BAS 112 Medical & Health Lecture 10

Standardization of AI in Healthcare

• Regulatory guidance for AI in healthcare is currently inadequate.
• The World Health Organization is developing a standardized assessment framework for AI applications related to health decisions.
• Standardization efforts should include methods to measure and reduce bias, ensuring reliability and performance.
• Ethical considerations, such as data privacy and safety, are essential in AI standardization.
• Standards for human augmentation must be established.

Trust in AI Systems

• Trust in AI requires development and operation by skilled professionals.
• Explanations provided by AI systems should be clear and understandable for users.
• Reliability is a critical factor in fostering trust in AI.
• Unbiased decision-making must be a core feature of AI systems.
• AI systems should undergo verification and validation according to recognized software development standards.

AI Applications in Predictive Medicine

• Predictive medicine identifies patients at risk for diseases, enabling early intervention strategies.
• AI assists in predicting effective treatments by analyzing individual patient data.
• AI is utilized in assessing hospital readmission risks and detecting patients at risk of adverse medication effects.

AI Applications in Participatory Medicine

• Participatory medicine empowers patients to monitor their health, such as through vaccinations.
• Individuals can collect and track health indicators, which AI can analyze to improve understanding and reduce healthcare workload.
• Mobile health platforms collect symptom data combined with medical information to monitor disease spread and patient interactions.

AI Applications in Personalized Medicine

• Personalized medicine utilizes genetic profiles to inform disease prevention, diagnosis, and treatment decisions.
• AI aids clinicians in selecting appropriate medications and dosages to improve patient outcomes.
• Early diagnosis and risk assessment lead to improved care while managing costs.
• AI enhances disease prediction, diagnosis accuracy, and optimal treatment approaches.

AI Applications in Preventive Medicine

• Preventive medicine focuses on practices designed to avert disease occurrence.
• Technologies such as wearables and telemedicine enhance patient monitoring and care quality.
• AI processes vast amounts of data to enable early intervention and disease prevention strategies.
• Successful disease intervention relies on knowledge of causative factors, risk identification, and early treatment methods.

This quiz covers Lecture 10 of the BAS 112 course, focusing on the standardization of AI in healthcare. It discusses the need for regulatory guidance and the initiatives by the World Health Organization to develop assessment frameworks for AI in health-related decisions.