, and cardiology, with algorithms capable of detecting conditions as accurately as human specialists. As these technologies continue to evolve, they must be integrated into everyday primary care practice.
Collaboration among policymakers, healthcare institutions, and developers is essential to ensure AI tools are user-friendly and widely accessible.
Ultimately, AI is empowering, not replacing, doctors. By leveraging AI’s capabilities, we can bring specialist expertise into every primary care clinic, making diagnoses more precise, treatment more timely, and the lives of both clinicians and patients significantly better.
Image: Thai Noipho, Getty Images
Dedi Gilad is CEO and co-founder of TytoCare, transforming the primary care industry by bringing doctor’s visits into the home with remote physical exams that provide affordable, always-on, and accessible primary care for all. TytoCare works with healthcare insurers and providers to provide better access to primary care virtually, with a handheld exam kit that connects users with a clinician for a medical exam and telehealth visit no matter where they are.
In the decade since co-founding the company, Mr. Gilad has led the launch and establishment of TytoCare as a major player in the telehealth market. Under his leadership, the company has built partnerships with nearly 250 major healthcare players across the world. Mr. Gilad and TytoCare have been recognized as a leader in the telehealth market, with awards from ATA, Fast Company, MEDICA, Forbes, and more, and have established a track record of improving access to healthcare and better telehealth adoption and results than other solutions on the market.
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