Predictive analytics in Healthcare
Category
Sciences and Technology
Department
Technology
Student Status
Graduate
Research Advisor
Dr. Tatiana Goris
Document Type
Event
Location
Governors
Start Date
10-4-2025 11:20 AM
End Date
10-4-2025 11:20 AM
Description
Predictive analytics is transforming the healthcare industry by helping doctors in informed decision-making. By analyzing the trends in health data, AI-powered tools can predict the patient's risk factors, enhance diagnostics, and even forecast potential emergencies before they occur. These advancements bring great possibilities, but they may also raise pressing ethical and practical concerns.
One of the primary challenges is to make sure that Al-generated insights should support human expertise, rather than replacing it. While healthcare has majorly relied on clinical knowledge and ethical guidelines, Al brings new debates on accountability and decision-making. For instance, what it may lead to, when an algorithm makes a flawed prediction. How can we safeguard against biases in healthcare data causing unequal treatment? How do we monitor these technologies to ensure they stay transported and accountable? This paper discusses both the advantages and challenges of predictive analytics in healthcare, stressing the importance of well-crafted regulations, ethical governance, and accountable Al development.
Predictive analytics in Healthcare
Governors
Predictive analytics is transforming the healthcare industry by helping doctors in informed decision-making. By analyzing the trends in health data, AI-powered tools can predict the patient's risk factors, enhance diagnostics, and even forecast potential emergencies before they occur. These advancements bring great possibilities, but they may also raise pressing ethical and practical concerns.
One of the primary challenges is to make sure that Al-generated insights should support human expertise, rather than replacing it. While healthcare has majorly relied on clinical knowledge and ethical guidelines, Al brings new debates on accountability and decision-making. For instance, what it may lead to, when an algorithm makes a flawed prediction. How can we safeguard against biases in healthcare data causing unequal treatment? How do we monitor these technologies to ensure they stay transported and accountable? This paper discusses both the advantages and challenges of predictive analytics in healthcare, stressing the importance of well-crafted regulations, ethical governance, and accountable Al development.
