1. Stakeholders who should be involved:

1.1 IT workers or data controller/health informatics, they take responsibilities to design and implement the IT infrastructure necessary for running machine learning models, including servers, cloud services and data storage solutions and to ensure that the infrastructure can handle the computational demands of training and deploying machine learning models. Also, they could do manage databases that store patient data for ensuring data integrity, security measures and compliance with healthcare regulations such as HIPAA. Including, to optimize long term system performance by upgrading hardware, improving software and resolve any technical issues that may disrupt the operation. Lastly, to train healthcare staffs on how to use new IT systems and machine learning tools effectively.

1.2 Regulators, they will ensure that the machine learning system complies with legal, ethical and safety standards before and after deployment for example they should develop guidelines for the development, validation and deployment of machine learning systems + establish protocols for validating to conduct thorough reviews of the machine learning system’s documentation before approving it for use, especially to ongoing monitor to ensure the system compliance with regulatory standards.

2. Potential ethical considerations should keep in mind deploying machine learning models to predict cancer symptoms:

For Security + Data Privacy, this is to ensure that the protection of patient data and maintaining the confidentiality and integrity. For example of access control, clinicians can view and update patient data, but administrative staff can only access billing information. So, conducting audit logs should help to identify and investigate suspicious activities.

HIPAA compliance could be applied to ensure this system aligns HIPAA regulations by implementing appropriate administrative and having technical safeguards.