Artificial intelligence is beginning to meet (and sometimes exceed) assessments by doctors in various clinical situations. A.I. can now diagnose skin cancer like dermatologists, seizures like neurologists, and diabetic retinopathy like ophthalmologists. Algorithms are being developed to predict which patients will get diarrhea or end up in the ICU, and the FDA recently approved the first machine learning algorithm to measure how much blood flows through the heart — a tedious, time-consuming calculation traditionally done by cardiologists.
It’s enough to make doctors like myself wonder why we spent a decade in medical training learning the art of diagnosis and treatment.
There are many questions about whether A.I. actually works in medicine, and where it works: can it pick up pneumonia, detect cancer, predict death? But those questions focus on the technical, not the ethical. And in a health system riddled with inequity, we have to ask: Could the use of A.I. in medicine worsen health disparities?
There are at least three reasons to believe it might.
The first is a training problem. A.I. must learn to diagnose disease on large data sets, and if that data doesn’t include enough patients from a particular background, it won’t be as reliable for them. Evidence from other fields suggests this isn’t just a theoretical concern. A recent study found that some facial recognition programs incorrectly classify less than 1 percent of light-skinned men but more than one-third of dark-skinned women. What happens when we rely on such algorithms to diagnose melanoma on light versus dark skin?
Medicine has long struggled to include enough women and minorities in research, despite knowing they have different risk factors for and manifestations of disease. Many genetic studies suffer from a dearth of black patients, leading to erroneous conclusions. Women often experience different symptoms when having a heart attack, causing delays in treatment. Perhaps the most widely used cardiovascular risk score, developed using data from mostly white patients, can be less precise for minorities.
Will using A.I. to tell us who might have a stroke, or which patients will benefit a clinical trial, codify these concerns into algorithms that prove less effective for underrepresented groups?
Second, because A.I. is trained on real-world data, it risks incorporating, entrenching and perpetuating the economic and social biases that contribute to health disparities in the first place. Again, evidence from other fields is instructive. A.I. programs used to help judges predict which criminals are most likely to reoffend have shown troubling racial biases, as have those designed to help child protective services decide which calls require further investigation.
In medicine, unchecked A.I. could create self-fulfilling prophesies that confirm our pre-existing biases, especially when used for conditions with complex trade-offs and high degrees of uncertainty. If, for example, poorer patients do worse after organ transplantation or after receiving chemotherapy for end-stage cancer, machine learning algorithms may conclude such patients are less likely to benefit from further treatment — and recommend against it.
Finally, even ostensibly fair, neutral A.I. has the potential to worsen disparities if its implementation has disproportionate effects for certain groups. Consider a program that helps doctors decide whether a patient should go home or to a rehab facility after knee surgery. It’s a decision imbued with uncertainty but has real consequences: Evidence suggests discharge to an institution is associated with higher costs and higher risk of readmission. If an algorithm incorporates residence in a low-income neighborhood as a marker for poor social support, it may recommend minority patients go to nursing facilities instead of receive home-based physical therapy. Worse yet, a program designed to maximize efficiency or lower medical costs might discourage operating on those patients altogether.
To some extent, all these problems already exist in medicine. American health care has always struggled with income- and race-based inequities rooted in various forms of bias. The risk with A.I. is that these biases become automated and invisible — that we begin to accept the wisdom of machines over the wisdom of our own clinical and moral intuition. Many A.I. programs are black boxes: We don’t know exactly what’s going on inside and why they produce the output they do. But we may increasingly be expected to honor their recommendations.
In my practice, I’ve often seen how any tool can quickly become a crutch — an excuse to outsource decision making to someone or something else. Medical students struggling to interpret an EKG inevitably peek at the computer-generated output at the top of the sheet. I myself am often swayed by the report provided alongside a chest X-ray or CT scan. As automation becomes pervasive, will we catch that spell-check autocorrected “they’re” to “there” when we meant “their”?
Still, A.I. holds tremendous potential to improve medicine. It may well make care more efficient, more accurate and — if properly deployed — more equitable. But realizing this promise requires being aware of the potential for bias and guarding against it. It means regularly monitoring both the output of algorithms and the downstream consequences. In some cases, this will necessitate counter-bias algorithms that hunt for and correct subtle, systematic discrimination.
But most fundamentally, it means recognizing that humans, not machines, are still responsible for caring for patients. It is our duty to ensure that we’re using AI as another tool at our disposal — not the other way around.
Dhruv Khullar (@DhruvKhullar) is a doctor at NewYork-Presbyterian Hospital, an assistant professor in the departments of medicine and health care policy at Weill Cornell Medicine, and director of policy dissemination at the Physicians Foundation Center for the Study of Physician Practice and Leadership.
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【两】【位】【老】【爷】【子】【的】【对】【话】【告】【一】【段】【落】。 【虽】【然】**【仟】【已】【原】【谅】【了】【俞】【承】【德】【的】【无】【心】【之】【失】，【但】【他】【还】【是】【有】【些】【尴】【尬】。 【为】【了】【缓】【解】【这】【份】【尴】【尬】，【俞】【承】【德】【连】【忙】【向】【林】【依】【依】【汇】【报】【这】【次】【拍】【卖】【的】【所】【得】【及】【后】【续】【安】【排】。 【还】【有】【这】【次】【拍】【卖】，【对】【他】【们】【腾】【龙】【旗】【下】【汇】【圆】【拍】【卖】【场】【的】【声】【誉】【有】【多】【大】【影】【响】。 【林】【依】【依】：“【那】【说】【好】【拍】【卖】【结】【束】【后】【给】【来】【宾】【们】【的】【意】【外】【惊】【喜】【呢】？” 【俞】
【孟】【语】【没】【答】【话】，【水】【盈】【盈】【的】【眼】【睛】【望】【着】【他】，【唇】【上】【有】【潋】【滟】【水】【光】，【清】【纯】【与】【娇】【媚】【完】【美】【地】【揉】【合】【在】【一】【起】，【像】【个】【妖】【精】。 【有】【种】【说】【不】【出】【的】【陌】【生】【感】【觉】【席】【卷】【温】【淮】【砚】【的】【身】【心】，【他】【喉】【结】【下】【意】【识】【上】【下】【滚】【动】【了】【一】【圈】。 “【淮】【砚】【哥】【哥】……” 【他】【抿】【了】【下】【干】【燥】【的】【唇】，【注】【视】【着】【她】：“【嗯】。” 【她】【伸】【出】【手】，【拉】【住】【他】【修】【长】【微】【凉】【的】【手】：“【你】【坐】。” 【温】【淮】【砚】【刚】【坐】
【人】【道】【长】【河】【渐】【渐】【壮】【大】，【流】【淌】【在】【整】【个】【宇】【宙】【之】【间】，【渐】【渐】【的】【从】【中】【生】【出】【一】【股】【吸】【力】，【从】【没】【有】【在】【长】【河】【中】【显】【化】【的】【生】【灵】【体】【内】【吸】【取】【一】【道】【道】【生】【命】【烙】【印】，【化】【入】【长】【河】【之】【中】，【成】【为】【浩】【荡】【人】【道】【的】【一】【份】【子】。 【众】【大】【能】【有】【的】【毫】【不】【抵】【挡】，【任】【由】【自】【己】【显】【化】【在】【人】【道】【之】【中】，【有】【的】【则】【主】【动】【进】【入】【长】【河】【之】【中】，【帮】【住】【嬴】【政】【推】【动】【人】【道】【回】【溯】，【有】【的】【则】【抗】【拒】，【不】【过】【随】【着】【吸】【引】【力】【越】【来】【越】【强】20016香港开马93期开马现场【晨】。 【草】【叶】【摩】【擦】【的】【触】【感】，【搓】【着】【后】【背】。 【好】【像】【是】【草】？ 【付】【小】【车】【在】【睡】【梦】【中】【想】【着】，【过】【了】【一】【会】【儿】，【草】【继】【续】【揉】【搓】【着】【他】【的】【后】【背】， 【他】【最】【终】【缓】【缓】【从】【懵】【懂】【中】【醒】【来】，【抓】【着】【那】【东】【西】【一】【拧】，【只】【见】【一】【搓】【草】【拧】【在】【眼】【前】，【附】【近】【是】【原】【野】。 “【原】【来】【是】【草】？” 【轻】【哼】【一】【声】，【付】【小】【车】【又】【迷】【迷】【糊】【糊】【的】【看】【着】【周】【围】，【双】【眼】【眯】【缝】【的】【擦】【了】【擦】。 【很】【快】【他】【又】【一】
“【警】【察】！” 【骑】【手】【大】【喊】【一】【声】，【摘】【下】【头】【盔】，【露】【出】【帅】【气】【的】【脸】【庞】，【三】【七】【分】【发】【型】，【整】【个】【人】【的】【眉】【宇】【之】【间】，【看】【上】【去】【透】【露】【出】【一】【股】【子】【正】【义】【之】【气】：“【警】【察】【办】【案】，【都】【别】【动】！” 【正】【是】【消】【失】【已】【久】【的】【钟】【天】【正】。 “【都】【别】【动】！” 【从】【机】【车】【后】【座】【下】【来】【的】【人】【同】【样】【摘】【下】【头】【盔】，【甩】【了】【甩】【小】【马】【尾】，【娇】【喝】【一】【声】：“【双】【手】【抱】【头】【蹲】【下】！” 【正】【是】【钟】【天】【正】【的】【黄】【金】
【当】【其】【它】【六】【方】【势】【力】【之】【主】【赶】【来】【之】【时】，【具】【是】【被】【眼】【前】【所】【见】【的】【境】【况】【镇】【住】【了】。 【多】【少】【时】【间】？【从】【得】【到】【消】【息】【到】【赶】【到】【这】【里】，【绝】【对】【不】【会】【超】【过】【一】【盏】【茶】【的】【时】【间】，【可】【眼】【前】【的】【是】【什】【么】？ 【整】【个】【青】【门】【的】【驻】【地】【破】【败】【一】【片】，【血】【迹】【渲】【染】【了】【青】【石】【铺】【就】【的】【地】【面】，【空】【气】【中】【散】【发】【着】【新】【鲜】【的】【血】【腥】【味】，【还】【没】【有】【发】【臭】。 【不】【过】【这】【都】【不】【是】【重】【点】，【无】【论】【是】【青】【门】【驻】【地】【的】【残】【像】，【还】【是】
【榻】【上】【二】【人】【赤】【条】【条】【相】【拥】，【正】【情】【话】【绵】【绵】，【刘】【星】【儿】【依】【然】【未】【从】【羞】【涩】【中】【走】【出】。 【电】【话】【突】【然】【响】【起】，【罗】【康】【安】【隔】【空】【抓】【来】【衣】【服】，【摸】【出】【手】【机】【一】【看】，【见】【是】【姚】【先】【功】【打】【来】【的】，【立】【刻】【意】【识】【到】【了】【不】【对】，【赶】【紧】【侧】【身】【接】【通】【在】【耳】【边】，【问】：“【怎】【么】【了】？” 【姚】【先】【功】：“【还】【没】【好】【吗】？【快】【走】，【丁】【兰】【来】【了】，【已】【经】【到】【坡】【上】【了】。” “……”【罗】【康】【安】【一】【阵】【无】【语】，【怎】【么】【就】【回】