AI in healthcare is moving faster than people can practically keep up with, and a gap in literacy is starting to manifest. A study titled Bridging the AI-Literacy Gap in Health Care: Qualitative Analysis of the Flanders Case Study hypothesizes that diagnostics, predictive analytics, and admin automation could improve quickly in the coming years. It estimates that AI could handle 30–40% of certain jobs. However, many healthcare workers still don't know how to utilize these tools securely and reliably, as training lags behind technological advancement.
AI is already a part of some elements of the US healthcare software offerings, mostly to make operations run more smoothly. Communication and documentation are among the most useful tools because they address the daily workflow problems professionals face. As Duke Health’s Jeffrey Ferranti puts it in an AAMC news article, “our doctors are burned out and overburdened by trying to keep up with tremendous amounts of administrative paperwork.”
These tools include:
The AI gap in healthcare implies that AI tools are getting smarter all the time, but many healthcare personnel don't obtain the training they need to utilize them safely and confidently. Research linked to the NCBI says that there is a widening gap between fast-moving technology and slow-moving education, especially after people graduate and have to learn as they go.
Personnel often have a hard time figuring out if an AI output is dependable, spotting bias, knowing what the tool can and can't accomplish, and figuring out who is responsible when anything goes wrong. A Journal of Medical Internet Research study found that only 13.8% of professionals think their training prepared them effectively for working with AI.
Nurses and other health professionals are the ones who feel the most pressure since they often rely on informal, unstructured learning instead of regulated, recognized programs. System obstacles keep the gap open since there is no protected time when personnel are low, resources are tight for upskilling, and there aren't many role-specific courses that match what you do every day in clinical work.
Many healthcare workers start from a low baseline on AI knowledge, which makes one-off training easy to forget and hard to apply under pressure; in one large Frontier in Public Health nursing survey, 57% said they knew “only a little” about AI. Training also tends to be generic, while real-world adoption depends on role-specific judgment. Knowing when an output is unreliable, how to challenge bias, and how to explain uncertainty to patients.
Workplace conditions then erase the gains as protected time is scarce, staffing is tight, and learning becomes extra work, so skills never consolidate into routine practice. A Digital Health study on generative AI use in clinical settings shows how limited institutional enablement can be as among 1,005 general practitioners surveyed, only 5% had received training, and only 11% were encouraged by their employer to use generative AI tools, which helps explain why confidence and safe use lag behind tool availability.
An Implementation Review piece puts it well: “Technological capabilities alone cannot shift complex care ecosystems overnight.” To close the AI gap, we need to help doctors with the things that are really stressing them out, too many emails, too much paperwork, and constant coordination work that takes time away from patients. When health institutions use generative AI as part of their workflow infrastructure instead of just a glossy add-on, it can assist.
AI-written replies to portal messages can help reduce cognitive load and signs of burnout by making it easier and faster to react to a lot of patient messages. A clinician can still evaluate and change the replies before they are delivered. Automated communication tools also give understaffed teams more capacity right away, especially in fields where there are a lot of emails. Paubox and other platforms like it are part of the security side of the "close-the-gap" plan.
Generative-AI detection helps employees deal with phishing and impersonation attempts that are getting more and more convincing without making every employee a cybersecurity expert. When companies use these tools along with role-based micro-training that focuses on the specific parts of their jobs that AI affects, written rules for how to use them and how to report problems, and auditing and feedback loops that keep track of error rates and corrections.
See also: HIPAA Compliant Email: The Definitive Guide (2026 Update)
No. Oversight is split across agencies and existing health laws. FDA covers many medical devices, HHS and ONC cover certified health IT, OCR and HIPAA cover privacy, FTC covers unfair or deceptive practices, and states can add extra requirements.
FDA regulation applies when the AI is a medical device or part of one, meaning the product is intended to diagnose, treat, cure, mitigate, or prevent disease.
Few laws mandate clinician training in a direct, universal way. Requirements show up indirectly through device instructions, organizational policy, payer and accreditation expectations, and risk management practices.