Fátima Rodríguez|David Scheinker|Robert A. Harrington
Promise and Perils of Big Data and Artificial Intelligence in Clinical Medicine and Biomedical Research
Stanford University
homecirculation researchvol 123 12promise perils big data artificial intelligence clinical medicine biomedical research free accessarticle commentarypdfepubaboutview pdfview epubsections toolsadd favoritesdownload citationstrack citationspermissions shareshare onfacebooktwitterlinked inmendeleyreddit jump tofree accessarticle commentarypdfepubpromise perils big data artificial intelligence clinical medicine biomedical research fatima rodriguez david scheinker robert harrington fatima rodriguezfatima rodriguez correspondence fatima rodriguez md mph division cardiovascular medicine stanford university school medicine 870 quarry rd falk cvrc stanford ca 943055406 email email address email protected division cardiovascular medicine cardiovascular institute fr rah department medicine fr rah david scheinkerdavid scheinker department management science engineering ds stanford university ca robert harringtonrobert harrington division cardiovascular medicine cardiovascular institute fr rah department medicine fr rah originally published6 dec 2018httpsdoiorg101161circresaha118314119circulation research 201812312821284the widespread growth big data artificial intelligence ai industry offers preview promise peril medicine biomedical research viewpoint summarizes valuable lessons industries promising recent developments health care ideas moving forwardthe use big data ai help guide clinical decisions central aspect precision medicine initiatives yet buzz words like big data ai mystify many clinicians biomedical researchers widespread use industries initial clinical applications serve guide clearer understanding promise potential perilmany common terms mean different things different contexts ai typically refers machine human capabilities machine learning ml may refer either set computational statistical tools identifying relationships data use tools make predictions based data deep neural networks particular type ml whose success tasks image recognition led referred ai deep learningdeveloping ai ml deep neural network tools requires access big dataanother concept multiple meanings data scientists implies using data one computer handle significant attendant analytical computational challenges opportunities clinicians biomedical researchers refers complex datasets numerous structured unstructured data fields typically found electronic health records figure 1 illustrates relationship ai ml deep neural networks big data reinforcement learning notable exception use big data train ai approach building ai tools based feedback example deepmind program alphago zero became powerful go program world solely playing thus far reinforcement learning health care developed using historical data representing decisions feedback ai starts make test clinical decisions algorithms capacity learn owndownload figuredownload powerpointfigure 1 overview relationship artificial intelligence ai machine learning ml big data deep neural networksthe widespread use ai companies amazon google offers lessons health care industries applied ai enable smart electronic assistants facial recognition software selfdriving cars use misuse applications raised concerns safety fairness privacy example data increasingly used predict consumer behavior preferences want computers department store know pregnant family charged plane tickets buy using iphonethere numerous proofofconcept examples ml health care applied clinical risk prediction learning large volume data generated electronic health records large datasets16 notable recent examples include classification picture nevus malignant benign3 retinal fundus image predict risk cardiovascular disease7 using histopathology specimens predict prognosis lung cancer8 everyday clinical cardiology ml used interpreting automated ecg determine left ventricular ejection fractions echocardiography scoring coronary artery calcium scans9 form ai known computer vision developed prevent clinical errors improve patient safety10 although relatively methods implemented routine clinical practice none large scale demonstrate promise ai clinical medicine biomedical researchai used automate simplify tasks onerous timeconsuming single person team perform could use voice recognition software medical scribe allow doctoring less documenting patient exam rooms11 could aggregate personalized cohort ask simple clinically relevant questions clicks mouse research teams stanford university developed variety approaches leveraging data found electronic health record help clinicians make decisions based patients unique characteristics one approach allows physician considering decision call informatics consult triggers ml algorithm identifies patients similar one considered presents treatments outcomes12 similar tools could used quickly identify patients meet criteria entry randomized clinical trials dramatically cutting enrollment recruitment costs translational research ml efficiently identify candidate molecules drug development ml better risk stratify populations underrepresented available evidence base identify patterns relationships captured traditional statistical methodsto realize potential gains clinicians need acquire basic knowledge workings ml ai basic statistical literacy become essential interpreting results clinical trials conducting research basic understanding ml ai become necessary clinicians evaluate new developments use tools make possible new concepts become frequently used abused p value include training data testing data may refer best practice randomly dividing data distinct subsets building models training data selecting best performing testing best performing model previously seen testing data concepts may abused example cherrypicking testing data model performs exceptionally well testing numerous models testing data reporting best performance two closely related concepts underfitting models simple adequately describe training data overfitting models specific training data generalize well testing data figure 2 illustrates example dataset split randomly training testing data 3 models underfit fit well overfit data performance models testing data supervised learning refers models trained data labels malignant benign3 unsupervised learning describes models identify previously unknown patterns classes tumors6 numerous concepts inform conversations physicians engineers computer scientists statisticians guess whether conversations continue occur mostly research institutions become commonplace nonresearcher providersdownload figuredownload powerpointfigure 2 overview common methods used machine learning 3 sequential steps common use machine learning algorithm identify relationship data left example data may randomly split training set testing set middle illustration 3 variants machine learning model variant produces curve meant capture relationship data right evaluation testing data quality fit curve generatedwith exciting promise could possibly go wrong among feared perils ai data misuse overuse insurance companies use ml algorithms refuse coverage patients high predicted risk adverse outcomes models exacerbate healthcare disparities inaccurately labeling certain groups higher risk nonadherent etc based limited data result patient distrust data impact clinical care unanticipated consequences common new technology considered carefully implementing models larger scalesit important remember principles scientific method continue apply although ai yield accurate predictions results often black box obscuring importance selection predictive inputs13 observational data bias continue exist dealt carefully black box nature ai algorithms may make difficult prevent algorithms trained biased data codifying amplifying bias applications ai setting bail recognizing faces screening job applicants criticized propagating racial bias data trained although many critiques apply particular use ai fundamental limitations ai highlight importance understanding mechanism behind predicted associations reason ml need used combination traditional biological epidemiological biostatical tools research must remain hypotheses driven continue guard spurious observations based multiple testing carefully consider construction large datasets important algorithms trainedwill ai replace doctors researchers clinician uses ai replace clinicians ai increase efficiency take many routine tasks provide first last set eyes medical teams become increasingly multidisciplinary consist clinicians engineers data scientists imagine 20 years ago someone asking phone afternoon got meaningful response warning traffic would seemed like true ai commonplace hardly considered ai remain optimistic big data ai transform lives clinicians researchers potential reduce time spend cumbersome tasks warn us signs trouble help catch potential errors free us provide higher quality lower cost caresources fundingf rodriguez funded mcgormickgabilan faculty award stanford universitydisclosuresf rodriguez receives research support verily life sciences scheinker serves advisor carta healthcarea healthcare analytics company ra harrington reports research grants stanford university name apple sanofi astra zeneca portola janssen bristolmyers squibb novartis commonwealth serum laboratories medicines company belongs consultingadvisory boards amgen bayer element science gilead myokardia webmd belongs board directors signal path scanadu american heart association unpaid stanford healthcare unpaidfootnotesthe opinions expressed article necessarily editors american heart associationcorrespondence fatima rodriguez md mph division cardiovascular medicine stanford university school medicine 870 quarry rd falk cvrc stanford ca 943055406 email email protectededureferences1 ross eg shah nh dalman rl nead kt cooke jp leeper nj use machine learning identification peripheral artery disease future mortality riskj vasc surg 2016 6415151522e3 doi 101016jjvs201604026crossrefmedlinegoogle scholar2 betancur j otaki 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heart association inchttpsdoiorg101161circresaha118314119pmid 30566055 originally publisheddecember 6 2018 keywordsmachine learningdelivery health carebiomedical researchprecision medicineartificial intelligencepdf download advertisement subjectscardiovascular diseasemachine learning artificial intelligenceprecision medicine