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HOSA Biomedical Debate 2024 Article Questions and Answers
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HOSA Biomedical Debate 2024 ArticleQuestions and Answers
Addressing Biased Software - -The method of embracing diversity in
development teams to mitigate biased data input and technological artifacts,
ensuring fair and inclusive AI solutions.
-Administrative inefficiencies - -Ineffective or wasteful management
processes
-AI Algorithms for Clinical Interpretation - -The challenge of validating AI
algorithms in clinical trials, ensuring their accuracy, reliability, and regulatory
compliance for clinical interpretation.
-AI assistance in next 1-3 years - -AI helping with symptom checking and
triage for providers
-AI data flow improvement - -AI recognizing and processing both structured
and unstructured data
-AI Implementation Conclusion - -The prediction that AI will expand swiftly
and become vital globally in healthcare systems, emphasizing its significant
technical developments and widespread integration.
-AI Integration in Medical Education - -The emphasis on integrating AI
across undergraduate, postgraduate, and specialization phases of medical
education to prepare professionals for AI adoption.
-AI investment impact - -Organizations left behind if not investing in AI
-AI role in diagnosing kidney disease - -AI using tools like natural language
processing to extract insights
-AI-based Medications in Clinical Trials - -The obstacle of insufficient
empirical data for successful deployment of AI-based treatments, affecting
regulatory approval and technological development.
-AI's help in symptom checking and triage - -AI's help for providers in the
next one to three years: Symptom checking and triage
-AI's role in improving data flow - -According to Natalie Schibell, AI's role in
improving data flow involves recognizing and processing both structured and
unstructured data
, -AI's role in targeted diagnostics and personalized care - -Aspect of AI
emphasized by Dr. Taha Kass-Hout in enabling targeted diagnostics and
personalized care: Structuring unstructured data
-Amazon HealthLake - -Amazon service enabling searching and querying of
unstructured data
-Americans' concern about AI in health care - -The percentage of Americans
concerned about health care providers moving too fast with AI
implementation: 37%, 75%
-Artificial Intelligence (AI) in Healthcare - -The use of AI to decrease
complexity, simplify healthcare systems, and advance medical research,
addressing various challenges and opportunities in the industry.
-Bayesian Modeling - -An AI algorithm suitable for assessing multimodal
issues in healthcare, utilizing probabilistic methods for decision-making and
analysis.
-Belief in AI reducing mistakes - -Percentage of Americans believing AI in
health and medicine would reduce the number of mistakes: 40%
-Black-box Problem - -The lack of explanation for forecasts made by deep
learning algorithms, posing challenges in understanding and validating AI-
based decisions.
-Chronic illness - -Long-term medical condition requiring ongoing
management
-Comfort with AI in health care - -The percentage of Americans comfortable
with AI in their own health care: 46%
-Conversational AI use case - -Employing AI for automating scheduling in
healthcare
-Convolutional neural network (CNN) - -Network used to analyze images
from smartphones or tablets
-COVID-19 Impact - -The pandemic that has stressed healthcare systems
globally, revealing flaws in accessibility, treatment availability, and cost
management.
-COVID-19 impact on digital transformation - -Accelerating digital
transformation, including AI adoption
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