Artificial Intelligence Issues, For over two decades, I have worked as a health sciences educator and researcher. 바카라사이트
I currently lead a research team that employs data science technologies to comprehend and forecast the impact of educational innovations on learner progress
Perceptions, and outcomes. Our work incorporates the most recent disruptive innovation in education: artificial intelligence (AI).
AI is a broad theory and practice that includes machine learning, deep learning, and neural networks (also referred to as artificial neural networks)
The term “artificial intelligence” was coined in 1956 by a Dartmouth Summer Research Project working group led by John McCarthy.
Goal was to create common understandings about thinking machines.
McCarthy et al proposed that “every aspect of learning or any other feature of intelligence
Can in principle be so precisely described that a machine can be made to simulate it.
Marr compiled a list of definitions culled from the English Oxford Living Dictionary
Merriam-Webster, Encyclopedia Britannica, Amazon, Google, Facebook, and IBM.
AI is rapidly gaining traction in higher education. Nonetheless, there are critical ethical and educational issues
That must be confronted and addressed in curriculum content and our general education.
Consider the numerous examples of AI in daily life, such as biometrics (e.g., facial recognition)
Personalized/targeted ads, smart home devices, first-level customer response systems
(such as when we click on the ‘chat now’ option on many company websites), and much more.
Lack of an agreed-upon definition of AI reflects the field’s rapid and broad expansion.
The spread of AI has far outpaced our ability to establish solid ontological, theoretical, and ethical foundations.
AI works by analyzing a large set of data, referred to as training data, in the system.
These data sets are parsed by deep neural networks within AI applications to identify patterns.
Deep learning models programmed into AI are intended to mimic the structures of the human brain, linking layers of code in the same way that neurons are linked. 카지노사이트
The training data constitute the vast majority of the available data for the analysis, with a small portion reserved as validation data.
Resulting model (or models) is fine-tuned using the validation data set before being tested for bias using a test data set.
Issues and Opportunities in AI in Health Science Education
AI has spread rapidly in higher education, with some intriguing innovations in the health sciences in particular.
Because of AI, patient simulators are becoming more fidelity and complex.
AI is being rapidly deployed and studied in diagnostics, medical record analysis, and exceptionally large epidemiologic studies, to name a few applications.
There are numerous AI-related initiatives at the National Institutes of Health.
The National Cancer Institute, for example, has identified four emerging applications of AI in oncology: screening and diagnosis, tumor genomic characterization
Drug discovery, and cancer surveillance.
In the content of curricula and in our general use of these technologies in health science education
There are critical ethical and educational issues that must be confronted and addressed.
We are falling behind and must quickly catch up. In my work, I believe that the most pressing issue is social/racial bias.
Patterns of social and racial bias in AI have received a lot of attention recently.
Timnit Gebru, an AI ethics researcher, claimed she was fired from Google for raising concerns about the technology’s societal impacts and criticizing the company’s diversity efforts.
Gebru’s departure was followed by a string of high-profile departures in protest of the circumstances surrounding her divorce.
“The problem is, training data sets may lack enough data from minority groups, reflect historical inequities such as lower salaries for women, or inject societal bias
As in the case of Asian-Americans being labeled foreigners,” Patel explained. Models that learn from biased training data will propagate the same biases.”
The good news is that we have options when it comes to how we use AI.
AI can either reproduce bias or be used to identify bias and inform strategies for resolving it.
A few specific ideas for applications of AI to identify bias in health science education include examining course and instructor evaluation data
(including natural language analysis of open-ended items), assessing the representativeness of patient mixes and health needs in clinical training
And testing for differences in care decisions made by students related to different patient groups.
In each example, changes can be implemented based on the learnings.
Faculty evaluation systems, selection of rotation sites, and curricula related to implicit bias can be targeted to reduce bias.
It is also essential for higher education to raise its consciousness about the presence of bias in data before any use of AI.
There are significant risks associated with these systems, and their seeming inevitability (as is often the case with powerful technologies)
Means it is incumbent on us to learn how these systems work, how to benefit from them, and how to avoid their flaws. 카지노 블로그