It offers new tools that can help clinicians perform their jobs better. Still, many healthcare professionals are not fully trained or familiar with how to use AI well. For medical practice administrators, owners, and IT managers, understanding this gap in AI education is essential. Closing it can help their teams use AI in a way that improves efficiency, patient care, and the overall operation of medical practices.
This article explains why AI education is necessary for clinicians, the current gaps in training, and how AI can be integrated effectively into medical practice workflows. The goal is to provide healthcare leaders with clear information on how to support their staff as they adapt to these new technologies.
AI is changing healthcare by automating repetitive tasks, helping with diagnoses, and supporting treatment decisions. According to Samir Kendale, MD, from Beth Israel Lahey Health, AI has the ability to improve patient care across many specialties. However, the rapid development of AI tools means that many clinicians are not prepared to use them effectively. This is mostly because formal AI education has only recently been introduced in medical training.
In the United States, this lag in AI knowledge presents a challenge for medical practices. Clinicians without sufficient AI training may not fully benefit from the technology or may use it incorrectly, reducing its potential advantages. For healthcare administrators and IT managers working in clinics or hospitals, the need to train their staff on AI is becoming increasingly urgent.
A survey titled “Radiographer Education and Learning in Artificial Intelligence (REAL-AI)” conducted in 2023 shows that this challenge is not limited to physicians alone. The survey looked at radiographers, radiologists, and students from 25 countries, including the United States. It revealed that only 10.3% of respondents had received any formal training on AI, with many still unsure about how to integrate AI into their work. This data clearly indicates that there is a widespread knowledge gap across different healthcare roles, not just doctors.
One reason behind the slow adoption of AI education is that many medical schools and training programs have just begun to include it in their curriculum. Traditional medical training focused on clinical knowledge and practical skills but did not prepare students for emerging digital tools. As AI technologies advance quickly, healthcare organizations now face the task of catching up.
Maha Farhat, MD, MSc, stresses the importance of clinicians learning how to incorporate AI into their work. She notes that being informed about AI technology allows clinicians to explore new diagnostic methods and treatments with less uncertainty. This is especially important when handling complex medical decisions. Without this knowledge, clinicians may miss opportunities or feel hesitant to rely on AI support.
Another challenge is the variation in AI literacy among healthcare staff. While some clinicians may use AI tools daily, others are unsure whether they should be using them or how to do so safely. This uncertainty can slow down AI adoption and even cause resistance within teams.
For medical administrators and IT managers, one of the most practical questions is how to make AI work in everyday clinical settings. It is not just about having the technology but ensuring it fits into workflows without causing disruptions.
AI can automate many routine tasks that clinicians previously did manually. For example, Samir Kendale mentions that AI tools can capture visit notes, summarize patient histories, and assist in clinical documentation. This helps reduce the time clinicians spend on paperwork and allows them to spend more quality time with patients.
In a busy medical practice, automating front-office communications is also vital. This is where companies like Simbo AI come into play. Simbo AI specializes in front-office phone automation and answering services using AI. By automating phone calls and handling routine administrative inquiries, practices can reduce the human workload on receptionists and administrators. This not only improves patient satisfaction by providing quicker responses but also streamlines workflows to reduce bottlenecks.
Automated phone systems can handle appointment scheduling, provide answers to common questions, and route complex calls to the right department. Having an AI-powered answering service ensures that patient interactions are more efficient and staff are freed up for more critical tasks.
Furthermore, AI can help identify patients at risk for serious conditions such as sepsis or opioid dependency by analyzing large datasets. This proactive approach allows healthcare providers to intervene early, improving patient safety and outcomes.
Integrating AI tools into existing workflows requires good planning and collaboration between clinical staff and IT professionals. Working closely with informatics teams helps ensure that AI technology supports the healthcare delivery process properly, enhancing both safety and service quality.
When clinicians and staff understand AI, they can use it more effectively and with greater confidence. This education helps them to:
Despite its clear benefits, AI training remains limited across healthcare professions. The REAL-AI survey highlights this issue in medical imaging fields, but it applies broadly across clinical roles. For instance, among radiographers, radiologists, and students surveyed globally, less than one in ten had any formal AI education. This gap is notable in US practices, as AI tools become more embedded in patient care.
The US Health and Care Professions Council (HCPC) recently amended standards to require healthcare professionals to demonstrate AI awareness. While this is a step forward, many practitioners still need practical and focused training to meet these standards.
It is essential for healthcare leaders to support ongoing AI education. Professional organizations, medical centers, and universities should collaborate to develop tailored programs that address the specific needs of different roles. Hands-on training, workshops, and continuous education can reduce fear and resistance to technology while building essential skills.
Medical practice administrators, owners, and IT managers play a key role in helping clinicians adapt to AI. They can take several steps:
Clinicians already engaged in AI learning can also help train their peers, creating a friendly environment where knowledge spreads naturally.
The United States healthcare system presents unique challenges and opportunities for AI integration. Complex regulations, diverse patient populations, and high patient volumes mean that AI tools must be carefully implemented to respect privacy, meet compliance standards, and handle varied medical needs.
AI has the potential to support value-based care models being adopted widely across US healthcare systems. In these models, providers focus on delivering high-quality care while managing costs. AI’s ability to analyze patient data and predict risks helps practices to meet these goals by improving preventive care and reducing hospital readmissions.
With rising burnout rates among US clinicians, AI’s contribution to workflow efficiency is especially important. Reducing paperwork and automating front-office communications has a direct positive effect on clinical staff well-being.
American medical practices also face pressure to adopt new technologies quickly to stay competitive. AI education helps organizations use these tools thoughtfully rather than just reactively, ensuring better outcomes and smoother transitions.
AI has a growing role in medical practice in the United States. However, without proper education, clinicians and healthcare teams cannot fully use its benefits. By investing in AI training and integrating automation technologies like Simbo AI’s phone answering services, healthcare administrators can support their clinical staff and improve patient care. Bridging the AI knowledge gap is no longer optional—it is necessary for effective, safe, and efficient healthcare delivery.
AI is revolutionizing healthcare by automating routine tasks, improving diagnoses, and facilitating the discovery of more effective treatments across various specialties.
Many healthcare providers lack familiarity with AI, as its introduction in medical education is recent. Clinicians need to fill this knowledge gap to incorporate AI effectively into their practices.
AI automates tasks like capturing visit notes, allowing clinicians to focus more on patient interaction, which can help reduce burnout and improve patient experience.
AI enhances the interpretation of imaging results by using image recognition for identifying polyps in colonoscopy and flagging irregularities in EKG and CAT scans.
AI analyzes large datasets to identify high-risk patients, enabling proactive responses such as timely interventions to prevent complications like sepsis.
AI can provide instant access to extensive data, helping clinicians formulate treatment options and personalizing care by analyzing similar historical cases.
AI speeds up the diagnosis of rare diseases by scanning large datasets to find similar cases and effective treatments, which clinicians might struggle to identify on their own.
Integrating AI can enhance quality and efficiency, streamline processes, and ensure better patient outcomes, aligning with value-based care principles.
Engaging with informatics teams in their healthcare systems and connecting with professional organizations can provide insights and resources on AI applications in medicine.
With ongoing innovation driven by digitization, AI is expected to further revolutionize clinical practices, ultimately transforming patient care delivery.
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