Calibrating healthcare AI
Problem being addressed
Artificial intelligence techniques such as deep learning have achieved unprecedented success with critical decision-making, from diagnosing diseases to prescribing treatments, in healthcare. However, to prioritize patient safety, one must ensure such methods are accurate and reliable.
Prediction calibration to meet 2 objectives: characterizing model reliability and enabling rigorous introspection of model behavior. More specifically, the approach is comprised of a calibration-driven learning method, which is also used to design an interpretability technique based on counter-factual reasoning. Furthermore, reliability plots, a holistic evaluation mechanism for model reliability, are introduced.
Advantages of this solution
Prediction calibration is an effective principle for designing reliable models as well as building tools for rigorous model introspection. The presented analysis clearly demonstrates the different kinds of insights one can infer by performing counterfactual reasoning via prediction calibration with disentangled latent spaces.
Solution originally applied in these industries
Possible New Application of the Work
Prediction calibration can be applied in pharmaceutical industry, presenting opportunities for online analyses to achieve real-time assessment of intermediates and finished dosage forms.
Source URL: #############