SwedenRuìdiǎn瑞典large-scaledàxíng大型study / researchyánjiū研究to find / to discoverfāxiàn发现artificial intelligenceréngōng zhìnéng人工智能can / is able tokě可through / viatōngguò通过routine / standardchángguī常规healthjiànkāng健康datashùjù数据to identify / to detectshíbié识别melanomahēisè sù liú黑色素瘤high-riskgāo fēngxiǎn高风险population / peoplerénqún人群。
Sweden
Ruìdiǎn
瑞典
large-scale
dàxíng
大型
study / research
yánjiū
研究
to find / to discover
fāxiàn
发现
artificial intelligence
réngōng zhìnéng
人工智能
can / is able to
kě
可
through / via
tōngguò
通过
routine / standard
chángguī
常规
health
jiànkāng
健康
data
shùjù
数据
to identify / to detect
shíbié
识别
melanoma
hēisè sù liú
黑色素瘤
high-risk
gāo fēngxiǎn
高风险
population / people
rénqún
人群
one / a (measure word for studies)
yī xiàng
一项
from / originating from
láizì
来自
Sweden
Ruìdiǎn
瑞典
(structural particle)
de
的
large-scale
dàxíng
大型
study
yánjiū
研究
to find / to discover
fāxiàn
发现
using / making use of
lìyòng
利用
routine / standard
chángguī
常规
medical
yīliáo
医疗
data
shùjù
数据
trained / to train
xùnliàn
训练
(structural particle)
de
的
artificial intelligence
réngōng zhìnéng
人工智能
model
móxíng
模型
can
kě
可
accurately
zhǔnquè
准确
to identify
shíbié
识别
melanoma
hēisè sù liú
黑色素瘤
high-risk
gāo fēngxiǎn
高风险
patient
huànzhě
患者
performance
biǎoxiàn
表现
far superior to
yuǎn yōu yú
远优于
traditional / conventional
chuántǒng
传统
assessment / evaluation
pínggū
评估
method
fāngfǎ
方法
April 16, 2026 at 5:00 PM
one / a (measure word)
yī xiàng
一项
from / originating from
láizì
来自
Sweden
Ruìdiǎn
瑞典
(structural particle)
de
的
large-scale
dàxíng
大型
study
yánjiū
研究
to find
fāxiàn
发现
artificial intelligence
réngōng zhìnéng
人工智能
can
kě
可
through / via
tōngguò
通过
to analyze
fēnxī
分析
patient
huànzhě
患者
(structural particle)
de
的
routine / standard
chángguī
常规
medical
yīliáo
医疗
data
shùjù
数据
effectively
yǒuxiào
有效
to identify
shíbié
识别
out / (result complement)
chū
出
to suffer from
huàn
患
melanoma
hēisè sù liú
黑色素瘤
(structural particle)
de
的
high-risk
gāo fēngxiǎn
高风险
population / people
rénqún
人群
melanoma
hēisè sù liú
黑色素瘤
is
shì
是
most
zuì
最
dangerous
wēixiǎn
危险
(structural particle)
de
的
skin cancer
pífū ái
皮肤癌
type
lèixíng
类型
one of
zhī yī
之一
early stage
zǎoqī
早期
to detect / to discover
fāxiàn
发现
for
duì
对
to improve / to raise
tígāo
提高
patient
huànzhě
患者
(structural particle)
de
的
survival rate
cúnhuó lǜ
存活率
crucial / of vital importance
zhì guān zhòng yào
至关重要
research
yánjiū
研究
team
tuánduì
团队
to use / to employ
shǐyòng
使用
machine learning
jīqì xuéxí
机器学习
model
móxíng
模型
on
duì
对
patient
huànzhě
患者
(structural particle)
de
的
age
niánlíng
年龄
medical history
bìngshǐ
病史
and
hé
和
routine
chángguī
常规
examination / test
jiǎnchá
检查
result
jiéguǒ
结果
and other / etc.
děng
等
data
shùjù
数据
to conduct / to carry out
jìnxíng
进行
(completion marker)
le
了
systematic
xìtǒng
系统
analysis
fēnxī
分析
result
jiéguǒ
结果
to show / to reveal
xiǎnshì
显示
artificial intelligence
réngōng zhìnéng
人工智能
model
móxíng
模型
(structural particle)
de
的
predictive
yùcè
预测
accuracy
zhǔnquè xìng
准确性
far / greatly
yuǎn
远
superior to
yōu yú
优于
traditional / conventional
chuántǒng
传统
(structural particle)
de
的
risk
fēngxiǎn
风险
assessment / evaluation
pínggū
评估
method
fāngfǎ
方法
research
yánjiū
研究
personnel / researchers
rényuán
人员
to believe / to consider
rènwéi
认为
this
zhè xiàng
这项
technology
jìshù
技术
to help / to contribute to
yǒu zhù yú
有助于
doctor / physician
yīshēng
医生
in / at
zài
在
earlier
gèng zǎo
更早
stage
jiēduàn
阶段
to identify / to detect
fāxiàn
发现
high-risk
gāo wēi
高危
patient
huànzhě
患者
and
bìng
并
in advance / early
tíqián
提前
to arrange
ānpái
安排
dermatology / skin clinic
pífū kē
皮肤科
examination
jiǎnchá
检查
this
zhè yī
这一
finding / discovery
fāxiàn
发现
for / provides
wèi
为
artificial intelligence
réngōng zhìnéng
人工智能
in
zài
在
cancer
áizhèng
癌症
screening
shāichá
筛查
in / within
zhōng
中
(structural particle)
de
的
wide / extensive
guǎngfàn
广泛
clinical
línchúang
临床
application
yìngyòng
应用
to provide
tígōng
提供
(completion marker)
le
了
new
xīn de
新的
strong / compelling
yǒulì
有力
evidence
zhèngjù
证据
relevant / related
xiāngguān
相关
research
yánjiū
研究
paper / article
lùnwén
论文
has been (published) in
yǐ yú
已于
recently / in recent days
jìn rì
近日
in
zài
在
international
guójì
国际
medical / medicine
yīxué
医学
journal
qīkān
期刊
on / in (a publication)
shàng
上
formally / officially
zhèngshì
正式
to publish
fābiǎo
发表
research
yánjiū
研究
personnel / researchers
rényuán
人员
to state
biǎoshì
表示
future
wèilái
未来
similar
lèisì
类似
(structural particle)
de
的
artificial intelligence
réngōng zhìnéng
人工智能
tool
gōngjù
工具
is expected to / hopefully will
yǒuwàng
有望
to be (passive marker)
bèi
被
to integrate
zhěnghé
整合
into
jìn
进
routine / standard
chángguī
常规
cancer
áizhèng
癌症
screening
shāichá
筛查
process / workflow
liúchéng
流程
within / into
zhī zhōng
之中
New Words
jìngjìng
竞争
competition
jiānguǎn
监管
regulation / oversight
yìngyòng shāngdiàn
应用商店
app store
zhīfù
支付
payment
dì sān fāng
第三方
third party
shìchǎng quánlì
市场权力
market power
xiāofèizhě
消费者
consumers
gōngpíng
公平
fair
kāifāzhě
开发者
developers
diàochá
调查
investigation
News in English
Lifestyle & CultureSwedish Study Finds AI Can Identify High-Risk Melanoma Patients Using Routine Health Data
A large Swedish study found that AI models using routine medical records can accurately identify patients at high risk of melanoma, significantly outperforming traditional risk assessment methods.
The European Union's competition regulator has launched an investigation into Apple's App Store practices regarding the handling of digital payment methods.
Authorities are examining whether Apple has violated fair competition regulations through its App Store policies.
The investigation focuses on whether Apple imposes excessive restrictions on third-party payment options and how it treats developers.
This regulatory action reflects growing global concerns about the market power of major technology platforms.
The EU is committed to ensuring fair digital market competition and protecting consumer interests in the technology sector.
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