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HOSA Biomedical Debate 23-24 Exam Questions and Answers

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HOSA Biomedical Debate 23-24 Exam Questions and Answers

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  • August 6, 2024
  • 7
  • 2024/2025
  • Exam (elaborations)
  • Questions & answers
  • HOSA BIOMEDICAL DEBATE
  • HOSA BIOMEDICAL DEBATE
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HOSA Biomedical Debate 23-24 Exam
Questions and Answers
First Development of AI in healthcare - Answer -1950s- rule based AI based on
premade stuff

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

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

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

advantages - Answer -- improved diagnosis and treatment
- increased efficiency
- personalized medicine (treatment plans n shit)
- cost savings

disadvantages - Answer -- bias
- lack of human interaction
- legal and ethical concerns
- not always transparent
- implementation challenges (healthcare systems are different from each other)
- ethical concerns

steps of AI in drug r&d - Answer -1. target ID- analyze large amounts of data
2. lead discovery- ID potential drugs that can interact w target
3. lead optimization- improve the drug- AI can predict effectiveness of this shit
4. preclinical testing- AI can predict how things will turn out, making process better
5. clinical testing- AI can improve the design of the trials thru predictions
6. regulatory approval- again predicting

drug r&d companies - Answer -atomwise, insilico medicine, benevolentAI

cost savings - Answer -CDSS- unnecessary tests n shit

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