Recent research from different countries shows that many healthcare workers think AI is useful in modern care. A study in Dhaka, Bangladesh, found that about 62% of healthcare professionals knew AI can help improve patient outcomes. More than half, about 51%, believed AI is helpful for early disease detection and diagnosis. Over 57% said they felt comfortable using AI tools in their daily work. This shows that more people are starting to accept AI technologies.
Even with these facts, there are some problems. Only about 39% had good knowledge about AI in healthcare. But around 73% had positive feelings about using AI. This means that even if people like the idea of AI, they still need more education to use it well.
In the United States, the story is similar. Data about how much healthcare workers know about AI changes by job type and place. A survey linked to the European Congress of Radiology, called REAL-AI, included people from the U.S. and other countries. It showed only 29.4% of radiology workers use AI in their jobs, and just 10.3% had formal AI training.
These numbers suggest many healthcare workers are either not ready or unsure about AI. For example, medical imaging professionals are still learning about how to use AI. Many do not know enough about how AI fits in their work.
In the U.S., though AI tools are more common, many healthcare workers do not know when or how to use AI. They are also not sure how AI will change their job duties. For healthcare managers and IT leaders, these knowledge gaps are a problem but also a chance to offer good education and training.
Continuous professional development, or CPD, means healthcare workers keep learning to improve their skills throughout their careers. CPD is very important for new technology like AI. AI changes fast and can be complicated.
A study by Moustaq Karim Khan Rony and others showed that healthcare workers who went to AI conferences or read research papers knew more and had better attitudes about AI. They were about 1.27 to 1.31 times more likely to have good knowledge and positive views. This means learning and research help people understand and accept AI better.
Healthcare organizations in the U.S. need CPD programs focused on AI skills. Such programs help workers keep up with new AI tools. They also clear up wrong ideas and reduce worries about AI taking jobs. CPD helps build confidence needed to use AI responsibly.
In the United Kingdom, the Health and Care Professions Council recently required radiographers to know about AI. This shows a growing trend. Similar rules may come in the U.S. soon. Many healthcare certifications might include AI skills. So, CPD is important to meet these future rules.
Moreover, CPD can be designed for different healthcare worker groups. This makes sure doctors, radiologists, and office staff all get useful AI knowledge that fits their work.
Special education programs can help fix AI knowledge gaps. The Heliyon study found that age, gender, and job type affect how much healthcare workers know about AI. Younger workers (ages 18–35) and males knew more and had better attitudes. Doctors and full-time workers also had better knowledge.
Because people learn in different ways, education programs can be made flexible. Examples include:
These ideas help people overcome fear or doubts about technology. They also build their confidence in using AI tools well.
The REAL-AI survey also showed very few medical imaging workers (only 10.3%) had formal AI training. Fixing this needs partnerships between healthcare providers and schools. They can create certification courses and on-the-job training about AI.
AI can automate tasks and make healthcare faster and easier. For example, companies like Simbo AI use AI to help with front-office phone calls and answering questions. This shows AI tools can reduce work for staff.
For medical office managers and IT leaders, AI in front-office work can:
AI also supports clinical staff by helping to analyze images, suggest treatments, and watch patient health signs.
However, to make AI automation work well, staff must be trained to understand AI’s strengths and limits. Training front-office and clinical teams on how to use AI tools helps build trust and avoid mistakes that can upset patients.
IT managers have an important role too. They choose the right AI tools, connect them to electronic health records, and follow healthcare rules like HIPAA. They also need ongoing learning to manage new AI technologies.
So, training both administrative and clinical staff, along with CPD for IT workers, helps healthcare get the most out of AI tools.
Many people hesitate to use AI because they do not know enough, worry about losing jobs, or fear AI might not be accurate or ethical. These issues are common in the U.S. healthcare system, which is large and complex with many specialties and different resources.
Studies show that positive views about AI are linked to good knowledge (correlation r = 0.89). This means education can change doubt into acceptance. On the other hand, lack of knowledge links to bad attitudes and blocks AI use.
Medical practice leaders in the U.S. must make sure their staff get proper training. Investing in CPD programs and supporting attendance at AI workshops or conferences can close knowledge gaps. Working with schools to make AI classes for healthcare workers is also a long-term help.
Healthcare IT leaders help by providing access to AI learning resources and guiding their organizations through technology changes.
CPD and focused education affect more than just individual knowledge. Medical practice leaders and IT managers need to plan for their entire organization to use AI well.
By meeting educational needs, healthcare leaders in the U.S. can build a system that works well with AI. This supports better patient care and runs healthcare more smoothly with smart tools and good clinical decisions.
AI will keep growing in healthcare tasks, both in offices and clinics in the U.S. Closing knowledge gaps with CPD and special education programs is important to prepare healthcare workers for this change. Medical practice leaders, healthcare owners, and IT managers should support ongoing and fair education programs that fit the needs of all staff. Being ready this way will help AI tools work better, improve patient care, and make U.S. healthcare systems more effective in the future.
62.18% of healthcare workers recognize AI’s role in improving patient outcomes, indicating a majority awareness of its potential impact on healthcare delivery.
57.54% of healthcare workers reported being comfortable using AI tools in their daily tasks, reflecting moderate acceptance and readiness to incorporate AI technologies in routine clinical operations.
Younger healthcare workers (ages 18–35) and males show better knowledge and more positive attitudes toward AI adoption, highlighting demographic variance in AI receptivity and understanding.
There is a strong positive correlation (r = 0.89) between AI knowledge and positive attitudes, suggesting that better understanding of AI significantly boosts acceptance and confidence in its use.
Attending AI conferences is positively associated with good AI knowledge and positive attitudes (AOR 1.27), emphasizing the value of professional development and exposure to new AI advancements.
Learning through research articles and journals increases the odds of good AI knowledge and positive attitudes (AOR 1.31), underscoring the importance of evidence-based education for workforce readiness.
Physicians, hospital workers, and full-time employees exhibit higher odds of good AI knowledge and positive attitudes, indicating professional role and employment status as influencing factors.
Continuous professional development is crucial as it helps healthcare workers keep up with evolving AI technologies, fostering confidence and competence that translate into improved patient outcomes and healthcare efficiency.
Targeted educational interventions tailored to specific demographic differences are recommended to bridge AI knowledge gaps, ensuring equitable preparedness across diverse healthcare professional groups.
Policies and training programs must consider demographic differences and promote ongoing learning to equip healthcare workers with necessary AI skills, enabling effective integration of AI technologies in clinical practice.
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