2026年临床人工智能（AI）发展状况研究报告（英文版）-

State

ofClinicalAI

Report2026ARISE-AI.ORGJanuary,20261Jonathan

HChenDr.

Jonathan

H

Chen

is

Stanford’s

inaugural

Directorfor

Medical

EducationinAIinthe

Divisionof

Computational

Medicine.

His

expertise

combining

human

with

artificialintelligence

toprovide

better

healthcare

than

either

alone

is

featured

in

the

popular

press

withover100publications

and

awards.ARISE-AI.ORGEthan

GohDr.

Ethan

Goh

is

the

Executive

DirectorofARISE.

His

research

has

beenfeatured

inThe

New

York

Times,

The

WashingtonPost,andCNN.

He

directstheStanfordHealthcareAI

Leadership

Program,

and

Harvard’s

Agentic

AI

Executive

Course.

Dr.

Goh

is

a

Founding

Editorial

Board

member

andAssociate

Editorat

BMJ

DigitalHealth

&AI.Peter

BrodeurDr.

Peter

Brodeur

is

a

rising

cardiologyfellowat

Harvard

Medical

School’sBethIsrael

Deaconess

MedicalCenter.

Dr.

Brodeur

is

an

affiliate

ofARISE,

reviewer

forNature

Medicine&

NEJM

AI,

and

former

life

sciences

strategyconsultant.

His

research

focuses

on

human

computer

interaction

and

LLM

clinical

reasoning.Adam

RodmanDr.

Adam

Rodman

is

an

assistant

professor

at

Harvard

Medical

School.

He

isthe

Director

of

AI

Programs

for

theCarlJ.Shapiro

Center.

Dr.

Rodman

isan

Associate

Editor

at

NEJMAI.

He

is

also

thehostoftheAmerican

College

of

Physicians

podcast

Bedside

Rounds.AboutTheAuthorsPage

#Page2Team

Name

Page#“Therearedecadeswherenothinghappens;andthereareweekswhendecadeshappen.”Recentdeploymentsbytechnologycompanies,health

systems,

and

regulators

have

madeclinicalAI

morevisibleandever

moreconsequential.Atthesametime,

it

has

becomeharderto

keepupwithemergingresearch.

In

some

areas

the

literature

isfragmented;

inothers,

itsimplydoesn’texistyetforthewaythesetoolsare

being

used

today.Sowhatactuallyholds

up

in

practice?TheStateof

Clinical

AIReport(2026)wascreatedto

look

beyond

model

performance

alonetoothercriticalfactorsthatdeterminereal-world

impact:

howsystems

areevaluated,howcliniciansandAIworktogether,andwhere

patient

risksstartto

appear.FrontierAIsystemsarealready

powerful.What’s

needed

now

istosafelyand

effectively

translatethesetoolsinto

real-worldcare.EthanGoh,Adam

Rodman,Jonathan

HChenInvestigators,ARISE

Network

ARISE-AI.ORGMessage

FromARISE

LeadershipPage

33StanfordComputationalMedicineColloquia●

HealthcareAIseminarswithStanford/industry

leaders●

Thursday

12pm

PT,freeStanford

Healthcare

AI

Leadership&StrategyProgram●

Application

required.CME

andaccreditedcertificate●May

2026GenerativeAIand

AgenticAIOnline

Course●

Harvard/Stanfordfaculty,

accreditedcertificate●

Summer2026Engagementand

EducationGetweeklyinvitesApply

nowGetearlyaccessARISE-AI.ORGPage4Page

#4ClinicalAI

IsWidely

Deployed

But

Poorly

Evaluated●AI

isnowembedded

across

health

care:

1,200+

FDA-clearedtools

and

350,000+

consumer

apps

have

generateda$70B

market1

.Onlyaminority

underwent

peer-reviewedevaluation.2●Of

691

FDA-cleared

AI/ML

medical

devices

(1995–2023),

>95%went

through

the

510(k)

clearancepathway,which

is

predicatedonequivalencytoexistingdevices

—many

of

which

were

approved

on

suboptimalevidence.2●~50%of

FDAdevice

summaries

omitted

study

design,

53%

lacked

sample

size,

and

<1%

reported

patientoutcomes.2●95%ofdevicesummaries

did

not

report

demographic

data,

and

91%

lacked

bias

assessments,

raising

concernsaboutsafetyandequity

in

real-world

use.2Bridging

the

gap

between

adoption

and

evidence

requires

supporting

clinicians,health

system

leaders,policymakers,andthe

publicininterpretingavailableresearch.ARISE-AI.ORGPageP#age

#TheCurrent

LandscapePage

5TopTakeaways1.Modelcapability

is

accelerating,

but

evidence

of

real

clinical

impact

remainslimited.Manystudiesshowwhat

modelscando

in

controlled

settings;what’sincreasinglyneededareprospectivestudiesthatshow

measurableeffects

on

patient

outcomesandcare

delivery.2.FrontierLLM

models

showvery

uneven

performance.They

perform

extremely

well

oncomplexreasoningtasks,yet

breakdownwhenuncertainty,

missing

information,

or

changingcontext

is

introduced.3.Clinicians

value

automation

where

it

reduces

administrative

and

workflow

burden,

buttheseusecasesremainunderstudied.Tasksclinicians

mostwant

support

with

areoftenunderrepresentedincurrent

benchmarks

and

evaluations.ARISE-AI.ORGPage

#Page

6TopTakeaways4.Patient-facing

AI

has

significant

potential

to

reshape

engagement

and

access,

butraisesdistinctsafetyconcerns.

Direct

interactionwith

patients

requires

muchstronger

guardrailsandscalableoversightsystemsthatdo

notcurrently

exist.5.Multimodal

clinical

AI

applications

are

approaching

practical

usability.Improvements

inbasemodelsareenablingapplicationsthat

integrate

unstructuredtext,

images,andotherclinicaldatatosupport

predictionand

decision-making

in

real-world

settings.6.FDA

clearance

is

increasing,

but

near-term

clinical

adoption

will

favor

narrow,

task-specificsystems.AItoolsthataretightlyscopedtospecificdomains

andcontextsaremore

likelytodemonstratevalueand

beadopted

in

practice.ARISE-AI.ORGPage

#Page

7Rebecca

HandlerKathleen

LacarJason

HomKameron

BlackEric

HorvitzLiam

McCoyLauraZwaanDavid

WuVishnu

RaviPriyankJainBrian

HanEmily

TatKevinSchulmanAdrian

HaimovichThe

organization

format

of

this

report

was

inspired

by

Nathan

Benaich’s

State

of

AIReport.ARISE-AI.ORGDesign&AccessibilityEmily

TatAcknowledgementsSupported

ByReviewersPage

#Page

8HowtoCiteThis

ReportPeterG.

Brodeur,

EthanGoh,

EmilyTat,

Liam

McCoy,

DavidWu,

PriyankJain,

RebeccaHandler,Jason

Hom,

LauraZwaan,Vishnu

Ravi,

Brian

Han,

KevinSchulman,

Kathleen

Lacar,

Kameron

Black,Adrian

Haimovich,

Eric

Horvitz,Adam

Rodman,Jonathan

H.Chen

“StateofClinical

AI2026,”ARISE

Network,January2026.ARISE-AI.ORGPage

#Page

9IntroductionTeam

Name本报告来源于三个皮匠报告站（）,由用户Id:349461下载,文档Id:1102014,下载日期:2026-02-04Page

1010Page

#ModelPerformance●Frontierreasoning

models(optimizedformulti-step

inferenceand

chainofthought)

showed

marked

improvement

on

challengingclinical

reasoningtasksagainst

humanbaselineswhile

prediction

modelscrossed

newthresholds

in

scalable

predictiontoenableactionable

prevention.●Dominantfailure

modes

include

model

recognitionofuncertainty,overconfidence,and

pattern

learning.●Benchmarks&EvaluationMultiplechoicebenchmarksaresaturatedandevaluations

still

underrepresent

real

clinicalwork:

administrative

tasks,

conversationaldialogue,realpatientdata,

and

bias/fairness.●New

benchmarksuites

(e.g.,conversational,simulated

EHRenvironments)areforcing

models

into

more

realistic,

dynamicscenarios.Foundational

MethodsNoveltechniquessuchasconverting

medicaldatatotokens

usedfor

prediction

brings

a

new

era

ofscreening

and

risk

stratification.●●Clinical

AI

is

beingadvanced

by

multiagentsystems,multimodaldiagnosticsupport,and

optimizing

reasoning

models.ExecutiveSummaryPage

11ARISE-AI.ORGPage

#●AIin

ClinicalWorkflowsAcrosssettings,

AIcanaugmentclinicianson

reasoningand

diagnostic

interpretationtasks.

However,

collaboration

isn’tyetoptimized.

Howclinicians

use

AI

isas

importantaswhatthe

modelcan

do.●Workflowtoolslike

AIscribesfeeltransformative,yetobjective

gainsare

still

modest.

The

addition

of

downstream

workflowtaskswilllikelyyield

more

productivityandefficiency

impact.Patient

FacingAI●Multi-turnconversationalagentsand

AI-basedcoachingshow

promise,

particularlyasthey

are

integrated

with

smart

devicestosupportmore

personalized

healthassistance.●Inaspacewithcompetingvendorinterests,overtrust

and

unsupervised

use

raise

the

barfor

guardrails

andfor

improvingobjective

patientoutcomes,notjustengagement.●AppliedAI&

DemosThe

most

immediatetranslatable

progresscan

beseenatthe

individualtask-specificlevelwith

imaging

remaining

the

dominant

usecase.●We

provideasneak

peekofthe

nextwaveoftoolssuchas

EHR

chatbots,

eConsults,

and

mental

health

chatbots.ExecutiveSummaryPage

12ARISE-AI.ORGPage

#●

Datasources&searchstrategy○Reviewed

PubMed,

preprintservers(e.g.,

medRxiv,arXiv)

using

a

combination

of

terms

suchas

“large

language

models

in

medicine,”“AI,”“diagnostic

reasoning,”“management

reasoning,”“diagnosticerror,”“benchmarks,”and

“patient-facingAI.”○InvitedcliniciansandAIresearchersfromacademic

institutions

and

issued

an

open

callforsubmissionsviasocial

media(e.g.,LinkedIn)to

identify

high-quality

studies

acrossthesixthemes.●

Studyselection○

Allstudiesreviewed

byauthorsand

reviewersofthis

presentation.○Includedempiricalstudiesthat

(1)

used

anAI

model/LLM

in

a

clinical

context,

(2)reportedquantitativeorqualitativeoutcomes(e.g.,diagnostic

accuracy,

bias,calibration,workflow,

user

performance),and(3)determinedto

beof

high

impact.○Excluded

purelytechnical

model

paperswithoutclinician-or

patient-facingevaluation,

editorials,and

non-clinicalAI(e.g.,drugdiscovery,

biotech).OurApproachtoaTargeted

ReviewofClinicalAIMethodsPage

13ARISE-AI.ORGPage

#Foundational

MethodsNoveltechniquesthatoptimizeclinicalAI

performanceaboveofftheshelf

models.AppliedAI&

DemosDemonstratingAI’sdomainspecificapplicationsand

usecases.ARISE-AI.ORGAIin

ClinicalWorkflowsHowcliniciansandAI

systemscollaborate

inreal

orsimulatedenvironments.Patient

FacingAIHowAIengagesdirectly

withpatientstoinform,

support,

andpersonalizetheirhealthcare.Model

PerformanceHowwell

models(trained

AIsystems)perform

independentlyacrosspredictionand

reasoningtasks.Benchmarks&EvaluationsTheevolvingmetrics

that

define

AIcompetence

in

medicine.Tableof

ContentsPage14ModelPerformanceTeam

Name

Page#Page15

15Model

PerformanceIn2025,frontier

models

made

major

leaps

inautonomousclinicalreasoningand

prediction.●Slides18–20:Reasoningfrontier

models

show

large

gains

in

autonomous

clinical

reasoningversushumans,

includingonhistorically

difficult

cases.●Slides21–22:Key

weaknesses

persist:poor

performance

in

uncertainty-heavyscenarios,overconfidence,andpattern-basedshortcutbehavior.●Slides23–27:

Models

continue

to

show

promise

for

scalable

prediction

across

a

wide

variety

of

use

cases

such

as

patient

deterioration,

screeningfor

insulin

resistance,

and

aging.Overall,model-only

evaluations

reveal

thatLLMshave

achieved

superhuman

capabilityin

controlledtasks

butstill

requirestronger

metacognition,

calibration,

andstress

testing

before

autonomousdeployment.Team

Name

Page#Page

1616●

Inpatientdeterioration●

Biologicalage●

Insulin

resistance●Wearabletimeseriesdatafor

diagnosisprediction●

Clinical

riskcalculator●Approachingsuperhuman

reasoning●AI

vs

MD○

LLMvs

PrimaryCare

Physician○

LLMas

an

expert

case

discussant●

Gaps○“Noneoftheother

answers”○

Brittleoverconfidenceand

uncertaintyPredictionComplex

ReasoningModel

PerformancePage

#

Page

17

17

ARISE-AI.ORGO1-preview/o1:

ReachingSuperhuman

Reasoning

PerformanceO1-previewando1consistentlyoutperformedoratthe

level

of

physicians

across

several

reasoning

evaluations,

solvingchallenging

NEJMcasesatstate-of-the-art

levels,documentingsuperiorreasoning

quality,

excelling

in

managementtasks,anddiagnosing

realemergency

roomcasesadmittedtothe

hospital.●On

NEJMclinicopathologicalconference(CPC)

cases,the

model

reached

78%

diagnostic

accuracy

and

selected

the

correct

nexttest87%ofthe

time.●o1-previewachieved

a

perfect

score

99%

ofthetimefor

clinicalreasoningqualitygraded

by

physicians.Thissignificantlyoutperformed

GPT-4(59%)andattending

physicians(35%).

Managementreasoning

for

o1-preview(86%)was

also

superior

compared

to

GPT-4

(42%)

andphysicians

with

GPT-4

(41%).●

In

real

EDcases,the

modeloutperformedoratthe

level

of

bothattendingphysiciansatthreediagnostictouchpointswith66%exact/near-exactdiagnosesvs.48–54%forphysiciansat

initialtriage.Brodeur,Buckley,Manrai,Rodman

etal.,

ArXiv,

Jul.2025●Modern

LLMs

may

nowsurpass

physicians

in

general

diagnostic

and

management

reasoning

in

controlled

environments,

motivatingthe

needfor

prospectiveclinicaltrialsforreal-worlddeployment.ARISE-AI.ORGPerformance

/Benchmarks/Methods/Clinical

Workflows/Patient-Facing/Applied

AI&DemosPage

18Page

#AMIE(gemini-based)wasdesignedasatwopart

systemwith

access

to

an

agentstate(current

patientsummary,differentialetc.):a

fast

DialogueAgenttocapturerelevant

HPIandaslower

Management

Reasoning

agent

using

longcontextreasoninggrounded

inclinical

guidelines.ComparedAMIEto

PCPsacross

100three-visitsimulatedscenariosspanningcardiology,

pulmonology,

neurology,OBGYN/urology,andGI,

eachgrounded

in

NICEand

BMJ

Best

Practiceguidelines.Graded

bysubspecialists,AMIE’srecommendationsfor

investigationsand

treatmentswereconsistently

more

precise(Yes/No),especiallyforinvestigations

in

follow-up

visits(visit2:99%vs.

84%,visit

3:

100%vs.88%),andcarriedexplicitcitationstoguideline

sources.

Possibilityfor

agenticagentstoserveasapoint

of

continuity

in

afragmented

system.Ona

novel

medication

reasoning(RxQA)

benchmark,AMIEoutperformed

PCPsonharderquestions(asdetermined

by

pharmacists)

in

both

closed-

andopen-bookconditions,demonstratingstrongtherapeuticreasoning.Google’sAMIEChatbot

Matches

PCPsat

Multi-Visit

Disease

ManagementEnhancedwitha

newmanagement-reasoningagent,theArticulate

Medical

Intelligence

Explorer(AMIE)was

non-inferiorto21

primarycare

physiciansacrossguideline-baseddecision-making,treatment

planning,and

longitudinal

care.AMIEproducedmore

precise,guideline-based

plans,andoutperformed

physicianson

medication-reasoning

questions.Performance

/Benchmarks/Methods/Clinical

Workflows/Patient-Facing/Applied

AI&DemosPalepu,

Schaekermann

etal.,

ArXiv,Mar.2025ARISE-AI.ORG●●●●Page

19Page

#●Built

from7,102

NEJM

CPCs

(1923–2025)

and

1,021

NEJM

ImageChallenges,CPC-Benchcovers

10reasoningtasks(DDx,testing

plans,

touchpoints,omission,VQA,

literaturesearch,etc.).●Among

eightfrontier

models,

o3

achieved

60%

top-1

and

84%

top-10

accuracyonCPCdifferentialdiagnosis,outperforminga

20-physician

baseline,with98%accuracyselectingthenext

test.●Dr.CaBot,

basedono3,

isa

publicly

available

(/)

systemthatproducesbothwrittenandvideocase

presentationsthat

outperformstheoriginallypresentedexpertcasediscussant.●

ThestudyshowsthatAI

is

nowcapableof

performingtheentire

CPC

discussantrole,withreasoningqualityrated

better

than

human

experts.AIOutperforms

Physiciansasan

ExpertCase

DiscussantonChallenging

CasesResearchersdeveloped

Dr.CaBot,anAIdiscussant

basedono3that

produceswritten

and

video

CPC-style

differentials.Dr.CaBotwasevaluatedon

NEJMCPCsand

NEJM

Image

Challenges,spanning

ten

tasks

that

test

differential

diagnosis,testingstrategies,clinical

reasoning,

uncertainty

handling,andmultimodal

interpretation.

In

blindedtesting,

physicianscould

not

reliablydistinguish

Dr.CaBotfrom

humanexperts,andconsistently

rated

its

reasoning

higher.Performance

/Benchmarks/Methods/Clinical

Workflows/Patient-Facing/Applied

AI&DemosBuckleyetal.,

ArXiv,

Sept.2025Page20ARISE-AI.ORGPage

#“Noneoftheotheranswers”:An

LLMWeaknessResearcherstestedwhetherLLMscouldtruly

reasonthroughmedical

questions

by

replacingthe

correct

answer

inmultiplechoicequestionswith“Noneoftheother

answers”

(NOTA).

Frontier

models

showed

significant

drops

inaccuracy,

revealingthatstrongmultiplechoice

performance,

is

in

part,dueto

pattern

recognition.●Researchersmodified

100

MedQAquestionssothatNOTAbecamethecorrectanswer,

creating

a

68-itemclinician-validatedtestofgenuinereasoning.The

pattern

ofanswershaschanged

butthe

underlying

clinicalreasoning

has

not.●DeepSeek-R1,o3-mini,Claude3.5

Sonnet,

Gemini

2.0

Flash,GPT-4o,and

Llama3.3-70Ball

performed

worse

on

NOTA-modifiedquestions.Significantdecreases

in

performancewereexhibited,rangingfrom9%to

38%.●

A

system

that

falls

for

example

from

81%

→43%accuracywhenapatternchangeswould

be

unsafefor

autonomousclinicaluse;rigorous

benchmarks

musttest

reasoning,notmemorized

answer

distributions.Performance

/Benchmarks/Methods/Clinical

Workflows/Patient-Facing/Applied

AI&DemosBedi,

Shah

etal.,

JAMANetworkOpen,

Aug.2025Page21ARISE-AI.ORGPage

#●SCTmeasuresthe

abilityto

revise

diagnostic

or

managementjudgments

when

new

informationarrives,acoreskillof

clinical

reasoning

underuncertainty.●This

study

established

a

benchmark

assessing

750

SCT

itemsfrom

10

datasets,

including

pediatrics,neurology,emergencymedicine,

internal

medicine,and

physiotherapy,mostnever

previously

published.●OpenAI’s

o3(68%)

led

performance,followed

by

GPT-4o(64%),

matchingmedicalstudentsbutbelowresidents

and

attending

physicians.

Manyreasoning

models

performedsurprisingly

poorly(e.g.,Gemini2.5:52%).McCoy,Rodman

etal.,NEJMAI,Sept.2025●LLMsoverusedextremeratings

(+2/-2),

rarelyselected

neutrality

(0),

and

showed

miscalibrated

confidence

patterns

unlikehumanexperts,suggestingthatchain-of-thought–optimizedmodelsmay

overcommit

in

uncertainty-richtasks.ARISE-AI.ORGScriptConcordanceTesting

RevealsGapsin

LLMClinical

ReasoningAstudycompared

10frontiermodelsto

1,500+clinicianson750

Script

Concordance

Testing

(SCT)

questions,whichmeasuretheabilityto

reviseclinicaldecisionswhen

new

information

becomesavailable.

Models

matched

medicalstudents

but

underperformed

relativetoseasoned

physicians,

revealingconsistentoverconfidenceanddifficultyupdatingdecisions

underuncertainty.Performance

/Benchmarks/Methods/Clinical

Workflows/Patient-Facing/Applied

AI&DemosPage22Page

#●Outsideofthe

ICU,

inpatientvital

signs

are

checked

every

4-8

hours,

which

leavestimegapsofmissedopportunityfor

detecting

critical

illness.●Researcherstraineda

recurrent

neural

networkwith

a

5

hour

sequence

ofcontinuousvitalsign

inputs(e.g.,

HR,

RR)collectedfrom

a

wearable

chestdevice,withdemographicsfrom888non-ICU

patientsto

detect

earlydeterioration.●

Predicted9x

moreclinicalalerts(Modified

EarlyWarning

Score

(MEWS)

>6for>30

mins)8-24

hours

before

EHR-based

MEWS

alerts,

withAUROC0.89(retrospective)and

AUROC0.84-0.9

(prospective).Predicted9of

11hardoutcomeevents

(cardiac

arrests,

death)

upto

17

hours

before

MEWS.●Enablesfaster

recognition

of

physiologic

decline

andthe

potentialto

preventavoidabledeteriorations.Predicting

Inpatient

Deterioration

Before

It

HappensResearchersdevelopedadeep-learningmodel

usingcontinuouswearablevital

sign

data

from

888

hospitalized

med-surgpatientsto

predictclinicaldeterioration

upto8-24

hours

beforestandard

EHRalerts.

The

model

generated

moretimelyalertsthanepisodicvitalchecksandaccurately

predicted

hardoutcomes,

including

ICUtransfer,

cardiac

arrest,

anddeath.Performance

/Benchmarks/Methods/Clinical

Workflows/Patient-Facing/Applied

AI&DemosScheid,

Zanos

etal.,Nature

Communications,

Jul.2025ARISE-AI.ORGPage23Page

#●Using

LLMs

in

the

Llama

and

Qwen

families,

applied

prompt

learning

without

supervised

learningonagingrelated

knowledge.After

beingfedhealthexaminationtext

reports,

LLMs

integrate

individualizedclinicaldatato

infer

biologicalagewithout

predefined

biomarkersor

labels.●LLM-based

biologicalageachievedaconcordance-indexof

0.76forall-causemortality.Alsooutperformedepigeneticclocks,telomere

length,

frailty

index,andconventional

ML

models.Thedifference

betweenLLM-predictedageandchronologicalage(“age-gap”)was

strongly

associatedwithall-cause

mortality(HR

1.05).●LLM-derivedorgan-specific

biologicalages

better

predictedcorresponding

organdiseasesandenabledpotentialdiscoveryof316

aging-related

protein

biomarkers.●Potentialforscalableandcost-effectivepersonalizedand

population

aging

assessmentwith

interpretabilityusingchainofthought

prompts.Predicting

BiologicalAgingat

PopulationScale

Using

Large

Language

ModelsThisstudy

introducesan

LLM

prompt

basedframeworkthat

predicts

biologicalagefrom

routine

health

records,enablingscalableagingassessmentacross

populations.Appliedto>10million

individualsfromsix

cohorts

(e.g.,

UK

Biobank),

theLLM-derived

biologicalageoutperformedtraditionalagingclocks

in

predictingmortality

and

multiple

age-relateddiseases.Li,Dietal.,NatureMedicine,

Jul.2025ARISE-AI.ORGPerformance

/Benchmarks/Methods/Clinical

Workflows/Patient-Facing/Applied

AI&DemosPage24Page

#●Currentme