Revolutionizing Patient Education: The Role of AI in Sports Medicine

Artificial Intelligence, especially technologies like ChatGPT, is actively reshaping patient education in sports medicine—promising clarity and reach while raising questions about trust, accuracy, and how best to balance technology with human guidance.

Artificial Intelligence, especially technologies like ChatGPT, is actively reshaping patient education in sports medicine, with early evaluations suggesting gains in accessibility and understanding that may translate to better experiences. As sports injuries become increasingly complex, so does the need for precise, reliable, and accessible information—points we revisit below when discussing scoliosis and anterior cruciate ligament queries. AI’s potential to generate clear, comprehensive materials is promising, but the extent of benefit is still being established.

In evaluations, ChatGPT has demonstrated the ability to provide detailed and clinically relevant responses to patient queries on complex topics such as scoliosis and anterior cruciate ligament injuries. By offering high-quality information synthesized from academic research, ChatGPT has been reported to provide more detailed or more complete responses than general web searches, according to a recent Cureus evaluation. These early findings set the stage for cautious optimism while underscoring the need for careful oversight.

Building on improved understanding, AI tools are being used not only to deliver information but also to support more personalized communication with patients. In pilot implementations, AI-assisted systems have offered tailored and accessible information that can help some patients engage more actively with their care; effects vary across literacy levels and care settings, as described in a cohort study. Clear signposting, plain language, and bilingual options are recurring themes where these systems appear to be most helpful.

From the patient experience lens, these tools are also being explored for injury prevention. By analyzing training and health data, AI can help clinicians and coaches adjust training plans and provide real-time feedback; in early reports, such approaches may reduce injury risk and improve safety culture. Importantly, prevention messages delivered at the right time and in patient-friendly language can reinforce the same education goals discussed above.

Even as personalization and access expand with AI tools, some patients prefer traditional, human-centric communication. That reality argues for a blended approach in which AI complements—rather than replaces—conventional care, with clinicians guiding use to support trust and adherence. When patients know how a tool was used and what its limits are, they can better integrate digital guidance with clinical advice.

Emerging opportunities in AI are also shaping rehabilitation. AI tools can support real-time adjustments and feedback during home-based exercises, which may enhance adherence and perceived engagement—particularly in remote settings where access is limited—according to a pilot program. For clinicians, this can translate into more timely course corrections and clearer insight into how patients are actually performing prescribed routines.

Equity and access remain central. While AI may lower barriers by providing plain-language explanations and on-demand guidance, gaps in digital literacy and device access can blunt these benefits. Program design should prioritize inclusive language, multimodal formats (text, audio, visuals), and workflows that do not assume constant connectivity. Ensuring interpreter integration and accessible reading levels is essential for patients with limited English proficiency or lower health literacy.

Safety and quality assurance also require attention. Teams need protocols for reviewing AI-generated content, documenting clinical oversight, and updating materials as evidence evolves. In practice, this can include checklists for accuracy and completeness, bias reviews, and version control so that outdated information is not inadvertently reused in patient-facing materials.

Clinician workflow integration is the hinge on which value often turns. Embedding AI education prompts into pre-visit planning, after-visit summaries, and remote monitoring dashboards can help clinicians reinforce key messages efficiently. Conversely, bolting tools onto existing systems without clear ownership or training risks confusion and inconsistent use.

Measurement should match ambition. Rather than focusing solely on clicks or time-on-page, evaluation can track practical endpoints like patient understanding scores, adherence to home exercise programs, follow-up question clarity, or appropriate self-care vs. escalation decisions. Early program audits can surface disparities and safety signals that merit design tweaks.

Looking ahead, governance will shape sustainable adoption. Transparent data use policies, patient consent processes, and clear pathways for reporting errors can build confidence. Multidisciplinary oversight—pairing clinicians, patients, data scientists, and ethicists—helps ensure that tools remain aligned with patient-centered goals in sports medicine.

Key Takeaways:

• Early evaluations suggest AI tools can make patient education more accessible and comprehensible for common sports injuries, though effectiveness varies by context.
• Personalization and communication support appear promising in pilot and cohort settings, with benefits depending on literacy, language, and care environment.
• AI-supported prevention and rehabilitation programs may aid adherence and safety culture, but current evidence is preliminary and should complement—not replace—clinical judgment.
• A blended model that pairs AI with clinician guidance best addresses trust, accuracy, and access concerns raised by patients and providers.