"AI Meets Medicine: A Quantitative Study of Growth, Market Value, and Wearable Innovations in Health by IRJET Journal

"AI Meets Medicine: A Quantitative Study of Growth, Market Value, and Wearable Innovations in Healthcare"

- Samaira Mittal

Abstract: Integration of Artificial Intelligence (AI) in healthcare is giving birth to a new era in modern medicine by redefining the landscape through diagnostic accuracy improvement, clinical workflows streamlining, enabling personalized treatment, and expanding remote care capabilities. This article discusses various applications of AI across healthcare domains such as medical imaging, predictive analytics, virtual assistants, and chronic disease management. Special focus is given to the part of AI-operated wearable devices and health applications in the process of continuous health monitoring and the early detection of illnesses. At the same time, the author also presents a quantitative analysis of the industry’s financial growth, which is based on current and future revenue data of the biggest companies like Apple, Fitbit (Google), Samsung, and Garmin. The global wearable healthcare market, which amounts to $70–80 billion in 2024, estimates being more than $150 billion in 2028 while the digital health app sector forecasts the growth from $10 billion now to $30 billion in 2030. Different types of revenue models such as sales of hardware, subscription services, B2B licensing, and corporate health partnerships are discussed critically. This research dwells into qualitative and quantitative parts of the topic at the same time, illustrating how AI-powered solutions are not only responsible for patient outcomes but also bring significant economic benefits to the global healthcare industry. AI-powered tools are quietly reshaping global healthcare economics by tightening operations, trimming expenses, and directing resources where they matter most. By taking over repetitive tasks such as preliminary diagnostics, paperwork, and constant patient checks, AI eases overloaded staff and slashes everyday running costs. Its predictive models can steer patients away from avoidable readmissions, fine-tune treatment road maps, and catch errors before they become expensive. In drug development, smarter algorithms speed up candidate screening and trial oversight, saving the industry billions usually tied up in lengthy labs and paperwork. Wearable devices with built-in AI also keep patients monitored at home, cutting the frequency of costly hospital visits and easing pressure on wards and staff. Taken together, these gains boost productivity, lift patient care, and give health systems the wiggle room to spend less while still delivering reliable service, yielding solid savings that widen over time.

Key Words: Wearable, technology, WOOP, Monitoring

1. Introduction:

Artificial Intelligence(AI) is Changing the WayHealthcare isDeliveredGlobally,fromreactive to proactive,personalized, anddata-informedhealthcareimprovements.AIhasshownitcanmakediagnostic processesmoreefficientandaccurate, automate administrative tasks, enhance clinician decision-making, and lead to early detection of illness using machine learning models, predictive analytics, and natural language processing. The transition from healthcare to AI not only improves all patient outcomes, along with the operational efficiency of workflow, but also enhances the overall patient experience and new health delivery models such as telemedicine, remote monitoring, and virtual care are entering the mainstream. Virtualcareischangingthewaypeopleaccesshealthcarebylettingthemtalktodoctorsandspecialistsright from home, cutting down on long waits and the expense of travel. The technology supports ongoing, real-time conversations between patients and providers, helps manage chronic conditions more smoothly, and makes early treatment much easier to arrange. By easing the strain on hospitals and clinics, this new approach spreads high-quality caretoremoteareasandcommunitiesthathavebeenoverlookedfortoolong.

The accelerated advancement of wearable fitness bands/smart watches and AI to augment mobile health applications is one of the most promising parts of this continuum. These assistive tools allow for ongoing assessments of heart rate, oxygen saturation, sleep behaviour, active days, and even ECG. AI can provide personalized recommendations based on real-time health data with sophisticated models which collect and analyze patients’ physiological data that alerts for 'normal'and'unusual'healtheventswhileforecastingillnessrisk.

Consequently,wearablesandhealthapplicationsarenotonlybeingtreatedand/orrecognizedasdevicesforwellnessand physical activity/sportsmeasures, but moreandmoretheyare beingidentified in their clinical valueor relevancetothe early detection/diagnosis and management of chronic conditions such as cardiovascular diseases, diabetes and sleep disorders.

Aboveandbeyondtheseadvancementsintechnology, AIinhealthcareisalsoamajoreconomicopportunity.Themarket forAI-enabledhealthcaresolutions,especiallywellness,wearablesandmobileapplications,hasincreasedsignificantlyin

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thelastfewyears.In2024thewearablehealthcaremarketwill likelybeestimatedtobe$70 - $80billionwith increases past$150billionin2028;likewise,inthelastfewyearsthedigitalhealthappmarketvaluedaround$10billionisexpected toexceed$30billionby2030.Theseopportunitiesaredrivenbytheincreasedconsumerdemandforhealthmanagement tools,theincreasedinterestfromtheinsuredandemployersinpreventativehealthmeasures,andthemajorinvestments beingmadebySiliconValleytechcompaniesandhealthstartups.

The objectives of this paper are twofold, with a primary focus on the application of AI in healthcare and the economic implications of AI-enabled wearables and health app technology. This focuses on key stakeholders, business models, marketgrowth,revenuegrowthandchallengesofthisindustryasitgrowsandthefutureeconomicoutlook.Byproviding a quantitativeassessmentof theeconomicdata andqualitativeassessmentofAIadoptionimplications,thispaperserves asanimportantopportunitytolearnabouthowAI-enabledwearablesandhealthappswillshapethefutureofhealthcare deliveryandeconomics.

2. Applications of AI in Healthcare:

Artificial Intelligence (AI) is being applied across a wide spectrum of healthcare domains, enhancing clinical precision, reducing workload, and enabling personalized patient experiences. The versatility of AI technologies particularly machine learning, deep learning, and natural language processing has enabled innovations in both patient-facing and operationalaspectsofhealthcaresystems.

1. MedicalImagingandDiagnostics:

 AI algorithms are widely used to interpret diagnostic images such as X-rays, CT scans, MRIs, and mammograms. These systems can find patterns, abnormal findings, or earliest signs of diseases with greataccuracy,andmayevenexceedhumanperformanceonsometasks.

 Example: Google's DeepMind invented an AI-based system that performed better than radiologists to detectbreastcancer.

 Impact:Early,accuratediagnosisofcancer,fractures,brainabnormalities,andmore.

2. PredictiveAnalyticsandRiskScoring:

 AI models can review large datasets from EHRs, results from format tests and wearable technology connected to the patient and detect patterns to identify likely complications, readmissions and the possibilityofadiseasebeingcontracted.

 Example:AIcandetectpatientsathighriskofsepsisandstrokehoursearlythancliniciansuspects.

 Impact:Timelyintervention,decreasedmortalityandimprovedresourceallocation.

3. AI-basedWearablesandRemoteMonitoring:

 Wearables, including smartwatches, fitness bands, and biosensors, continually collect basic health data whichAIusestointerpretdatatoidentifyirregularitiesorpatterns.

 Example:AppleWatchutilizesheartrhythmmonitoringtodetectatrialfibrillation.

 Impact:Remotepatientcare,chronicconditionsthroughcontinuinginterventionsandpreventivecare.

4. PersonalizedMedicineandGenomics:

 AIcananalyzeandsynthesizegeneticdatatocreatetreatmentplansthataretailoredtoapatient'sDNA, lifestyle,andenvironmentalinfluences.

 Example: In oncology, AI can connect patients with the most effective cancer therapeutics based on patienttumourgenomics.

 Impact:Greaterefficacyoftreatmentandlessadversedrugreactions.



5. VirtualHealthAssistants&Chatbots:

 AI-basedchatbotsandvoiceassistantscandeliverbasicmedicaladviceandmentalhealthsupportaswell asreminderstotakemedicationorattendappointments.

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 Example: Babylon Health and Ada Health provide AI-enabled symptom screening by using chatbots to engageandsharedecisionswithusers.

 Impact:Providesincreasedaccessibility,especiallyforindividualsinremoteorunderservedlocations.

6. DrugDiscoveryandDevelopment:

 AI expedites the drug development process by identifying possible compounds, anticipating results, and designingtrials.

 Example:Atomwiseusesdeeplearningtofindnewdrugcandidatesandrepurposeexistingdrugs.

 Impact:Reducesthecostandtimeoftraditionaldrugdiscovery.

7. ClinicalDecisionSupportSystems(CDSS):

 AI-enabled CDSS offers evidence-based recommendations to clinicians by evaluating patient’s history, currentsymptoms,andthemedicalliterature.

 Example: IBM Watson for Oncology provides treatment options based on the patient in real-time by synthesizingthedata.

 Impact:Improvesthequalityofcliniciandecision-makingandprovidesconsistentpatientcare.

8. RoboticSurgeryandAutomation:

 AI uses robotic surgical systems to enhance the precision of surgical procedures and minimize complicationsandrecoverytimes.

 Example:ThedaVinciSurgicalSystemusesAIassistanceinminimallyinvasiveoperations.

 Impact:Greateraccuracyinsurgicaloperationleadstoimprovedpatienthealthoutcomes.

9. HealthcareOperationsandAdministration:

 AI tools automate the administrative chores of healthcare; billing, insurance claims and various operationssuchasschedulingofappointmentsandclinicaldocumentation.

 Example:Imagineifnaturallanguageprocessingisusedtoautomatethetranscriptionofphysiciannotes.

 Impact:Reduceclinician’sburnoutandenhancetheefficiencyofoperations.

3. Benefits of AI in Healthcare:

1. Better Diagnostic Accuracy: AI algorithms are able to find patterns and irregularities in medical images and patient information with high precision, reducing the opportunity for diagnostic error and facilitating early diseasedetection.

For Example: AI areas in radiology are becoming and producing subsections to recognize tumors, fractures, or internalbleedingwithgreaterconsistency.

2. Faster and Effective Decision-Making: AI supports clinicians in rapid decision-making with confidence through theanalysisofsophisticatedandmassivedatasetsutilizingstructuringevidence-basedsub-sets.

Outcome:reductionindecisionfatigueandimprovedcaretimelines.

3. PersonalizedandPreventiveCare: AI permitsindividualizedtreatment plansbased ongeneticprofiles,lifestyle, andclinicalhistory.

Advantage:improvedpatient'soutcomesandreductioninlong-termcosts.

4. Operational efficiency and reduction in cost: The automation of administrative tasks such as billing, scheduling, andrecordsdocumentationwillallowhealthcareproviderstodevotemorespecifictypeoftimetopatientcare.

Effect: A reduction in labour costs, a reduction in opportunities for errors, and an overall improvement in workflow.

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5. Remote Monitoring and Accessibility: AI-enabled wearables and virtual health assistants facilitate the ability to monitor health constantly, and offer further support, especially useful in areas that are remote or lack an abundanceofcare.

UseExample:Managementofchronicdiseasewithoutvisitingafacilityfrequently.

4. Challenges of AI in Healthcare:

1. Privacy and Security of Data: AI systems involve large amounts of sensitive patient data. Risk: Data breaches, unauthorizedaccess,misuseofpersonalhealthinformation.

2. BiasandFairness:AImodelstrainedonbiaseddata cangivedisparatetreatmentrecommendationstodifferent demographic groups. Example: If a given population group is underrepresented in the training data, algorithms mayunderperformforthatgroupiftheunderlyingdistributiondiffersfromtrainingdata.

3. LackofTransparencyandExplainability:SomeAImodels,especiallydeeplearningsystems,mayberegardedas blackboxes;itcanbedifficulttodeterminehowtheyarriveatdecisions.Concern:Trustissuesamongstclinicians andpatients,especiallyinthepresenceofhigh-stakesdecision-making.

4. Regulatory and Legal Barriers: AI technologies must undergo clinical validation and respective regulatory approval. Regulatory and legal approval of AI technologies can be time-consuming, vary by jurisdiction, and createchallengeswithalackofstandardizedframeworksforapprovalofAIinhealthcaresystems.

5. Integration with Existing Systems: Implementing AI in existing hospital IT systems can present challenges and mayinvolveissuesofinteroperabilitywithcareprovidersystemssuchasEHRsorlegacysystems.

6. Resistance to Adoption: Some care providers may be reluctant to use AI technologies due to potential job disruption, loss of control of their profession, or lack of familiarity with these models. Solution: Ongoing education on AI models to instil confidence and training, and include these stakeholders in the design and implementationofAIsystems.

5. Role of Wearables and Apps in AI-Enabled Healthcare

1. ContinuousHealthMonitoring:

 Devices such as Fitbit, Apple Watch, Garmin, and Xiaomi Mi Band automatically log vital signs of interest including: heart rate, oxygen saturation (SpO₂), sleep patterns, steps, calories burned, and ECG in some products.

 AI examines trends in daily logged data to identify potential abnormalities including arrhythmias, sleep apnea,andvariationofnormaloxygenlevels.

2. EarlyDetectionandPredictiveAnalytic:

 AI algorithms use wearable data to identify if there are early warning signs of an evolving problem of hypertension,diabetes,atrialfibrillation,andevendepression.

 For example, Apple Watch uses data and device algorithms to identify irregular heart rhythms that could indicatethepresenceofatrialfibrillationbeforeanyothersignspresent.

3. PersonalizedAdjustmenttoHealthandFitnessRecommendations:

 AIusesuserinputandongoingactivitydata(e.g.,levelsofactivity,sleepquality,stresspatterns,andfitness goals)tomakepersonalizedadjustmentstotheirworkout,diet,andlifestylerecommendations.

 ManyApps, includingGoogleFit, Samsung Health,and MyFitnessPal plugintoAIto adaptfitnessplansin realtime.

4. ManagementofChronicDisease:

 Forpatientslivingwithdiabetes,hypertension,orcardiovasculardisease,wearablesprovideawaytotrack dailyvitalsandadherencetomedications.

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 AIwillsignalwhenpatientsorthepatient'sdoctor,needtointerveneasthepatientcontinuestotracktheir vitalsignsweekly,andcouldmaketheirvitalsignreadingsavailableandmonitoredbya physicianinreal time.

5. MentalHealthandStressResponse:

 Devices track stress levels, and the wearable would measure the user's heart-rate variability, or galvanic skinresponsetodeterminestressresponseactivity.

 Whentheuserfindstheirstressresponseto beelevated orbecoming problematic,theycoulduseanapp such as Headspace or Wysa, which uses AI algorithms to recommend meditation routines or suggest emotionalsupportstepsbasedonuserinputre:theirfeelingsandupdatedbiometricmapping

6. IntegrationwithHealthSystems:

 Some wearable data uploads directly into Electronic Health Records (EHRs) giving physician's timely metricsdirectlyandallowingforinformeddecision-making.

 FacilitateRemotePatientMonitoring(RPM),especiallyforelderlyorpatientswithmobilityissues.

6. Companies and Products Using AI in Healthcare Wearables:

1. AppleInc.

 Product:AppleWatchSeries

 AIFeatures:ECG,AFibdetection,SpO₂monitoring,falldetection

2. Google(Fitbit&PixelWatch)

 Products:FitbitSense,Versa,PixelWatch

 AIFeatures:HRV,sleepanalysis,stressdetection,long-termhealthpatternrecognition

3. Samsung

 Products:GalaxyWatchSeries

 AIFeatures:ECG,bloodpressuremonitoring,bodycompositionanalysis,sleepscoring

4. Garmin

 Products:Forerunner,Fenix,VenuSeries

 AIFeatures:VO2Max,fitnessandrecoveryAImodels,HRVanalytics

5. WHOOP

 Product:WHOOPStrap

 AIFeatures:AI-poweredrecoveryandstrainanalytics,sleepperformanceprediction

6. Withings

 Products:ScanWatch,BPMCore

 AIFeatures:ECG,sleepapneadetection,vascularagecalculation

7. OuraHealth

 Product:OuraRing

 AIFeatures:Sleepstageprediction,illnessdetectionviatemperatureandHRVtrends

8. BioIntelliSense

 Products:BioSticker,BioButton

 AIFeatures:ContinuousremotepatientmonitoringwithAI-basedearlywarningalerts

9. Empatica

 Products:EmbracePlus,E4

 AIFeatures:Seizuredetection,stressandemotionalstatetracking,wearableforclinicalresearch

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RevenueofSmartwatchesmarketworldwide (inbillionUSD)

Fiq1:Revenueofsmartwatchesmarketworldwide(inbilliondollars)

Source-https://www.statista.com/outlook/hmo/digital-health/digital-fitness-well-being/fitness-trackers/smartwatches/worldwide

GlobalSmartwatchRevenueShare(ByApplication)

Fiq2:Globalsmartwatchrevenueshare(byapplication)

Source-https://www.gminsights.com/industry-analysis/smartwatch-market

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Volume: 12 Issue: 07 | Jul 2025 www.irjet.net p-ISSN:2395-0072

SmartwatchBrand%Share(Global)

Fiq3:SmartwatchBrand%Share(Global)

Source - https://www.forbes.com/sites/johnkoetsier/2021/05/27/global-smartwatch-market-apple-34-huawei-8-samsung-8-fitbit42/

7. Future of AI in Healthcare

1. PersonalizedMedicine

AIwillassistincreatingindividualizedtreatmentplansusingthepatientsgenetics,lifestyleandmedicalhistory, allofwhichcanenhanceaccuracyandoutcomes.

2. RemoteMonitoringandTelehealth

WearablesandhomemonitoringdevicesusingAItechnologywillallowdoctorstomonitorpotentiallyemergency vitalsignsandsymptomsinreal-time,allowingthemtointervenemoretimelyfromadistance.

3. AutomatedDiagnostics

AIwillbeusedinreadingX-rays,MRIs,andlabresultsfasterandmorereliablythananyonecan.AIwillalwaysbe superiorindiagnosingtumors,fractures,andextrabonegrowth,etc.

4. RoboticSurgeryandVirtualHealthAssistants

TheuseofroboticsurgeryandotherAIdeviceswillenhancetheprecisionofasurgicalprocedurecomparedtoa surgeon doing the same procedure without robotic assistance. Virtual health assistants will help patients set personalreminders,tracksymptomsandcouldevenhelpthemmanagementalhealth.

5. DrugDiscoveryandDevelopment

AI will be used to more rapidly identify potential drug candidates, predict what these drugs will do and more rapidly, and at a lower cost help companies take pharmaceutical drugs to market. Mental Health Monitoring- AI willtrackpatternsofvoice,typingresponse,andwearablesdatatopotentiallyidentifyearlysignsofdepression, anxiety,andburnoutsotheygethelpimmediately.

Apple Huawei Samsung iMoo Fitbit Other

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6. EHR/ClinicalDataIntegration

AI will automatically identify and summarize information across EHRs by extracting patient information that is relevantandimportant,andclinicianswon’thavetodoanyofthework.Withaccesstosomuchinformationand data, the clinician will improve their quality of diagnosis and medical decisions about the diagnosis are more timelyandpurposeful.EthicalandLegalConsiderations-FuturechallengesforAIwillcomefromdataprivacy,AI algorithmbias,andethicaluse,andwillrequiremoreregulationsandethicallimitstouseAIorelseweriskusing itdangerously.

8. Calculations:

a. RevenueduetoAIinallindustries:

Ingraph,x-axisasyearsfrom2015to2025andY-axisshowRevenueduetoAIinallindustries(inUSDbillion dollars)

Let’sconsider2015as1,2016as2……..2025as11. Aftermanuallydrawinggraphanonexcel. Weconcludedexponentialequationcoversmaximumnumberofplots

Consideringy=A.Bx Where:

 yistherevenueduetoAI(inUSDbillion),

 xistheyearindex(e.g.,2015as1,2016as2,...,2025as11),

 Aistheinitialrevenue(whenx=0),

 Bisthegrowthfactor(i.e.,howmuchtherevenuemultipliesperunitincreaseinyearindex).

Formula: y = 3*(1.2999)x

Where“y’isRevenueduetoAIinallindustries(inUSDbilliondollars) And“x”represents1,2,3……(1represents2015,2represents2016andsoon.)

RevenueduetoAIinallindustries

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b. RevenueduetoAIinHealthcare:

Ingraph,x-axisasyearsfrom2015to2025andY-axisshowrevenueduetoAIinhealthcare.

Let’sconsider2015as1,2015as2……..2025as11.

Aftermanuallydrawinggraphanonexcel.

Weconcludedbiquadraticequationcoversmaximumnumberofplots

Consideringy=A.Bx

Where:

 yistherevenueduetoAIinHealthcare(inUSDbillion),

 xistheyearindex(e.g.,2015as1,2016as2,...,2025as11),

 Aistheinitialrevenue(whenx=0),

 Bisthegrowthfactor(i.e.,howmuchtherevenuemultipliesperunitincreaseinyearindex).

AIHealthcare(USBillionDollars)

Calculated

Formula: y = =0.49*x1.72

Where“y’isrevenueduetoAIinhealthcare(inUSDbilliondollars) And“x”represents1,2,3……(1represents2015,2represents2015andsoon.)

c. AIMarketWorldwide:

Ingraph,x-axisasyearsfrom2015to2025andY-axisshowAImarketworldwide(inbillionUSD) Let’sconsider2015as1,2016as2……..2025as11. Aftermanuallydrawinggraphanonexcel.

Weconcludedbiquadraticequationcoversmaximumnumberofplots

Consideringy=A.Bx

Where:

 yisAIMarketworldwide(inUSDbillion),

 xistheyearindex(e.g.,2015as1,2016as2,...,2025as11),

International Research Journal of Engineering and Technology (IRJET) e-ISSN:2395-0056

 Aistheinitialrevenue(whenx=0),

 Bisthegrowthfactor(i.e.,howmuchtherevenuemultipliesperunitincreaseinyearindex).

AIMarketWorldwide(inbillionUSD)

Formula: y = 3.18*(1.3997)x

Where“y’isAIMarketworldwide(inUSDbilliondollars) And“x”represents1,2,3……(1represents2015,2represents2016andsoon.)

d. FutureofAI:

In graph, x-axis as years from 2020 to 2030 and Y-axis show AI market worldwide in coming years (in billionUSD)

Let’sconsider2020as1,2021as2……..2030as11. Aftermanuallydrawinggraphanonexcel.

Weconcludedbiquadraticequationcoversmaximumnumberofplots Consideringy=A.Bx

Where:

 yismarketofAIincomingyears(inUSDbillion),

 xistheyearindex(e.g.,2020as1,2021as2,...,2030as11),

 Aistheinitialrevenue(whenx=0),

 Bisthegrowthfactor(i.e.,howmuchtherevenuemultipliesperunitincreaseinyearindex).

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AI

Formula: y = 42.76*(1.46)x

Where“y’isAIMarketworldwideincomingyears(inUSDbilliondollars) And“x”represents1,2,3……(1represents2020,2represents2021andsoon.)

e. RevenueduetoFitnessbandsandappsinUS(inbilliondollars):

Ingraph,x-axisasyearsfromQ12017toQ42020(eachyearthereare4qauters) andY-axisshowrevenueofFitnessbandsandappsinUS(inbilliondollars) Let’sconsiderQ12017as1,Q22017as2……..Q42020as16 Aftermanuallydrawinggraphanonexcel.

Weconcludedbiquadraticequationcoversmaximumnumberofplots Consideringy=A.Bx

Where:

 yistherevenueduetofitnessbandsandappsinUS(inUSDbillion),

 xistheyearindex(e.g.,Q12015as1,122015as2,...,Q42020as16),

 Aistheinitialrevenue(whenx=0),

 Bisthegrowthfactor(i.e.,howmuchtherevenuemultipliesperunitincreaseinyearindex). 0

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RevenueofSmartsBandsandAppsinUS(inbillionUSD)

Formula: y = 47.11*(1.11)x

Where“y’isAIMarketworldwideincomingyears(inUSDbilliondollars) And“x”represents1,2,3……(1represents2020,2represents2021andsoon.)

9. Conclusion:

TheinclusionofArtificialIntelligenceintohealthcareisachanging pointinhowmedicalservicesarecreated,distributed, and used. This research has established that AI has moved from vision to action and is a catalyst for innovation in healthcare diagnostics, treatment protocols, operational efficiencies, and person-centered medicine. Whether discussing the myriad of machine learning algorithms analyzing complex medical data, or wearable-monitoring devices collecting real-timehealthdata,AIisexpandingpossibilitiesinpreviouslyconstrictedhealthcaremodels.

Throughrevenuecomparisons,IwilldemonstratethatAIasanindustryisgrowingrapidly,andAIasahealthcaresectoris growingmuchmorerapidly.MarketresearchsuggeststhatwhileAIasawholemayexceedthehundredsofbillionsvalue range, AI for healthcare is estimated to grow at a compound annual growth rate (CAGR) over 40%, which is faster than manyotherareas.ThissuggestsahighdemandandconfidenceinAI'sroleinthehealthcareecosystem.

The rapid expansion of wearable technology especially smart fitness bands, watches with ECG capabilities, and mobile health apps powered by artificial intelligence also continues to validate this trend. These wearable devices provide meaningful, time series, continuous and quality health data to augment AI model's predictive capabilities, and ultimately improvepatientoutcomes.CompanieslikeApple,Fitbit,andXiaomiarealreadywellpositionedintheburgeoninghealthtecheconomy.

TheexponentialgrowthgraphofAIinhealthcaremarksnotjustatechnologytrendbutalsoaninflectionpointofchanging consumerandinstitutionaltrust.Hospitals,startups,andgovernmentsareinvestingmoremoneyinAI-basedsolutionsto improvepatientcare,addresscostandpatientpressures,andfillworkforcegaps.

But as we enter into a phase of the implementation of AI, the health sector needs to address key concerns such as data privacy, algorithmic bias, legislative compliance, and transparency of AI models in health. The ethical and equitable implementationofAItoolswillbenecessarytocreatetrustandasustainableapproachforlong-termviability.

AsEricTopol,aleadingdigitalmedicineresearcher,said: “The future of medicine will be driven by the convergence of human intelligence and artificial intelligence.”

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Insummary,thefinancialandfunctionalfutureofAIinhealthcareissurelyinthepositivedirection.Theupwardrevenue growth, increasing uptake of smart devices, and continued growth in the function of AI all support a future where healthcare is more intelligent, predictive, and accessible. With proper guidance, this progression can have a significant impactonglobalhealthcaresystemsandpopulations'health.

Current economic data and on-the-ground examples together make a strong case for artificial intelligence in health care. Thetechnologyisnotjustaresearchcuriosity;itisalreadydrivingrevenue,withthehealth-caresegmentoftheAImarket growingatover40percentayear-fasterthanthesectorasawhole.EverydayproductslikeApplessmartwatchandFitbits healthtrackershowhowthistechnologyhasslippedintoroutinepatientmonitoring.Hospitals,early-stagecompanies,and evengovernmentagenciesarepumpingmoneyintoAI,amovethatreflectstheirgrowingtrustintoolsthatpromisebetter clinical results, smoother operations, and broader access to services. Taken together, these signs point to a health-care systembeingquietlybutprofoundlyre-engineeredbyscalableandsustainableintelligenttechnology.

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International Research Journal of Engineering and Technology (IRJET) e-ISSN:2395-0056

Volume: 12 Issue: 07 | Jul 2025 www.irjet.net p-ISSN:2395-0072

12. CBInsights.

13. AI 100: The Most Promising AI Startups in Healthcare.AccessedMay22,2025. https://www.cbinsights.com/research/report/artificial-intelligence-top-startups-healthcare/

14. GlobalMarketInsights.

15. Wearable Medical Devices Market size worth $180 Bn by 2032.AccessedMay21,2025. https://www.gminsights.com/industry-analysis/wearable-medical-devices-market

16. Worldwide Artificial Intelligence Spending Guide.AccessedMay22,2025. https://www.idc.com/getdoc.jsp?containerId=prUS49117022

17. NatureMedicine.

Topol,Eric.2019. High-performance medicine: the convergence of human and artificial intelligence.NatureMedicine 25(1):44–56.https://www.nature.com/articles/s41591-018-0300-7

18. NIH/NLM.

Jiang,Fei,etal.2017. Artificial intelligence in healthcare: past, present and future.StrokeandVascularNeurology 2(4):230–243.https://doi.org/10.1136/svn-2017-000101

11. Biography:

Samaira Mittal

Class:12thNon-medical

Subjects:Physics,Chemistry,maths,English,Informaticspractices

EXTRACURRICULARS:

INTERNATIONALLEVEL

- Achieved 1st School Rank, 1164th International Rank, 496 Zonal Rank, and 54th Regional Rank in the lOS International Olympiad of Science

- Achieved 2nd School Rank, 1465th International Rank, 727 Zonal rank, and 47th Regional Rank in the IOM International Olympiad of Maths

NATIONAL LEVEL

- Aryabhatta National Maths competition

- Maths kangaroo competition.

ONLINE COURSES

- Using Basic Formulas and Functions in Microsoft Excel, Coursera Project

- Inspiring Leadership through Emotional Intelligence, Case Western Reserve University, Coursera

- Learning and improvising Spanish Language skills from Duolingo with the help of its interactive lessons, practical exercises, and self-paced learning approach

SOCIALWORK

- Co-founder of Hustle and Hope

- Drug free world

Under the guidance of:

Dr. Mamta Jain

-M.Sc (Mathematics) (Double gold medalist)

- M.Phil (Computer Applications) with honours From University of Roorkee (now IIT Roorkee)

-PhD (Mathematics)

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International Research Journal of Engineering and Technology (IRJET) e-ISSN:2395-0056

Volume: 12 Issue: 07 | Jul 2025 www.irjet.net p-ISSN:2395-0072

-Various papers published in international journals

-Former Lead Auditor ISO 9001, ISO -22000 School Accreditation Examiner by QCI -26 years of teaching experience

Er. Raunaq Jain

-B.E Mechanical Engineering From Thapar Institute of Engineering and Technology. -School Physics Topper.

-Mechanical Mentor from session 2019-2020.

-Technical Data Analyst at Deloitte.

-Multiple research papers published.