Process Framework For The Ethical Deployment Of Artificial Intelligence In Oncology
Process Framework For The Ethical Deployment Of Artificial Intelligence In Oncology
is an that uses to at an over a range of , the of to tasks that would human of . The use of AI in care is : a May 2022 of the term cross- with 26,000 , with more than 60% in the past five years. for AI in , , and ; the roles of human- and -based ; and the - .to other parts of , the of AI are , and some are . AI tools apply to not one but two ; can the of bias, , and in care; and can and , to care that is than -. These , in the of for AI, a where AI is both and .We that a - for AI in care, such as the for , can these and a where AI is .Copy the link or click the original link to download the original SCI text
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(J Clin Oncol, IF: 50.717)
(AI) is an that uses to at an over a range of , the of to tasks that would human of . The use of AI in care is : a May 2022 of the term cross- with 26,000 , with more than 60% in the past five years. for AI in , , and ; the roles of human- and -based ; and the - .
to other parts of , the of AI are , and some are . AI tools apply to not one but two ( and ); can the of bias, , and in care; and can and , to care that is than -. These , in the of for AI, a where AI is both and .
We that a - for AI in care, such as the for (A4R), can these and a where AI is .
Artificial intelligence (AI) is an emerging technology that uses sophisticated algorithms to draw results in a range of situations, leveraging the capabilities of computer systems to perform tasks that usually require a level of human intelligence. The application of artificial intelligence in cancer treatment is rapidly expanding: in May 2022, searches for cancer-related terms showed about 26,000 citations, with more than 60% of which were published in the past five years. Ethical considerations of artificial intelligence in the field of oncology include patient equity, privacy, and autonomy; roles based on human and machine judgment; and relationships between patients and oncologists. Compared with other parts of medicine, the impact of tumor artificial intelligence is huge, and some are special. Tumor AI tools are not only suitable for one, but two genomes (germline and somatic cell lines); they can greatly complicate existing biases, discrimination and structural racism in cancer treatment; and they can subtly weaken the autonomy of patients and doctors, resulting in cancer treatment algorithm-centered rather than patient-centered. Against the backdrop of unreserved enthusiasm for AI, these different concerns challenge the broadly implemented and ethically acceptable future of tumor AI. We suggest that a process-centric approach to deploying AI in cancer treatment, such as the Rational Framework (A4R) Accountability, can address these issues and enable a future of ethically deploying AI oncology.
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