Explanation:
The correct answer is C.
C is correct because machine learning experts and data scientists are integral during the AI Development phase, which involves building the AI model. In this scenario, their expertise is crucial for selecting the appropriate algorithms, training the AI model with the medical imaging dataset, and ensuring the tool's reliability and accuracy in medical diagnoses.
A is incorrect as the deployment phase, involving the integration of the tool into hospital systems, typically requires system integrators and software developers. This phase comes after the development and testing of the AI model.
B is incorrect because the planning and designing phase primarily involves AI designers, domain experts, and potentially healthcare professionals to set the tool's objectives and functionalities. This phase precedes the development and model-building stage.
D is incorrect as continuous monitoring and adaptation post-deployment are tasks for operational managers and compliance experts. This phase involves ensuring the tool's ongoing effectiveness and compliance with healthcare standards, which is after the model development and testing phase.
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Q.5698 A healthcare startup, "MediAI," is working on an AI-driven diagnostic tool that can analyze medical imaging to assist in early detection of diseases. The tool has been meticulously planned and designed. The team has collected a vast dataset of medical images and is now ready to build the AI model. They require expertise to select the right algorithms, train the model with the dataset, and ensure its accuracy and reliability in diagnosing various conditions. At which point in this scenario does the involvement of machine learning experts and data scientists become most crucial?
A
During the final deployment phase where the diagnostic tool is integrated into hospital systems and its compatibility is tested.
B
When the team is planning and designing the diagnostic tool, setting objectives, and determining the tool's functionalities.
C
While selecting, training, and testing the AI model with the medical image dataset to ensure its accuracy and reliability.
D
After the tool is deployed, for continuous monitoring of its performance and adapting the tool based on user feedback.
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