Abstract: A computer aided medical method include following steps. An initial symptom is collected through an interaction interface. A representative prediction model is selected from plural candidate prediction models according to the initial symptom. The candidate prediction models are trained by a machine learning algorithm according to clinical data. A series of sequential actions is generated according to the representative prediction model and the initial symptom. The sequential actions are selected from plural candidate actions in the representative prediction model. The candidate actions include plural inquiry actions and plural disease prediction actions. Each of the sequential actions is one of the inquiry actions or the disease prediction actions. The series of sequential actions is displayed on the interaction interface.

Abstract: A computer aided medical method includes the following steps. An initial symptom of a patient and context information is collected through an interaction interface. Actions in a series are sequentially generated according to the candidate prediction models and the initial symptom. Each of the actions corresponds to one of the inquiry actions or one of the disease prediction actions. If the latest one of the sequential actions corresponds to one of the disease prediction actions, potential disease predictions are generated in a first ranking evaluated by the candidate prediction models. The first ranking is adjusted into a second ranking according to the context information. A result prediction corresponding to the potential disease predictions is generated in the second ranking.

Abstract: The disclosure provides a method for verifying training data, a training system, and a computer program produce. The method includes: receiving a labelled result with a plurality of bounding regions, wherein the labelled result corresponds to an image, the bounding regions are labelled by a plurality of annotators, the annotators comprises a first annotator and a second annotator, and the bounding region comprises a first bounding region labelled by the first annotator and a second bounding region labelled by the second annotator; and determining the first bounding region and the second bounding region respectively corresponds to different two target objects or corresponds to one target object according to a similarity between the first bounding region and the second bounding region.

Abstract: A computer aided medical method include following steps. An initial symptom is collected through an interaction interface. A representative prediction model is selected from plural candidate prediction models according to the initial symptom. The candidate prediction models are trained by a machine learning algorithm according to clinical data. A series of sequential actions is generated according to the representative prediction model and the initial symptom. The sequential actions are selected from plural candidate actions in the representative prediction model. The candidate actions include plural inquiry actions and plural disease prediction actions. Each of the sequential actions is one of the inquiry actions or the disease prediction actions. The series of sequential actions is displayed on the interaction interface.

Abstract: A computer aided medical method include following steps. An initial symptom is collected through an interaction interface. A representative prediction model is selected from plural candidate prediction models according to the initial symptom. The candidate prediction models are trained by a machine learning algorithm according to clinical data. A series of sequential actions is generated according to the representative prediction model and the initial symptom. The sequential actions are selected from plural candidate actions in the representative prediction model. The candidate actions include plural inquiry actions and plural disease prediction actions. Each of the sequential actions is one of the inquiry actions or the disease prediction actions. The series of sequential actions is displayed on the interaction interface.

Abstract: A computer aided medical method includes the following steps. An initial symptom of a patient and context information is collected through an interaction interface. Actions in a series are sequentially generated according to the candidate prediction models and the initial symptom. Each of the actions corresponds to one of the inquiry actions or one of the disease prediction actions. If the latest one of the sequential actions corresponds to one of the disease prediction actions, potential disease predictions are generated in a first ranking evaluated by the candidate prediction models. The first ranking is adjusted into a second ranking according to the context information. A result prediction corresponding to the potential disease predictions is generated in the second ranking.

Abstract: A computer aided medical method includes the following steps. An initial symptom of a patient and context information is collected through an interaction interface. Actions in a series are sequentially generated according to the candidate prediction models and the initial symptom. Each of the actions corresponds to one of the inquiry actions or one of the disease prediction actions. If the latest one of the sequential actions corresponds to one of the disease prediction actions, potential disease predictions are generated in a first ranking evaluated by the candidate prediction models. The first ranking is adjusted into a second ranking according to the context information. A result prediction corresponding to the potential disease predictions is generated in the second ranking.

Abstract: The disclosure provides a method for verifying training data, a training system, and a computer program produce. The method includes: receiving a labelled result with a plurality of bounding regions, wherein the labelled result corresponds to an image, the bounding regions are labelled by a plurality of annotators, the annotators comprises a first annotator and a second annotator, and the bounding region comprises a first bounding region labelled by the first annotator and a second bounding region labelled by the second annotator; and determining the first bounding region and the second bounding region respectively corresponds to different two target objects or corresponds to one target object according to a similarity between the first bounding region and the second bounding region.

Abstract: The disclosure provides a method for verifying training data, a training system, and a computer program produce. The method includes: providing a plurality of raw data to a plurality of annotators; retrieving a plurality of labelled results, wherein the labelled results includes a plurality of labelled data, and the labelled data are generated by the annotators via labelling the raw data; determining a plurality of consistencies by comparing the labelled results, and accordingly determining whether the labelled results are valid for training an artificial intelligence machine; in response to determining that the labelled results are valid, determining at least a specific part of the labelled results are valid for training the artificial intelligence machine.

Abstract: A computer aided medical method includes the following steps. An initial symptom of a patient and context information is collected through an interaction interface. Actions in a series are sequentially generated according to the candidate prediction models and the initial symptom. Each of the actions corresponds to one of the inquiry actions or one of the disease prediction actions. If the latest one of the sequential actions corresponds to one of the disease prediction actions, potential disease predictions are generated in a first ranking evaluated by the candidate prediction models. The first ranking is adjusted into a second ranking according to the context information. A result prediction corresponding to the potential disease predictions is generated in the second ranking.

Abstract: A computer aided medical method include following steps. An initial symptom is collected through an interaction interface. A representative prediction model is selected from plural candidate prediction models according to the initial symptom. The candidate prediction models are trained by a machine learning algorithm according to clinical data. A series of sequential actions is generated according to the representative prediction model and the initial symptom. The sequential actions are selected from plural candidate actions in the representative prediction model. The candidate actions include plural inquiry actions and plural disease prediction actions. Each of the sequential actions is one of the inquiry actions or the disease prediction actions. The series of sequential actions is displayed on the interaction interface.

Abstract: A medical system includes an interaction interface and an analysis engine. The interaction interface is configured for receiving an initial symptom. The analysis engine is communicated with the interaction interface. The analysis engine includes a prediction module. The prediction module is configured for generating symptom inquiries to be displayed on the interaction interface according to a prediction model and the initial symptom. The interaction interface is configured for receiving responses corresponding to the symptom inquiries. The prediction module is configured to generate a result prediction according to the prediction model, the initial symptom and the responses.