International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 12 Issue: 03 | Mar 2025 www.irjet.net p-ISSN: 2395-0072
Harnessing AI and Quantum Computing for Enhancing Supply Chain and Healthcare Operations: A Comprehensive Survey
Aman Garodia1 , Saurabh Pahune2 , Noopur Rewatkar3
1Capella University, USA
2Tata Consultancy Services, USA
3Independent Researcher
Abstract - Thearticleexplorestherevolutionaryeffectsof artificialintelligence(AI)onhospitaloperationsandsupply chainmanagement,whichincludequantumcomputationand largelanguagemodels(LLMs).Weinvestigatethepotential ofstate-of-the-arttechnologies,suchasquantum-enhanced models,roboticsprocessautomation(RPA),andgenerative artificialintelligence,toenhancedecision-making,optimize processes, and facilitate innovation in these sectors. Our discourse incorporates these technologies. This paper providesathoroughexaminationoftheopportunitiesand challenges that are linked to the convergence of quantum computingandartificialintelligence.Itaccomplishesthisby utilizingrecentresearch,whichencompassesadvancements in cognitive automation, artificial intelligence in rural healthcare, and quantum-enhanced supply chain optimization.
1. INTRODUCTION
Thefunctionofartificialintelligenceinoptimizingbusiness andhealthcareproceduresisexpandingrapidly.Theanalysis oftextandimagesisbeingalteredbytechnologiessuchas Generative Artificial Intelligence (AI) and Large Language Models (LLMs), which alsooffer new methods to enhance operational efficiency and decision-making. These innovations enable organizations to accomplish their objectives by automating activities, improving customer relationships, and fostering innovation across industries. The identification of diseases and the development of personalizedtreatmentregimensbymedicalprofessionals are being transformed bydiagnostic technologiesthatare powered by predictive analyticsandartificial intelligence. This is resulting in enhanced patient outcomes and a reductionincosts.
Theimplementationof quantumcomputingintroduces an additionalstratumofpotential,enablingthedevelopmentof solutionsthatsurpassthelimitationsofclassicalcomputing. Quantumcomputinghasthepotentialtoenhanceartificial intelligencemodels,particularlyincomplexsystemssuchas supply chain management, by optimizing algorithms and enhancingdataprocessingcapabilities.Thecapacityofthis technologytoassessvastdatasetsandoptimizeoperations in real-time has the potential to significantly improve logistics,inventorymanagement,anddemandforecasting.
This will ultimately lead to cost reductions and a more efficientabilitytomakedecisionsrapidly
The objective of this study is to examine the potential applications of artificial intelligence (AI) and quantum computationinthefieldsofsupplychainmanagementand healthcare. These industries are crucial not only for the global economy but also for the well-being of the public. Artificial intelligence algorithms are currently assisting in thesimplificationofsupplychainoperationsbyenhancing efficiency, reducing waste, and identifying disruptions. Quantumcomputinghasthepotentialtorevolutionizethese applications by providing enhanced optimization for resource allocation and logistics. In the medical field, artificial intelligence systems are already assisting in the identificationofdiseases,thedevelopmentofpersonalized treatments,andthediscoveryofnoveldrugs.Furthermore, the integration of quantum computation and artificial intelligencemayfacilitatethedevelopmentofmodelsthat aremorepreciseintheirabilitytopredicttheoutcomesof patienttreatmentandtomanagehealthcareresourcesmore effectively.
Thesetechnologieshavethepotentialtoaddresssomeofthe most severe issues currently being encountered in the healthcare and business sectors when considered in their entirety. They offer solutions that are more cost-effective, scalable, and efficient, and they have the potential to revolutionize these industries. On the other hand, the incorporation of artificial intelligence with quantum computingwouldrequiretheresolutionofissuesrelatedto data security, ethical considerations, and hardware limitations. The appropriate application of these sophisticated technologies is contingent upon the satisfactionofthesechallenges.
2. LARGE LANGUAGE MODELS (LLMS) AND THEIR ROLE IN SUPPLY CHAIN
2.1 Overview of LLMs and Their Applications
Strong tools capable of processing and analyzing vast quantities of textual data, such as Large Language Models (LLMs)likeGPT-3,haveemerged.TheseLLMshavebecome essentialforindustriessuchassupplychainmanagementas a result. The ability of these models to effectively manage
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 12 Issue: 03 | Mar 2025 www.irjet.net p-ISSN: 2395-0072
complexdatasetsenablesbusinessestoautomateadiverse arrayofprocessesthatwerepreviouslytime-consumingand labor-intensive.Theevaluationofhistoricaldatatoestimate demand, optimize inventory management, and expedite logisticaloperationsisataskthatLLMscanhelpwithinthe context of supply chain management. In addition, they improve communication across departments, automate reporting, and produce summaries to enhance the documentation processes. The utilization of LLMs enables organizations to make data-driven decisions more effectively,therebysignificantlyenhancingtheiroperational efficiencyandstrategicplanning.AsperPahuneetal.(2023), thesetechnologiesareresultinginatransformationinthe manner in which businesses interact with data, which is resultinginanincreaseinbothperformanceandagility[1].
2.2 Challenges in Supply Chain Optimization
Several obstacles must be overcome in order to achieve successful integration when deploying Large Language Models(LLMs)insupplychains.Thisisdespitethefactthat LLMshavesignificantpotential.Oneoftheprimaryconcerns istheethicalcollectionofdata,whichiscrucialforensuring the quality and fairness of the data used to train these models.Thisisnecessarytomitigatetheeffectsofbiasesand maintain conformance with regulatory standards. Furthermore, the interpretability of models remains a significantissue.AlthoughLLMsarecapableofgenerating precisepredictions,the"black-box"natureofthesemodels oftenrendersitchallengingforstakeholderstounderstand the decision-making process, which can impede trust and influence adoption. Another challenge is the demand for high-quality, large-scale training datasets, which can be resource-intensiveandmaynotalwaysbereadilyavailable. Thisisparticularlytrueforbusinessesthathavearestricted quantity of digitized data. In addition, the integration of LLMsintothecurrentsupplychainarchitectureisacomplex process that necessitates a substantial degree of technical expertise.Organizationsareaccountableforthemeticulous incorporation of contemporary artificial intelligence technologies with legacy systems in order to ensure seamlessinteroperabilityandscalability.Itisimperativeto overcome these challenges in order to fully realize the potentialofLLMsinsupplychainoptimizationoperations,as notedbyPahuneetal.(2024)[2].ArtificialIntelligence(GEN AI) provides a thorough examination of the distinct advantages and disadvantages that each of these technologies possesses in the context of supply chain management.Thestudydemonstrateshoweachtechnology contributestotheoptimizationofvariousaspectsofsupply chainoperationsbyexaminingthecapabilitiesofboth.These elements encompass the optimization of decision-making processesandtheautomationofrepetitivetasks.
The objective of this study is to examine the evolving landscape of cognitive automation in supply chain environments,withaparticularfocusonthecontributionsof
GENAIandRPAtotheproductionofstrategicinsightsand operational efficiency. GEN AI introduces intelligent decision-making,whichallowssystemstoevolveandadapt over time. GEN AI introduces intelligent decision-making capability, despite the fact that RPA is highly proficient in automatingrule-basedoperationswithexceptionalprecision and speed. This paper provides a comprehensive examination of the ways in which general artificial intelligence(GENAI)androboticprocessautomation(RPA) are transforming supply chain management, enhancing workflowautomation,andenhancingdata-drivendecisionmaking. This is accomplished by examining the ways in whichthesetechnologiescomplimentoneanother[3].
3. QUANTUMCOMPUTINGANDITSROLEINSUPPLY CHAIN OPTIMIZATION
3.1 Quantum-Enhanced Generative Adversarial Networks (QGANs)
The transformative potential of quantum computing in augmenting the capabilities of Generative Adversarial Networks(GANs),particularlyinsupplychainoptimization, hasbeenrecentlyhighlightedbyrecentstudies.Quantumenhanced GANs, or QGANs, offer substantial benefits by offeringmoreprecisepredictionsandoptimizedsolutions forintricatesupplychaintasks,includinglogisticsplanning, inventorymanagement,androuteoptimization.QGANscan process and analyze large datasets with unprecedented efficiency, addressing optimization problems that are computationally intensive for classical methods, by leveraging the power of quantum computing. Quantum models are capable of making real-time, data-driven decisions as a result of their capacity to manage complex variablesandhigh-dimensionaldata,whichresultsinmore cost-effectiveandefficientoperations.Asubstantialquantity of consideration is given to the potential advantages and challenges that may arise as a result of the integration of classicalandquantumcomputingparadigms.Thisresearch addresses a variety of issues, including scalability considerations, error correction techniques, and quantum hardwarelimits.Theseareallcriticalconcernsfortheactual implementationofquantum-enhancedGANs[4].
4. THE ROLE OF AI IN HEALTHCARE
4.1
Healthcare Innovations Enabled by Generative AI
Ingeneral,thearticleoffersathoroughexaminationofthe healthcare landscape's transformation by Large Language Models (LLMs) and Generative Artificial Intelligence. It illuminatesboththeimmensepotentialandthechallenges thatthesetechnologiespresent.
FunctionofLargeLanguageModels(LLMs):Theresearch underscoresthesubstantialinfluencethatadvancedLLMs,
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 12 Issue: 03 | Mar 2025 www.irjet.net p-ISSN: 2395-0072
including GPT-3, have on the evolution of healthcare. Currently, these models are being employed to address a diverse array of healthcare concerns. They are capable of comprehending and producing language that is strikingly similartothatgeneratedbyhumans. LLMsarecapableof analyzingextensivequantitiesofmedicalliterature,which allows them to offer valuable insights for patient communication,treatmentrecommendations,anddiagnosis. Thiscanbeachievedbyautomatingmedicaldocumentation andimprovingclinicaldecisionsupport.
Generative Artificial Intelligence in Medical Imaging: The paperalsoexploresthetransformativerolethatGenerative AI,specificallyGenerativeAdversarialNetworks(GANs)and VariationalAutoencoders(VAEs),playinmedicalimaging. Theprecisionandaccuracyofmedicaldiagnosticshavebeen substantiallyenhancedasaresultofthesignificantprogress thatthesecutting-edgetechnologieshavemade. Inaddition to enhancing the quality of images obtained from medical imaging,generativemodelsallowmedicalprofessionalsto identifyanddiagnosediseasesatanearlierstage,whenthey are more treatable. This is achieved by optimizing the synthesisandaugmentationofmedicalimages. Thepaperunderscorestheimportanceofethicalgovernance and responsible design, despite the remarkable advancementsthatgenerativeAIhasmade. Thisisinstark contrast to the potential hazards that are linked to generative AI. Although these technologies have the potentialtosubstantiallyimprovehealthcareoutcomes,they are also associated with inherent hazards, such as the generation of medical information that is inaccurate or disingenuous. Thesafetyofpatientsandtheirconfidencein medicalsystemscouldbejeopardizedbytheproliferationof misleading information that is not adequately managed. This underscores the importance of establishing rigorous governancestructuresandnormstoensureaccountability.
Future Directions and Multimodal Models: This study explores the incorporation of a variety of data types, including text and images, into multimodal models to enhancehealthcareapplications,inlinewiththeaccelerated advancements in artificial intelligence. These models, includingELIXR,offersolutionsthatdemonstratepotential for the classification of diseases, semantic search within medicalimages,andotherchallengingscenariosthatoccur in the healthcare sector. By integrating a variety of data types,thesemodelsenhancetheprecisionandefficiencyof healthcareapplications. Consequently,theyprovideamore comprehensiveapproachtothemanagementofpatientsand theconductofmedicalresearch[5].
5. EMBAU: ENHANCING DATA SECURITY WITH STEGANOGRAPHY IN SUPPLY CHAIN COMMUNICATIONS
5.1 Introduction to Embau Technique
Embau, a novel method for integrating audio data within photographs,wasintroducedbyNokhwaletal.(2023).This method employs the Shuffled Frog Leaping Algorithm (SFLA).Thisinnovativemethodisdesignedtoaddressthe growingdemandforsecuredatatransmission,particularly in cases where sensitive information, such as proprietary companystrategiesortransactiondata,mustbeconcealed within data streams that are otherwise harmless. Embau optimizes the pixel selection procedure for embedding to ensure that the cover image has minimal distortion. This renderstheembeddedaudiodataimpervioustosteganalytic attacks and detection [6]. Applications in Supply Chain Management:Itisimperativetoensurethesecurityofdata transmissioninsupplychainmanagement.Thecapacityto encryptandembedsensitivedata,suchasfinancialdetails, logistics information, or proprietary supply chain models, withoutattractingunwantedattentionisacriticalconcern. The Embau method isan ideal candidateforsafeguarding supply chain communications, such as real-time delivery trackingorinventoryupdates,whichfrequentlynecessitate secure channels, due to its capacity to seamlessly incorporateaudiodataintoimages.
Optimizing Security with Embau: Embau guarantees the preservation of the visual quality of the image by incorporatingauditorydataintoimages,therebyminimizing thelikelihoodofdetectionbythird-partysecuritysystems. Thisisespeciallybeneficialinsituationswheresupplychain dataistransmittedovernetworksthatarenotassecure.The embedded audio may contain sensitive business information,includinginventoryforecasts,orderdetails,or logistics data, that must be kept confidential during transmission.Consequently,theEmbautechniqueenhances the security of the communication channel without jeopardizingitsintegrity.
6. ACCELERATING NEURAL NETWORK TRAINING FOR SUPPLY CHAIN OPTIMIZATION
6.1 Introduction to Neural Network Training Challenges
The process of training deep neural networks (DNNs) is typicallyatime-consumingandresource-intensiveendeavor, which presents substantial obstacles in practical applications. A comprehensive overview of strategies to accelerateneuralnetworktraining,acrucialadvancementin machinelearning,isprovidedinthepaperbyNokhwaletal. (2024). These technologies have the potential to have substantial repercussions in the context of supply chain
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 12 Issue: 03 | Mar 2025 www.irjet.net p-ISSN: 2395-0072
management, such as the improvement of the efficacy of demandforecastingsystems,predictivemodels,andlogistics optimization[7].Supplychainoptimization:Thedeployment and enhancement of models in supply chain management aredirectlycorrelatedwiththeaccelerationofthetraining processofDNNs.Forexample,modelssuchasResNet50and Vision Transformer (ViT) that are employed for image recognitionininventorymanagementcanbeoptimizedto provide real-time updates with minimal latency. Furthermore, the study's emphasis on efficiency improvementscanbeadvantageous for predictivemodels thatanticipatedemand,optimizedeliveryroutes,oridentify anomalies in supply chain processes. Thepaperevaluatestheefficacyofstate-of-the-artmodels suchasResNet50andViTondatasetssuchasImageNetand CIFAR100, and includes a comparative analysis and implicationsforsupplychainmanagement.Insupplychain management, these models have a broad range of applications,astheycansimplifyoperationsthroughtheuse ofpreciseandeffectiveimageprocessing.Forinstance,ViT canbeemployedtoautomatequalitycontrolinwarehouse settings,whileResNet50canbeemployedtodetectdamaged products or packaging irregularities in real time during shipment.
7. AI-DRIVEN INNOVATIONS IN RURAL HEALTHCARE SUPPLY CHAINS
7.1 Introduction to AI in Rural Healthcare
Thispaperemphasizesthepotentialapplicationsofartificial intelligence (AI) in rural healthcare to resolve substantial challenges.Thesechallenges,whichfrequentlyimpedethe efficient delivery of treatment, include a lack of skilled healthcare personnel, inadequate infrastructure, and resource restrictions. These difficulties also apply to the healthcare supply chain, where inefficiencies in transportation,inventorymanagement,anddistributionof medicalsuppliescanimpactthequalityofserviceprovided. The integration of artificial intelligence (AI) technologies, including Machine Learning (ML), Natural Language Processing(NLP),RoboticsProcessAutomation(RPA),and DeepLearning(DL),isapromisingsolutionforoptimizing theseareasinruralhealthcare[8].
8.
ENHANCING
SPEECH EMOTION RECOGNITION WITH HIERARCHICAL SAGACITY MECHANISMS
8.1 Introduction to Speech Emotion Recognition (SER)
SER,orspeechemotionrecognition,isacriticalelementof affectivecomputing,asitallowscomputerstocomprehend humanemotionsthroughvoicecommunication. Thisisof paramount importance for applications including virtual assistants,mentalhealthsurveillance,andhuman-computer interaction. It remains a substantial challenge to identify
emotions from speech in environments with challenging acousticsorahighlevelofbackgroundcommotion. Liand Pahune's research study presents a novel approach to improve the accuracy and robustness of SER systems by addressingtheidentifiedissues[9]. TheHSMframeworkisa promising approach for the development of dependable speech-emotionrecognitionsystems. Themodeladdresses boththeprecisionofemotiondetectionandtheadaptability to chaotic surroundings, which provides the potential for moredependableandinterpretableemotionrecognitionin real-world applications.. Theaccuracyof dynamic human interactions may be further enhanced by extending this approachtomanagemulti-modaldata,suchasintegrating speechwithfacialexpressionsorgestures,infuturestudies.
9.STRENGTHENINGIMAGERECOGNITIONAGAINST ADVERSARIAL ATTACKS
9.1IntroductiontoAdversarialChallengesinImage Recognition
The capacity to identify images is indispensable for the operationofadiversearrayofstate-of-the-artdevices,such asself-drivingvehiclesandsurveillancesystems.Conversely, adversarial assaults, which are intentional alterations to input data with the intention of deceiving the model into making inaccurate predictions, have the capacity to significantly erode the performance of image recognition systems.Thesecurityanddependabilityofcriticalsystems areoftheutmostimportance,makingthesevulnerabilities particularlyproblematic.Lietal.investigatetheseissuesand propose a machine-learning approach to enhance the resilienceofpictureidentificationinenvironmentsthatare antagonistic to it. The study underscores the critical importance of developing image recognition systems that are not only precise but also resilient in the presence of adversarialthreats.Theresearchestablishesthefoundation formoresecureanddependableimageidentificationinrealworld applications by incorporating machine learning techniques to strengthen models against assaults of this nature.Theseresultscouldbefurtherdevelopedinfuture research to enhance resilience in a variety of operational contexts, including healthcare systems and supply chain monitoring,wheresecurityandreliabilityareoftheutmost importance[10].
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 12 Issue: 03 | Mar 2025 www.irjet.net p-ISSN: 2395-0072
10. ENHANCING SUPPLY CHAIN AND HEALTHCARE OPERATIONS: ADUALAPPROACHTOROBUSTNESS AND EFFICIENCY
10.1 Introduction to Adversarial Robustness and Real-time Object Detection
Theapplicationofmachinelearningincontemporarysupply chain and healthcare operations is resulting in a rapid transformationofthelandscape,improvementsindecisionmaking, and enhancements in operational efficiency. However, challenges such as the necessity for real-time processingindynamiccontextsandadversarialassaultson critical systems continue to pose risks. The investigations conducted by Li et al. investigate key breakthroughs that have been made to boost the robustness and efficiency of these systems, which are crucial for both supply chain monitoringandhealthcareapplications,byprovidingunique methodsthatimprovetheaccuracyandresilienceofimage recognition and object identification tasks [11]. The capabilitiesofmachinelearningincriticaldomainssuchas healthcare operations and supply chain management are substantiallyenhancedbyresearch.Thesemethodsenhance the accuracy, efficiency, and security of systems that are essential to these industries by addressing challenges in adversarialrobustnessandreal-timeobjectdetection.These advancementsarepavingthewayformoreresilient,reliable, and high-performing operational systems in both sectors, assuring improved decision-making and better service delivery, as supply chains become more automated and healthcaresystemsrelyincreasinglyonAI.
11. HUMAN-CENTRIC MACHINE LEARNING:
ADDRESSING BIAS AND FAIRNESS IN THE SUPPLY CHAIN AND HEALTHCARE AI SYSTEMS
11.1 IntroductiontoHuman-CentricAIApproaches
As artificial intelligence systems become more tightly incorporatedintohealthcareandsupplychainprocesses,it isbecomingincreasinglycrucialtoaddressethicalconcerns, includingjusticeandprejudice.Artificialintelligenceisbeing employedinboththepublicandprivatesectorstooptimize complex processes, automate operations, and make judgments.Incontrast,thesesystemshavethecapacityto unintentionallyperpetuatebiasesormakedecisionsthatare unjust, which could have a detrimental effect on underrepresentedgroups.Human-CentricMachineLearning (HCML)isamethodologythatisdesignedtoguaranteethat artificialintelligencesystemsarealignedwithhumanvalues, unbiased, and fair. This methodology is promoted in the paperwrittenbyZhangandhisassociates.Thisapproachis especially noteworthy in the context of healthcare and supplychainmanagement,asthehealthandwell-beingof individualsandcommunitiescanbedirectlyinfluencedby thedecisionsmadebyartificialintelligence[12].
2:HarmonizingAIwithHumanValues
Thecurrenttrendtowardhuman-centeredmachinelearning offers a substantial paradigm for addressing bias and impartiality in artificial intelligence systems, with a particular emphasis on supply chain and healthcare operations.Byprioritizingtheintegrationofhumanvalues into the development of artificial intelligence, these industrieshavethecapacitytoestablishmoreresponsible, egalitarian,andefficientsystemsthatareadvantageousto society as a whole. Zhang and his associates have made a significantcontributiontothecurrentdiscussionandhave provided critical insights into the future of artificial intelligenceinthesecriticalfields.
Figure1:EnhancingImageRecognitionResilience
Figure
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Volume: 12 Issue: 03 | Mar 2025 www.irjet.net p-ISSN: 2395-0072
12. NEURO-INSPIRED LANGUAGE MODELS:
BRIDGING NLP AND COGNITIVE SCIENCE FOR ENHANCED OPERATIONS IN THE SUPPLY CHAIN AND HEALTHCARE
12.1 Introduction to Neuro-Inspired Language Models (NILM)
The objective of this work is to introduce Neuro-Inspired LanguageModels(NILM),agroundbreakingapproachthat endeavorstoreconcilethedividebetweenCognitiveScience and Natural Language Processing (NLP). By merging neurobiologicalinsightswithcontemporarymethodsfrom thefieldofnaturallanguageprocessing(NLP),NILMaimsto replicate the cognitive processes that underlie human languagecomprehension.Thiswillleadtolanguagemodels that are more intuitive and effective. This research has implications for natural language processing (NLP) and other disciplines, including healthcare and supply chain management, where effective data processing, communication,anddecision-makingareessential[13].The potentialforrevolutionaryadvancementsintheoperations of both the supply chain and the healthcare industry is presentwiththedevelopmentofNeuro-InspiredLanguage Models (NILM). In addition to enhancing language processing,NILMalsofacilitatesthedevelopmentofmore intelligent and adaptive systems that are capable of addressingintricatechallengesinbothcognitivescienceand naturallanguageprocessing.Thisisachievedbyestablishing a connection between the two domains. NILM will be instrumentalinenhancingtheprecision,dependability,and efficacy of operations in a variety of industries, with a particular emphasis on data-intensive contexts like healthcareandsupplychains,asthesetechnologiescontinue toevolve.
13. CONCLUSIONS
In general, the article concludes that while artificial intelligenceandquantumcomputinghavethepotential to revolutionize healthcare operations and supply chain operations, it is imperative to carefully consider ethical, technical, and human-centered issues to guarantee their successfulimplementation.Thestudy'sresultssuggestthat the evolution of multimodal models, which integrate a diversearrayofdatatypes,hasthepotentialtosignificantly enhance healthcare applications. It is anticipated that the implementationofthesemodelswillleadtoimprovements intheclassificationofdiseasesandthesemanticsearchof medical images, thereby facilitating the development of a more comprehensive strategy for medical research and patientmanagement.
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International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
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