22 Matching Annotations
  1. Jan 2022
    1. d harm patients,

      ethics

    2. seeing things that are not there.

      ethics

    3. is “machine learning” happens on such an enormous scale— human behavior is defined by countless disparate pieces of data — that it can produceunexpected behavior of its own.

      replicability, transparency

    4. coupled with often-competing financialincentives

      bias

    5. subtly changingbilling codes and other data in computer systems that track health care visits.

      transparency

    6. maximize the money coming their way.Warnings of a Dark Side to A.I. in Health CareWarnings of a Dark Side to A.I. in Health Care - The New York Times https://www.nytimes.com/2019/03/21/science/health-medicine-artificial-i...

      bias, ethics

    7. adversarial attacks

      ethics

    8. computer systems used by health care regulators, billing companies and insuranceproviders.

      ethics/bias?

    9. efficiently, and less expensively,

      equity

    10. Care

      transparency, ethics, bias, equity, replicability

    Annotators

    1. lethality

      ethics

    2. Thanks to data-sharing agreementsbetween Humetrix, the Centers forMedicare and Medicaid Services, and theHealth and Human Services Department,

      transparency

    3. Always listen to your patients before runningtests—they will tell you their diagnosi

      bias

    4. technologycan’t work without empathy for patients.

      ethics

    5. Humetrix

      equity, whose funding this? what is this corporations role and what are their incentives to do this transparency: with what permissions are this data being moved

    6. predictive logistics”

      equity, where is that information distributed? whose interest are we talking about

    7. natural-language processing

      equity; who is designing this? is it sensitive to systemic issues such as racism?

    8. 300-pagereports

      ethics/transparency seems like a violation of privacy, even if it is for 'good' intentions prevention care can have biases; who receives this care

    9. o other healthcareorganization has a pathology database of this magnitude.

      bias, with what intentions is this being used for and for what purpose? whose backing them?

    10. algorithms from some of this portfolio to help improve diagnoses and to researchnew therapeutics,” he says. “It’s akin to the human genome project in scale andlong-term importance

      replicability, how is this process replicable in other areas of study

    11. robustelectronic health records

      transparency, ethics, autonomy and privacy to pt, how is that data being used?

    12. DoctorSmarterFrom

      Transparency, Ethics, Bias, Equity, Replicability

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    Annotators