HOSA Biomedical Debate 23-24 Questions and All Correct Answers
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HOSA Biomedical Debate
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HOSA Biomedical Debate
HOSA Biomedical Debate 23-24 Questions and All Correct Answers HOSA Biomedical Debate 23-24 Questions and All Correct Answers HOSA Biomedical Debate 23-24 Questions and All Correct Answers HOSA Biomedical Debate 23-24 Questions and All Correct Answers
First Development of AI in healthcare - ANSWE...
HOSA Biomedical Debate 23-24 Questions
and All Correct Answers
First Development of AI in healthcare - ANSWER-1950s- rule based AI based on premade stuff
deep learning - ANSWER-A type of machine learning that uses artificial neural networks to analyze
large datasets- can ID complex patterns and predict with high accuracy
medical imaging - ANSWER-limited by ability of radiologists to interpret complesx images. AI can
improve
Lack of Standardized data - ANSWER-many healthcare systems use different data formats so they
cannot be shared thru AI to analyize large datasets
drug r&d companies - ANSWER-atomwise, insilico medicine, benevolentAI
cost savings - ANSWER-CDSS- unnecessary tests n shit
challenges of AI - ANSWER-data quality and availability
integration with existing systems
liability and security
user acceptance and adoption
Neural networks - ANSWER-type of machine learning that uses interconnected nodes to analyze data
and ID patterns, ex) QMR, quick medical reference system in the late 1980s
support vector machines - ANSWER-a type of machine learning algorithm that can be used for
classification and regression analysis ex) diagnosis of breast cancer and detection of alzheimers and
shit
rule based systems use - ANSWER-diagnosing heart disease by analyzing electrocardigram data- rules
can define the patterns and abnormalities in the ECG signals
, rule based system advantage - ANSWER-transparent and easily updated and modified to adapt
rule based system limitations - ANSWER-heavily rely on the accuracy and completeness of the rules,
so if there are any weird shit in the rule, they will give incorrect info- cannot handle ambiguity, so
cannot work in complex situations
robotic process automation - ANSWER-automates repetitive rule based tasks- only the super tedious
shit- used to streamline administrutive tasks
rpa limitations - ANSWER-any changes in underlying systems or interfaces may require updates to
the rpa workflows which can introduce additional maintenance overhead
machine learning - ANSWER-training algorithms on large datasets to ID patterns and make
predictions. can be used for more shit
machine learning uses - ANSWER-computer aided diagnosis for interpreting medical images like xrays
and mris and also can be used in drug discovery by analyzing large datasets of biological and
chemical info to ID potential drug targets and predict the likelihood of success for specific drug
candidates
Natural Language Processing (NLP) uses - ANSWER-analysis of clinical notes to ID potential adverse
event or side effects associated with specific treatments- can ID potential safety issues- can help with
accuracy of coding and billing (FRAUD YAY)
robotic systems - ANSWER-robots used for surgical procedures and patient monitoring and rehab and
shit
expert system - ANSWER-replicate decision making capabilities of human experts
expert system uses - ANSWER-cilinical decision support systes to provide healthcare providers with
real time recs and alerts based on patient data
generative AI- like chatgpt- uses - ANSWER-make new images to get bigger dataset, discovering new
drugs
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