https://doi.org/10.1007/s00270-025-04171-y
TheRoleofAIinClinicalTrialDesignandScientificWriting
NikiKatsaraAntonakea1 • JuliusChapiro1 • JeffGeschwind2,3
Received:10February2025/Accepted:13August2025
SpringerScience+BusinessMedia,LLC,partofSpringerNatureandtheCardiovascularandInterventionalRadiologicalSocietyofEurope (CIRSE)2025
Abstract Artificialintelligenceistransformingthelandscapeofclinicalresearchandscientificwriting,offering innovativesolutionstoaddressinefficienciesintrial design,patientrecruitment,andmanuscriptdevelopment. Thisreviewexploresapplicationsofartificialintelligence inpatientmatching,endpointdesignandpredictivetrial outcomes,andreal-timepatientmonitoring.Italsodiscussesitsroleinassistingwithliteraturereview,generating content,andrefininglanguageinscientificwriting,especiallyfornonnativeEnglishspeakers.Challengessuchas datastandardization,explainability,andethicalconcerns arehighlightedalongsideemergingregulatoryframeworks toensuretransparentandresponsibleartificialintelligence integration.Byexaminingitscurrentcapabilitiesand futurepotential,thisarticleunderscoresthetransformative roleofartificialintelligenceinenhancingefficiency, reducingcosts,andadvancinginnovationinmedical research.
Keywords Artificialintelligence(AI) Machine learning(ML) AI-assistedscientificwriting Naturallanguageprocessing(NLP) AIindrug development AI-assistedpatientrecruitment AIin clinicaltrialdesign AIapplicationsinmedical research
& JeffGeschwind jgeschwind@namsa.com
1 YaleUniversitySchoolofMedicine,333CedarStreet, NewHaven,CT06510,USA
2 NAMSA,NewYork,NY,USA
3 HistoSonics,Plymouth,MN,UnitedStates
Introduction
Prospectiveclinicaltrialsarethegoldstandardforevaluatingthesafetyandefficacyofnewmedicaltreatmentsand providingthenecessaryevidenceforscientificdiscoveriesin aregulatedframework.However,theprocessofdeveloping anewtherapyisbothtime-intensiveandcostly,takingupto 15yearsandanaverageof$2billioninresearchand development(R&D)tobringanewdrugtomarket.Despite thisinvestment,drugdevelopmentsuccessratesremainlow, withonlyoneoutofevery10drugsenteringclinicaltrials ultimatelysecuringFDAapproval.Thistranslatesintoupto a$1.4billionlossperfailedclinicaltrial[1].Inoncology, clinicalresearchhasreachedhistoriclevelsofexpenditure, withglobalR&Dspendinginthisfieldroughlydoubling from$137billionin2012to$262billionin2023.Currently, thisbudgetisprojectedtoexceed$300billionby2027.The numberoftreatedoncologicpatientshasseenarisebyan averageof5%overthepastfiveyears.Thecostsof developingantineoplasticandimmunomodulatingagentsare amongthehighestintheindustry,withmedianexpenditures reaching$2.7billionperdrug.Meanwhile,smallerbiopharmacompaniescontinuetodriveinnovation,accounting for71%oftheoncologypipelinein2022[2].
In2024,theglobalmarketformedicaldeviceswas estimatedataround$640billion,withforecastssuggesting itcouldnearlydoubleandsurpass$1.1trillionby 2034.NorthAmericacontinuestoleadthesector,generatingcloseto40%ofworldwiderevenue[3].Similarly,the medicaldeviceindustrywasworth$540.69billionin2021 andwasexpectedtoreach$523.13billionby2023, accordingtoGlobalData’sanalysis.NorthAmericahadthe largestshareofclinicaltrialsformedicaldevices(48%)in 2022,followedbyEurope(27%),andAsia–Pacific(22%)
[4].Meanwhile,clinicaltrialsarebecomingmorecomplex withthenumberofcountriesinvolvedmorethandoubling. Theaveragenumberofendpointscollectedhasa214% overall20-yearriseandtheprocedurestomeasurethese outcomesa139%rise,respectively.Ontheotherhand, patienteligibilitycriteriahaveremainedrelativelyconstant [5].Highclinicaltrialfailureratesandtherisingcomplexityofclinicaltrialsaremajorfactorsfortheinefficiencyofthecurrentdrugdevelopmentprocess. Suboptimalpatientselection,ineffectiverecruitment strategies,aswellasinsufficientmonitoringandadherence mechanismsbreakdowntheserates[1].
AIreferstotheabilityofmachinestoperformtasksthat typicallyrequirehumanintelligence,suchaslearning,reasoning,anddecision-making.WithinAI,machinelearning (ML)isamethodologyfocusedonenablingautomatedpatternrecognitiontoachievepredictionsfromdata,mostly basedonannotateddatasetswithadegreeofhumansupervision.Deeplearning(DL)isanothermorerecentlydeveloped methodologywhichusesneuralnetworks(mimickingthe neuronalconnectionsofthehumanbrain)toidentifycomplex relationshipswithinlargedatasetswithouthumaninput. Naturallanguageprocessing(NLP),acriticalcomponentof AI,bridgesthegapbetweenhumanlanguageandmachine comprehension.Byanalyzingwrittenorspokenlanguage, NLPenablescomputerstoextractmeaningfromunstructured text,suchaselectronicmedicalrecords(EMRs)orclinical trialnotes.Morerecently,largelanguagemodels(LLMs),a cutting-edgeadvancementinNLP,havedemonstrated exceptionalcapabilitiesinprocessinglargeamountsoftextual data,generatinghuman-likeresponses[1].Thesetoolsenable AIsystemstoprocessandinterpretunstructuredclinical, molecular,andimagingdata,whichwerepreviouslychallengingtointegrate[6].Therecenttechnologicaladvancementsaswellasthedigitalandpublicavailabilityoflarge datasetshaveenabledtherefinementoftheseAImodelsand theirimplementationinmedicalscience[1].AIcanprovetobe especiallyhelpfulinareaswhereprofitabilitymaynotjustify significantinvestment,suchasrarediseasesandtargeted therapies.Particularly,oncologyisthemostprominently discussedfieldfortheimpactsofAImodelsinitsincreasingly demandingandcostlyinnovativedevelopments[7].
Thefollowingsectionwillexplorespecificcategoriesof AIapplications,demonstratinghowthesetechnologiesare reshapingthelandscapeofclinicaltrials.
PatientEnrollment,PatientMatching,Patient Selection
Therecruitmentprocessforclinicaltrialsfacesnumerous challenges.Apatient’sspecificmedicalhistoryordisease stagecanmakethemineligible.However,eveneligible
potentialpatientsmaylackawarenessofsuitableclinical trialsorfindthecomplexrecruitmentprocessdemotivating.Thesebarrierssignificantlyhinderrecruitmentefforts, with86%oftrialsfailingtomeetenrollmentdeadlines.AI andmachinelearningsystemsofferpotentialsolutionsby optimizingpatientselectionandrecruitmentprocesses[1]. Consideringthis,JamesZouandhisteamatStanfordhave createdasystemcalled‘‘TrialPathfinder’’,designedto analyzecompletedclinicaltrials,evaluatingvarious thresholdsofeligibilitycriteriaandtheircorrelationtothe rateofadverseoutcomeslikesevereillnessordeathamong patients.Severalfirms,suchasRoche,Genentechand AstraZeneca,arenowusingTrialPathfinder[5, 6].As statedonGenentech’swebsite,theTrialPathfinderproject approachdemonstratedthatbylooseningeligibility requirements,thepoolofpotentialtrialparticipantsgrew by107%,allowingforgreaterinclusionofwomen,African-Americanindividuals,andpeopleacrossawiderage spectrum[8].
ChunhuaWeng’slabatColumbiaUniversityhas developed‘‘Criteria2Query’’,whereuserscaninputeligibilityandexclusioncriteriainnaturallanguageandare thengivenpossiblepatientdatabasestoidentifymatching candidates.Tohelppatientsalsofindtheirwaytosuitableclinicaltrials,Weng’steamcreated‘‘DqueST’’,which extractscriteriafromtrialdescriptionsfromCriteria2Query andgeneratespatient-focusedquestionstorefinetheirtrial search.Similarly,‘‘TrialGPT’’,developedbySun’slabin collaborationwiththeNIH,usesalargelanguagemodelto matchpatients.TrialGPTanalyzesbothstructuredand unstructuredinformationfromelectronichealthrecordsto assesspatienteligibility.Itinterpretsthedetailedcriteria outlinedintrialprotocolsandperformsseparateassessmentsforinclusionandexclusionrequirementsforeach patient-trialcombinationleadingtoanoverallsuitability scoreforeachtrial.TheseAI-drivenapproachesreduce screeningtime,improvepatientmatchingandespecially helppromoteaccessibilitytopatientswithterminalcancer orrarediseases,whooftenstruggletofindsuitabletrials. IntelligentMedicalObjectshasintroducedasetofthree toolsaimedatimprovingclinicalresearchandhealthcare monitoring.Thefirstextractsandorganizeseligibility criteriafromclinicaltrialdescriptions.Thesecondsupports FDAsubmissionsbyanalyzingrelevanttrials.Thethird monitorssocialmediatodetectandreportunmethealthcareneeds[5].‘‘WatsonforClinicalTrialMatching’’, anothertoolusingNLPforpatient-trialmatchingworkson thebasisofthreecomponents:atrialdatainput,apatient datainput,andlastlythematchingprocess[9].Despiteall theseefforts,patientmatchingmodelshavebeenestablishedinEnglishlanguagecontexts.Toovercomethis, researchersinChinadevelopedaclinicaltrialmatching system(CTMS),integratedwiththeChineseEHRsystem,
whichefficientlyreducedineligiblepatientsandthusprocessingworktimeforhealthcareprofessionalsprovingthe utilityofsuchAImodelsalsoinChineseclinicaltrials [10].Nevertheless,challengesincludingmorecomplex clinicaltrialcriteriaorthelackofstandardlanguagefor theirdescriptionarestillevident[11].
CohortComposition
Clinicaltrialsoftenuseastrategycalledclinicaltrial enrichment,inwhichonlythosepatientsubgroupsmost likelytoshowadrug’seffectareenrolled.Whilethis approachboostsstatisticalpower,itcanalsolimitgeneralizability—patientswithdifferentcharacteristicsmay experiencelowerefficacythanwhatthetrialreports. Combiningmultipledatasources,suchasmulti-omics[6], EMRsandmedicalimagingandusingAItoidentify biomarkersandrefinepatientpopulationscanassistin unifyingthecomplexdatainputsandhelpwiththeright cohortcomposition.AIcanenhancepatientselection accordingtoFDAbyharmonizingpopulationvariability, identifyingpatientswithmeasurableoutcomes(prognostic enrichment),andchoosingthoselikelytorespondto treatment(predictiveenrichment )[1].AnexampleofAIdrivencohortrefinementcomesfromarecentstudyin renalcellcarcinoma,whereadeeplearningmodelidentifiedanangiogenesis-relatedtranscriptomicsignature— Angioscore—fromhistopathologyimages.ThisAI-derived biomarkerwasusedtostratifypatientsintheIMmotion150 trial,helpingdefineasub-populationlikelytobenefitfrom anti-angiogenictherapy[12].
PatientMonitoring
Artificialintelligenceenhancespatientmonitoringby analyzingdatafromwearabledevicestodetectsubtlesigns ofpathologicalchangesoradverseeventsofmedication. Byintegratingmultipledata,suchasphysicalactivity, sleepquality,andmedicationadherence,AIsystemscan deliverpersonalized,real-timeinterventions,whilecontinuouslyadaptingbasedonthepatient’suniqueconditions,improvingadherenceandclinicaloutcomes.AIalso helpsidentifybehaviorsthatsignalnon-adherence, enablingearlyinterventions[13].Inclinicaltrialsettings, patientmonitoringcanbeimprovedwiththeuseofwearables.OnepracticalexampleofthisisastudythatsuccessfullyintegratedbiometricdatafromanAppleWatch intoEMRsforpatientsundergoingradiotherapy.This studydemonstratedhowAIcouldanalyzedailyresting heartrate,heartratevariability,stepcount,andexercise minutes,providingvaluableinsightsforoncologists.The
wearabledatawasautomaticallytransmittedandreviewed byphysicians,revealingtrendssuchasadecreasein exerciseovertimeandanincreaseinheartratevariability, whichcouldbeindicativeofnon-adherenceorchangesin thepatient’shealthstatus.Thisreal-timeintegrationof wearabledataintoclinicalpracticeshoweditspotentialto improvemonitoring,adherence,andpatientoutcomes. Furthermore,thesystem’shighfeasibilityandpatientsatisfactionunderlinethepracticalityofusingwearablesin clinicaltrials[14].However,ethicalconsiderationsrelated todataprivacyandsecurityremaincrucial.Itisessentialto implementsecuritymeasurestoprotectsensitivepatient informationandensurethatpatientshavecontrolovertheir data[13].
Patientdropoutisasignificantchallengeinclinicaltrials,withonly15%avoidingitandanaveragedropoutrate of30%.AIsensorsinwearabletechnologiescanprovide real-time,personalizedpatientmonitoringbycollecting data[7],whichcanbeanalyzedinreal-timebyML/DL modelsandpotentiallypredictdropoutriskbyidentifying behaviorsthatindicatenon-adherence[1].Researchersat NovartishaveproposedthatAIcanpredictwhichpatients areatriskofdroppingoutbasedonhistoricaldata, allowingtimelyinterventionsandtheuseofvideoanalysis toensurepropermedicationadherence.Chatbotsalsooffer valuablesupportforpatientsfurtherreducingdropoutrates byprovidingsufficientsupportforpatient’sconcernsby answeringtheirquestions.Forexample,astudyfoundthat healthcareprofessionalsfavoredChatGPT’sresponsesover doctors’answersfromReddit’sAskDocsforum.Additionally,‘‘ChatDoctor’’,atooldevelopedusingMeta’s LLaMA-7Bmodelonpatient-doctorinteractions,waseven moreefficientinansweringmorerecentmedicalquestions thanChatGPT[5].
Anothermajorgoalofreal-timepatientmonitoring wouldbetopredictdrugtoxicityandpreventadverse effects.Inthiscontext,YauneyandShahusedDLto optimizechemotherapydosingforbraintumors.Their modeliterativelyadjustedtreatmentregimenstosuccessfullyminimizetoxicitywhilepreservingtumorshrinkage, thusenhancingpatientadherenceandreducingdropout rates[15].Accuratetoxicitypredictionscouldserveasan alternativetotraditionalinvitroandanimalmodels[7]. Wearablescanalsohelpwithendpointdetectionbycollectingthereal-timedatamoreefficientlythanself-collectingmethods[1, 7].
TrialDesign
AIcancreatenewapproachesformodernclinicaltrial designtypes.Forinstance,reducingthenumberoftrial armsbyusingsyntheticdata[11].Ininterventional
radiology,certainclinicalscenariospresentethicalchallengestotraditionalclinicaltrials.Forinstance,incasesof high-riskpulmonaryembolism(PE),wherepatientsexhibit hemodynamicinstability,immediateinterventionisoften necessary,makingrandomizationtoanon-interventional armethicallyproblematic.Interventionaltreatments,such ascatheter-directedtherapiesandsurgicalembolectomy, arefrequentlyemployedintheseurgentsituations[16]. Thisunderscorestheneedforalternativeresearchdesigns.Onepromisingapproachistheutilizationofthesocalledin-silicotrialsusingclinicaldatatobuildsimulated cohortgroups.TheCIRSERegistryforLifePearlMicrospheres(CIREL),forexample,collectsextensivedataon transarterialchemoembolizationincolorectalcancerliver metastases,providingarichdatasetthatcanpotentiallybe usedtocreatein-silicotrials.Suchmethodologiesnotonly addressethicalconcernsbutalsotransformtheefficiency andapplicabilityofclinicalresearchinIR[17, 18].Digital twinsuseAItobuilddynamicvirtualmodelsofpatientsor systems,continuouslyupdatingwithreal-worlddata.These modelsrefineclinicalstudydesignsbypredictingoutcomesandfuturescenarios,tailoringinterventions,and improvingresearchefficiency.Byreducinguncertaintyand supportingdata-drivendecision-making,digitaltwinshelp createmoreadaptiveandprecisemethodologiesinclinical research[19].Thismeansthattheclassicplaceboarm couldeventuallyconsistofsyntheticpatientsreducing ethicalissues,patientconcerns,patientdropouts,and increasingpatientrecruitmentandadherence[7].Digital twintechnologyinInterventionalRadiologycanoffer promisingsolutionsforprocedureplanningandpersonalizedtreatment.OnenotableexampleistheGo-Smart project,whichdevelopedaweb-basedsimulationplatform thatallowsclinicianstocreatepatient-specificdigitaltwins forprocedures,suchasradiofrequencyablation,microwaveablation,andcryoablation.Byuploadingimaging andclinicaldata,IRspecialistscansimulatevarious treatmentscenariostoguidedecision-makingandimprove proceduraloutcomes[20].Similarly,theconceptofTheranosticDigitalTwins(TDTs)hasbeenintroducedinthe contextofradiopharmaceuticaltherapy.TDTsintegrate imaging,biodistribution,anddosimetrydatatomodelthe effectsofdifferentradiotherapeuticregimensvirtually, enablingpreciseandpersonalizedtreatmentplansbefore actualintervention[21].Morebroadly,digitaltwinsin radiologyarebeingexploredastoolstomodelanatomical andphysiologicalcharacteristicsofindividualpatients, allowingsimulationofdevicebehaviororproceduraloutcomes,whichisespeciallyrelevantinIRwhereminimally invasive,high-precisionproceduresarecentral[22].To helpresearcherswiththeoveralldesignandresearch workflowfromstarttoend,‘‘AgentLaboratory’’wascreated,anautonomousframeworkusingLLMstoassistthe
researchprocessfromliteraturereviewtoexperimentation andreportwriting.Itproducesresearchoutputslikereports andcode,withusersprovidingfeedbackateachstage. Becauseofthis,researcherscanfocusoncreativeand conceptualaspectsoftheirwork,whilereducingthecost anddurationoftheoverallprocess[23].Furthermore,the abilityofAImodelstohandlelargeamountsofdatacanbe especiallyhelpfulwhenitcomestofailedclinicaltrials, whosedatacanandshouldbeanalyzedbyAItechnologies toextractnewconclusionsforfutureimprovedtrialdesigns aswellastoreconsidertrialeddrugsinnewways[1].
DataProcessingandAnalysis
AIalgorithmscanassistinautomaticallyidentifyingkey markersthatwouldtypicallyrequiremanualinputfrom experts.Theycanalsoimproveimagingreviewbyusing automatedclassificationtools,significantlyreducingtime andcost.Additionally,MLmodelscanimputemissing datatotakeadvantageofallcollecteddatadespitemissed dataormissedpatientvisits[7].However,challenges remain,includingtheneedtovalidatethesealgorithmsand getregulatoryapprovalfortheiruse[11].Furthermore,AI modelsriskoverfittingtotheirtrainingdataandmaynot generalize;thus,thoroughvalidationonindependenttrial dataandregulatoryscrutiny(FDAsoftwareapprovals)are requiredtoensurethesetoolsmeetsafetyandefficacy standardsforclinicaluse[24].
OutcomePrediction
Predictingoncologicoutcomesisdifficultduetothevarietyofcancersandcomplexbiologicalfactors.However, MLmodelscanidentifypatternsbetweenclinicaltrialdata, imaging,clinicalandlaboratorybiomarkers,molecular profiles,andpatientoutcomes.Anotherstudydemonstrated howmultimodalMLalgorithmscanmeaningfullypredict oncologicoutcomesbyintegratingdiverseclinicaltrialand biomarkerdataacrossseveralcancertypes,includingcolorectal,pancreatic,melanoma,andnon-smallcelllung cancer.Theirmodelincorporatedfeaturessuchastreatmentregimens,drugclasses,molecularalterations,anda composite‘‘probabilityofdrugsensitivity’’(PDS)derived fromgenomic,transcriptomic,andproteomicbiomarkers. Bytrainingarandomforestmodeltheyachievedhigh predictiveaccuracyandidentifiedthesuperiortreatment armin81%ofrandomizedtrialsforPFSand71%forOS. TheseresultssuggestthatmultimodalAIcanaidin designingmoreeffectiveclinicaltrialsbyforecasting treatmentefficacy,guidingpatientstratification,and reducingrelianceontrial-and-errorapproaches.These
modelscouldhelpoptimizeclinicaltrialdesigns[7, 11].In adifferentcontext,JimengSun’slabcreated‘‘HINT’’ (hierarchicalinteractionnetwork),analgorithmdesignedto predictthesuccessofclinicaltrialsbyanalyzingthedrug molecule,targetdisease,andpatienteligibility.Thiswas followedbythedevelopmentof‘‘SPOT’’(sequentialpredictivemodelingofclinicaltrialoutcome)whichalso considersthetimingofthetrialinthetrainingdata[5].
ScientificWritingandEditing
Scientificwritingisafundamentaltoolforcommunicating researchfindings.Theprocessofwritingamanuscript startswithathoroughreviewofexistingliteratureand collectionofthereferencesthatwillbeusedideallyina citationsoftware.TwoAIprogramscanbeparticularly helpfulforthispurpose:ResearchRabbit(https://www. researchrabbit.ai/),Elicit(www.elicit.org)andOpenEvidence(https://www.openevidence.com).Thesetoolscan findreferencesrelatedtoaspecifictopictakingadvantage ofinformationfromitscontent,authors,orcitationsand quicklybuildanewlistofreferences.AIcanbenefitless theMethodsandResultssectionsofthepaper,whichare themostavailabletotheresearcherandthemostspecificto therespectiveresearch.However,sectionslikeIntroduction,DiscussionaswellasAbstractarecomplexandcan benefitfromAItext-generatingtoolssuchasChatGPT, whichcansummarize,paraphrase,orsynthesizecontent basedonpreviouslywrittenmaterial.ChatGPTcanalso correctspelling,punctuation,andgrammaticalerrors. SpecificsoftwareprogramsalsouseAIthatcancorrect grammarandspelling,suchasGrammarly(app.grammarly.com)andPaperpal(www.paperpal.com).Inthis way,thesetoolscanhelpnonnativeEnglishspeakersand decreaseinequalitiesexistingwithnativeones.Apartfrom that,GPT-4ooro1isevencapableofgeneratingcodeto analyzedatawithinMicrosoftExcelspreadsheetsorstatisticalsoftwarelikeRaswellasprovidingguidanceon appropriatestatisticaltestsfordataanalysisandoffering explanationsoftheresults.Whilethesetoolscanhelpin writingthemanuscriptandbreakingdownmentallydifficultpartsforbetterunderstandingandalsoassistwith analyzingresults,AIuseissuboptimalwhenitcomesto generatingnovelinsightsandunderstandingcomplexoutcomes.AIcanbeusedforwritingpapers,butwithespecial cautionbyyoungresearcherstonotharmtheircreativity andwritingskills.Inanycase,itsuseshouldalwaysbe disclosed.AIshouldneverbeexpectedtowriteanyofthe sectionsofascientificpaperasanauthorwould.ChatGPT cannotbeconsideredacoauthorinascientificpaper[25]. Moreover,ChatGPT’sreasoningprocessremainsunclear. Asignificantdrawbackisitstendencytopresentincorrect
orfabricatedinformationwithhighconfidence,aphenomenonknownas‘artificialhallucination.’Thisissueis especiallyconcerninginscientificwriting,wherethemodel mayproducecitationsthatdonotexist.WhileAIcan streamlinethewritingprocess,allresearchersmustcriticallyevaluateitsoutputs,cross-checkreferences,and remainalertaboutpotentialbiasesandinaccuracies.Furthermore,manyjournalsrequireauthorstomentionifthey usedAI[26].
Challenges,RegulationandFutureDirections
TheintegrationofAIintohealthcareandclinicalresearch iscomplicatedbychallengessurroundingthedigitalization andaccessibilityofEMRs.EMRsinvariousinstitutions havedifferentformats,digitalornot,andthelackof standardizationleadstoamoredifficultdataexchange.On theotherhand,averystrongregulatedenvironmentcan oftenimpedetheaccessofdatabyAImodels,creatingthe so-calledEMRinteroperabilitydilemma.Frameworkslike theUSHealthInsurancePortabilityandAccountabilityAct (HIPAA),theEUGeneralDataProtectionRegulation (GDPR),SaMD[24]andtheEUAIAct[27]shouldcontinuetoevolveasgoverningandprotectingsensitivehealth databecomesmorecomplexandshouldestablishnew areasoffocusthatincludetheefficientandsafeAIapplicationsinclinicalresearch[1],whileprotectingtheintellectualpropertyandpatientprivacy.Consideringthis,The ‘‘CancerMetastasesinLymphNodes’’(CAMELYON45) and‘‘InternationalBrainTumorSegmentation’’(BraTS) challengeshavefacilitatedaccesstoextensivede-identified datasetsformachinelearningteams.Similarly,thenewly established‘‘NightingaleOpenScienceInitiative’’aimsto provideadditionalde-identifieddatasetsforresearchpurposes.Federatedlearning(FL)isalsoanotherapproach, wheredataremainssecurelystoredatindividualinstitutionswhilemachinelearningmodelsarecollaboratively updatedandsharedinadecentralizedmanner[28].
AnotherchallengeintheuseofAIinclinicaltrialsisthe explainabilityoftheiroperation.AImodelsoftenrequire largedatasetsandcandetectcomplexpatternsinthedata leadingtoinnovativeresults,butresearchersoftenlackthe capacitytoexplaintheirmethodologies,theso-calledblack boxproblem.Thereisstilllittletransparencyordifficulty inunderstandingthecomplexnatureofthealgorithms,and theresultsareoftennotreproducibleorbiased[5].To addressthis,researchersaredevelopingexplainableAI (XAI)approaches,suchassaliencymaps,ShapleyAdditiveExplanations(SHAP),andLocalInterpretableModelAgnosticExplanations(LIME),whichaimtovisualizeor rankinputfeaturescontributingtoamodel’sprediction [29].Inthiscontext,involvingend-usersinthedesign
process,asproposedintheINTRPRTguideline,canfurtherhelpmodeldesignersdevelopmoretransparent machinelearningmodelsformedicalimagingbyclosely workingwithclinicians[30].Overfittingorthelackof generalizabilityofAItoolsdevelopedforspecificpurposes indealingwithdifferentpopulationsorcontextsmayfurtherimpedethebroaderuseofAItoolsinclinicaltrials[9].
TheuseaswellastheefficiencyofAIapplicationsin sensitivepatientdatainresearchsettingsisamust. Researchersshouldobtaininformedconsentfrompatients, andpatientsshouldbethoroughlyinformedonhowtheir datacanbeusedandbegiventheoptiontoopt-outatany time.TheuseofAIinclinicaltrialrecruitmentshouldbe transparentwithaclearunderstandingofhowthealgorithmsofpatientmatchingwork.Thereshouldalsobe mechanismstoevaluatefairness,discrimination,and selectionbiasissuesinAItools[9].
Despitethesechallenges,initiativeslike‘‘SPIRIT’’ (StandardProtocolItems:RecommendationsforInterventionalTrials)and‘‘CONSORT’’(ConsolidatedStandards ofReportingTrials)aimtoestablishstandardizedguidelinesforAI-drivenclinicaltrials,ensuringrobustreporting andethicalimplementation[31].Regulatorybodies, includingtheFDAandtheEU,shouldadapttoensure equitableandresponsibleuseofAI.Ultimately,thesuccess ofAIinclinicaltrialswilldependonovercomingtechnical,regulatory,andethicalchallengeswhilefosteringtrust andaccountabilityinitsapplications.
Conclusion
AIisstillinitsinfancyinthedesignofclinicaltrials,with itsprimaryapplicationscurrentlyfocusedondrugdevelopmentanditssuccessdependingonovercomingkey challengessuchasdataquality,biasmitigation,andregulatoryacceptance.Frompatientrecruitmenttooutcome prediction,AIistransformingthewaywecandesigntrials andwritescientificmanuscripts.Giventhecostsandthe growingcomplexityofclinicaltrials,AIwillplayan increasinglyintegralroleinfutureclinicaltrials.
Acknowledgements Theauthorshavenoacknowledgmentsto declare.
Funding Thisstudywasnotsupportedbyanyfunding.
Declarations
Conflictofinterest Theauthorsdeclarethattheyhavenoconflictof interest.
EthicalApproval Thisarticledoesnotcontainanystudieswith humanparticipantsoranimalsperformedbyanyoftheauthors.
InformedConsent Forthistypeofstudyinformedconsentisnot required.
ConsentforPublication Forthistypeofstudyconsentforpublicationisnotrequired.
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