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HOSA Biomedical Debate 23-24 Questions and All Correct Answers

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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...

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  • August 8, 2024
  • 8
  • 2024/2025
  • Exam (elaborations)
  • Questions & answers

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  • hosa biomedical debate 23 24 questions and all cor
  • hosa biomedical debate 23 24 stuvia
  • first development of ai in healthcare answer 195
  • deep learning answer a type of machine learning

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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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