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February 26.2025
2 Minutes Read

Cedars-Sinai Uses Generative AI to Extract Data on Pickleball Injuries

Cedars-Sinai uses AI for pickleball injury data, two men playing.

Transforming Injury Data with AI

Cedars-Sinai Medical Center is leveraging the power of generative AI to analyze a growing concern: pickleball injuries. With the sport booming in popularity, injuries among players have surged, rising over 200% in recent years. Many of these injuries are attributed to falls, leading to serious conditions such as bone fractures and ligament sprains, as highlighted by a recent report.

Unleashing the Power of AI for Data Extraction

By harnessing AI, particularly the capabilities of GPT-4, Cedars-Sinai has been able to extract actionable data from unstructured medical notes. Kathy Bailey, a principal data intelligence analyst with the institution, emphasized that this innovative approach allows for the identification of injuries that traditional methods often miss. Previously, extracting this data required manual input from clerks, but AI enables a faster, more efficient review, cutting down the time from weeks or months to mere hours.

Significance of Accurate Data in Healthcare

The project not only showcases the advancements in healthcare technology but also addresses a critical gap in injury data collection. Given that many injuries occur outside of structured medical codes, having AI analyze clinician notes provides a richer depth of context surrounding each incident. This is especially pertinent in pickleball, where understanding the nuances can lead to better preventative strategies and patient outcomes.

Looking Ahead: The Future of Injury Prevention

As the popularity of pickleball continues on its exponential trajectory, insights gathered from this AI project could set a precedent for future studies in other sports. The findings not only shed light on pickleball injuries but could also contribute to shaping injury prevention strategies across various activities, particularly among older adults who are increasingly participating in these recreational sports.

As we embrace these technological advancements, the partnership between healthcare and AI stands to revolutionize how injury data is collected and analyzed, ultimately improving care for patients.

Kathy Bailey will present more on this innovative approach at HIMSS25, offering insights that could change the landscape of injury data collection in sports and healthcare.

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The Hidden Costs of AI: Energy Use and Climate Impact Unveiled

Update Understanding AI's Growing Energy Demands Recent investigations into AI energy use reveal significant reality checks for AI enthusiasts and developers alike. Machine learning models, particularly in “query-heavy” tasks such as generating content or analyzing data, have varying energy needs based on the complexity and type of task. For instance, researchers have found that a request as simple as generating a few jokes can use vastly less energy compared to more complicated tasks, such as planning a travel itinerary, which has been observed to consume nearly tenfold the energy. Notably, power requirements can increase up to seventy times depending on the model size utilized, emphasizing that not all AI applications are created equal. The Carbon Footprint of AI Energy Consumption Yet, energy efficiency isn’t solely about the demand; it heavily depends on how that energy is produced. The source of the electricity powering AI systems (wind, solar, coal, etc.) drastically influences the overall climate impact of AI operations. A data center powered by renewable sources will have a significantly lower carbon footprint compared to one relying on fossil fuels. This brings forth crucial discussions on responsible AI deployment. Using measures of carbon intensity provides a holistic understanding of AI's environmental impact, highlighting the urgent need for data centers to adopt sustainable energy practices. Anticipating Future Trends in AI and Sustainability Lastly, as the demand for AI continues to grow, industry stakeholders must focus on refining models and investing in energy-efficient practices. This evolving landscape presents opportunities alongside its challenges. Major tech companies need to consider their energy efficiencies not just for operational costs but to fulfill corporate responsibilities towards sustainable practices. In conclusion, as we navigate the rapidly advancing AI terrain, understanding its energy requirements and environmental implications is pivotal. The responsibility now squarely rests on tech developers and leaders to innovate sustainably in both model efficiency and energy sourcing.

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