AI-Based Medical Decision Support: Exploring the Data Gap

Comprehensive Summary

AI is increasingly being used to aid physicians in clinical decision making, however its real world impact is still limited. The biggest issue lies in AI's holistic efficacy stemming from input data used in training. This data is often fragmented and incomplete. Most AI systems rely on electronic medical records(EMRs), which are designed for billing rather than detailed medical understanding. Due to this, important patient information is often missing, scattered, or unrecorded in a consistent manner. Even advanced tools like natural language processing and large language models can only work as well as the input data. Medicine also lacks continuous, real time physiological data, especially for muscle and joint health where measurements are subjective. Future progress in medical AI will depend on better data collection, especially through wearable technologies such as sensors and continuous monitoring technologies.

Outcomes and Implications

AI in healthcare is limited because it depends on incomplete and inconsistent medical records. This affects how accurate and useful AI-based or generated medical recommendations are for patient care. To improve this, medicine requires continuous data instead of subjective measurements or one-time records. Wearable devices and sensor based tools can help doctors track patients more accurately, especially for movement and musculoskeletal issues. Overall, improving AI for medicine will depend on the quality of data rather than focusing on the algorithm themselves.