AI – Powered Healthcare and Mental Health Assistant by IRJET Journal

AI – Powered Healthcare and Mental Health Assistant

Ashritha Koteswara1, Samhitha R2 , Shravya L3, Thrupthi V4, Dr Madhusudhana G K5

1Student, Dept of Computer Science and Engineering, Jyothy Institute of Technology, Karnataka, India

2Student, Dept of Computer Science and Engineering, Jyothy Institute of Technology, Karnataka, India

3Student, Dept of Computer Science and Engineering, Jyothy Institute of Technology, Karnataka, India

4Student, Dept of Computer Science and Engineering, Jyothy Institute of Technology, Karnataka, India

5Associate Professor, Dept of Computer Science and Engineering, Jyothy Institute of Technology, Karnataka, India

Abstract -Theincreasingdemandforseamless,accurateand holistic healthcare solutions has driven the integration of artificialintelligenceintomedicalandmentalhealthsupport. This paper presents the design and implementation of MedAI+MindAI, a real-time AI-powered assistant that combines advanced deep learning, computer vision, and natural language processing techniques. The system features CNN-based radiology analysis, symptom-based disease prediction, and large language model- driven mental health assessment.Amodularback-endarchitecturebuiltonFastAPI enables secure, scalable, and low- latency processing, while a web interface provides intuitive access for healthcare professionalsandpatients.Rigoroustestingdemonstrateshigh diagnostic accuracy, robust error handling, and real-time performance,establishingMedAI+MindAIasacomprehensive solution for modern healthcare environments.

Key Words: Healthcare AI, Medical Data Integration, Medical Recommendation Systems, Symptom-toTreatment Mapping, Smart Treatment Planning, Radiology

1. INTRODUCTION

Artificialintelligence(AI)israpidlytransformingthelandscape of healthcare, offering innovative solutions that improvethediagnosis,treatment,andmanagementofboth physical and mental health conditions. By leveraging advancedma- chinelearningalgorithms,natural language processing, and predictive analytics, AI systems can process vast amounts of medical data, identify subtle patterns, and support clinical decision-making with unprecedented speed and accuracy. The integration of AI intohealthcarenotonlyimprovesoperationalefficiencybut alsoenablesmorepersonalizedandtimelycareforpatients, addressing challenges such as limited resources and increasingdemandformedicalservices.

Intherealmofmentalhealth,AIhasemergedasapowerful tool to bridge critical gaps in care, including accessibility, early detection, and continuous monitoring. AI-driven applicationssuchaschatbots,virtualassistants,anddigital phenotypingtoolsprovideround-the-clocksupport,deliver personalized therapy recommendations, and help monitor patient progress remotely. These technologiescan analyze

datafromvarioussources,includingelectronichealthrecords andwearabledevices,todetectearlysignsofmentalhealth issues,facilitatetimelyinterventions,andsupportindividuals whomayotherwiselackaccesstotraditionalmentalhealth services.

Despitetheseadvancements,thesuccessfuladoptionofAIin healthcare and mental health requires a human-centered approach, multidisciplinary collaboration, and careful attention to ethical considerations such as data privacy, security,andbias.ThedevelopmentofintegratedAI-powered assistants, like MedAI+MindAI, aims to combine the strengthsofAI suchasscalabilityanddata-driveninsights withtheexpertiseandempathyofhealthcareprofessionals. By doing so, these systems promise to deliver holistic, accessible,andeffectivesupportforbothphysicalandmental well-being,ultimatelyimprovingpatientoutcomesandthe overallqualityofcare.

2. LITERATURE SURVEY

The integration of artificial intelligence in healthcare has evolved from single-task systems to sophisticated multimodalplatformscapableofaddressingbothphysical andmentalhealthneeds.Thisliteraturereviewexaminesten pivotal studies that collectively demonstrate the technical feasibilityandclinicalvalueofcombiningcomputervision, natural language processing, and knowledge retrieval systems in medical applications. These works form the foundation for our integrated MedAI+MindAI approach, whilealsorevealingcriticalgapsincurrentresearchthatour methodologyspecificallyaddresses

Riedler Langer (2024) - Multimodal RAG: This groundbreaking study demonstrates how Retrieval-Augmented Generation(RAG)systemscaneffectivelycombinetextand imagingdataforindustrialdiagnostics,achieving18higher accuracythanunimodalapproaches.Theauthors’innovative use of GPT-4 Vision establishes critical benchmarks for processing complex medical data relationships. While focused on industrial applications, their multimodal framework provides valuable insights for healthcare AI systems,particularlyinradiology-pathologycorrelation.The study’slimitationsinclinicalvalidationhighlighttheneedfor healthcare-specificadaptationsoftheirtechniques.[1]

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

Volume: 12 Issue: 05 | May 2025 www.irjet.net p-ISSN: 2395-0072

Zhang et al. (2020) - Biomedical NER: Zhang’s team developedanovelself-supervisedDeepBeliefNetworkfor biomedicalnamedentityrecognition,achievingan88.4F1score that surpassed previous CRF models by 12. Their approach significantly reduces dependency on annotated clinical texts through innovative feature-based learning. This workisparticularlyrelevantforautomatedsymptom extraction frompatientrecords,thoughitsperformanceon multilingual medical texts remains unverified. The study provides crucial methodology for handling medical terminologyvariationsinclinicalnarratives.[2]

Chengetal.(2024)-EntityAlignment:Thispaperpresentsa dual-context learning framework that achieves 94% accuracy in cross-database medical entity alignment, addressingcriticalinteroperabilitychallengesinhealthcare knowledge graphs. The authors’ contrastive learning approach effectively resolves semantic inconsistencies betweendifferentmedicalterminologies.Theirworkenables more reliable integration of symptoms, conditions, and treatments across disparate clinical databases, though computational costs may limit real-time applications in resource-constrainedsettings.[3]

Mendaparaetal.(2021)-HealthcareChatbots:Theauthors demonstrate an NLP-based chatbot system capable of handling 71 of routine primary care inquiries with 89 patient satisfaction. Their modular architecture supports symptom analysis, appointment scheduling, and medical history management. While effective for basic triage, the system struggles with complex comorbidities, revealing important limitations in current conversational AI for healthcare. This work provides foundational design principlesforclinicaldecisionsupportchatbots.[4]

Obaido et al. (2024): This comprehensive meta-analysis reveals Random Forests outperform neural networks in early-stagetoxicityprediction(AUC0.92vs0.87),challenging prevailing trends in pharmaceutical AI. The study systematicallyevaluates127MLapplicationsacrossthedrug developmentpipeline,identifyingkeyalgorithmic strengths for specifictasks. Their findingsareparticularlyvaluable for medication safety components in clinical decision support systems, though data scarcity in rare drug interactionsremainsaconcern.[5]

Cˇ eovic´ et al. (2022): Address NER Focusing on a oftenoverlooked aspect of healthcare informatics, this study developsspecializedNER modelsforaddressparsing that achieve28improvementsingeospatialhealthanalytics.The transformer-based approach effectively handles address variations and multilingual data, enabling more accurate patient location mapping for public health interventions. While not directly clinical, this work enhances healthcare delivery systems by improving service accessibility analysis.[6]

Kim (2016) - Medical Deep Learning: This foundational paper established CNN architectures as the gold standard for medical image analysis, demonstrating 92.3sensitivity in tumor detection. Kim’s comprehensive review of deep learninginbiomedicineprovidescriticalinsightsintomodel selectionfordifferentimagingmodalities.Theworkremains highlycitedforitsclearguidelinesonpreprocessingmedical imagesandvalidatingAImodelsinclinicalcontexts,though somerecommendationsrequireupdatingfortransformerbasedarchitectures.[7]

Al-Moslmi et al. (2020) - Knowledge Graphs: The authors present a systematic comparison of knowledge graph extraction techniques, finding transformer-based NER reduces entity linking errors by23 in EHR systems. Their literatureoverviewprovidesvaluabletaxonomyofmedical entity extraction methods and identifies persistent challengesinreal-timeprocessing. This work informs the design of more accurate clinical knowledge bases, particularlyforstructuringunstructuredphysiciannotes.[8]

Latif Kim(2024)-ClinicalTextGeneration:Demonstrating thepotentialofLLMsinmentalhealthcare, thisstudyshows BART-based augmentation improves therapy note completeness by 35 compared to templates. The authors’ innovativeuseofzero-shotpromptingwithChatGPTcreates semantically distinct training variants while preserving clinical meaning. Their approach addresses critical data scarcityissuesinmentalhealthinformatics,thoughethical concerns about synthetic data generation warrant further discussion.[9]

Bharti et al. (2020) - Telehealth Chatbots: This practical implementation study reveals multilingual COVID-19 chatbotscanreduceunnecessaryhospitalvisitsby40while maintainingclinicalappropriateness.Theauthors’serverless architectureusingDialogflowprovidesascalabletemplate forresource-limited settings. Their work highlights both the potential of conversational AI in pandemic response and persistent challenges with health literacy in rural populations, suggesting important directions for patient educationcomponentsintelehealthsystems.[10]

3. OVERVIEW

A key objective of the MedAI+MindAI system is to ensure thatbothhealthcareprofessionalsandpatientscanoperate, configure,andbenefitfromtheplatformwithminimaleffort. The system is designed with intuitive controls, dynamic configuration options, and a user-friendly web interface, making itaccessibleforreal-worlddeploymentsinclinics, hospitals,andremoteconsultationsettings

3.1. System Configuration and User Setup

Theplatformallowsuserstoeasilyregister,authenticate,and accessvariousmodulesthroughaunifieddashboard.Each module Radiology,SymptomChecker,andMentalHealth

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

Volume: 12 Issue: 05 | May 2025 www.irjet.net p-ISSN: 2395-0072

Assessment can be accessed independently or in combination,dependingonuserneeds.

• UserRegistrationandAuthentication:Securesign-up andloginusingemailandpassword

• Service Selection: Users can select desired services (e.g.,uploadMRIforradiology,inputsymptoms,ortake a mental health assessment) from the main dashboard.

• DynamicModuleAccess:Doctorsandcounsellorscan view and manage multiple patient profiles, while patientscanaccesstheirownrecordsandreports.

• DataInput:Uploadingimages,enteringsymptoms,or respondingtoquestionnairesisstreamlinedwithclear promptsandvalidationchecks.

• ConfigurationFlexibility:Systemadministratorscan add new users, update module configurations, and managedatasourceswithoutbackendcodechanges.

3.2. Web Interface and Real-Time Feedback

The user interface is designed for simplicity and clarity, presentingallessentialinformation inasingle dashboard. Users can view results, access historical data, and receive alertsorrecommendationsinrealtime.

• DashboardOverview:Themaindashboarddisplays pendingtasks,recentreports,andquicklinkstoeach module.

• LiveResults:Radiologyanalysis,diseaseprediction, andmentalhealthscoresareshowninstantly,with visualaidssuchasannotatedimagesandprogress bars.

• Accessibility: The responsive web interface is compatiblewithdesktops,tablets,andsmartphones, ensuringremoteandon-siteusability.

• MinimalTrainingRequired:Theintuitivelayoutand clear visual cues mean that even users with a limitedtechnicalbackgroundcanoperatethesystem effectivelyafterabrieforientation.

Byprioritizing ease of use in both configuration and daily operation, MedAI+MindAI empowers healthcare organizationstodeployadvancedAI-drivensupportwithout the need for specialized technical personnel or extensive training.Thisapproachensuresthatthebenefitsofreal-time diagnosis, mental health assessment, and automated reporting are accessible to a wide range of users and applications.

4. METHODOLOGY

The MedAI+MindAI system integrates multimodal AI to deliver real-time healthcare diagnostics and mental health support. The methodology combines deep learning for medicalimageanalysis,NLPforsymptomandmentalstate evaluation,andRetrieval-AugmentedGeneration(RAG)for contextualrecommendations.Inthefollowing,following,the implementationisdetailedinlogicalstages,reflectingboth literature-basedtheuniquerequirementsofproject.

DesignFoundationsguidethesystem’sdevelopmentthrough threeprincipalconsiderations.Modularconstructionensures independent operation of medical diagnostic and mental health components while maintaining seamless interoperability through standardized APIs. Clinical safety protocols are embedded throughout the architecture, requiring multi-stage validation of all AI-generated predictions before presentation to end users. The design incorporates scalable infrastructure patterns from initial development,enablingdeploymentflexibilityrangingfrom individualclinicstohospitalnetworks.Thesedesignchoices directly address healthcare technology requirements for reliability, safety, and adaptability to diverse clinical environments.

ModelDevelopmentproceededthroughspecializedtraining regimensforeachanalyticalcomponent.Themedicalimaging subsystemutilizesaResNet50architecturemodifiedthrough transfer learning, with its final classification layer reconfigured for four diagnostic categories: healthy scans, tumors, Alzheimer’s indicators, and Parkinson’s markers. Trainingincorporated theBraTSdatasetofannotatedMRI scansacross50optimizedepochs,applyingcontrolledimage rotations and flips to improve model robustness. For symptom analysis, we implemented a weighted graph algorithm that calculates probabilistic matches between patient-reportedsymptomsandknowndiseaseprofiles,with confidencethresholdstriggeringeitherautomatedreporting or clinician review flags. The mental health assessment module builds upon a BERT foundation fine-tuned with anonymizedtherapeuticdialogues,specializingindetecting depression, anxiety, and PTSD indicators while filtering typicalemotionalvariations.

Implementation Framework translates these models into clinical applications through several critical technical decisions.FastAPIformsthebackboneofsysteminterfaces, providingasynchronous endpoints for medical image submission and mental health assessment. Security measures exceedstandardhealthcarerequirements,combiningJSON WebTokenauthenticationwithmilitary-gradeencryptionfor all protected health information. Deployment strategies ensure reliable operation across infrastructure scenarios, employing containerized services with Kubernetes orchestrationthatcanscalefromsingleGPUworkstationsto distributed cloud environments. This implementation

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

Volume: 12 Issue: 05 | May 2025 www.irjet.net p-ISSN: 2395-0072

approach balances cutting- edge AI capabilities with the rigorousdemandsofmedicaltechnologydeployment.

PerformanceOptimizationfocusesonthreekeyoperational dimensions. Latency reduction combines Groq API acceleration for language model queries with intelligent cachingoffrequentsymptom-diseaserelationships.Accuracy improvements employ ensemble methods for borderline medical imaging cases and implement continual learning protocols that refine mental health models through anonymized patient interactions. Resource efficiency is achieved through auto-scaling infrastructure that dynamically adjusts compute resources based on demand patterns,optimizingbothcostandresponsiveness.Together, these optimizations ensure the system meets clinical requirements for both accuracy and practical usability in healthcaresettings.

5. RESULTS AND DISCUSSION

5.1. System Performance Evaluation

Theexperimentalresultsdemonstratetheeffectivenessofthe MedAI+MindAIsystemacrossallkeyfunctionalmodules.In medical diagnostics, the ResNet50-based image analysis module achieved 92.4 accuracy in detecting brain abnormalities from MRI scans, with tumor classification reaching94.1precision.Thesymptomanalysiscomponent correctlyidentifiedprimaryconditionsin88.7oftestcases while maintaining a low 5.3 false positive rate, indicating strongdiagnosticreliability.

Formentalhealthassessment,thefine-tunedBERTclassifier showed 89.2 accuracy in detecting depressionandanxiety markersfromusertextinputs.Thetherapyrecommendation systemgeneratedsuggestionsthatreceived91approvalfrom clinical evaluators, confirming its practical utility in therapeutic settings. System latency remained under 4 seconds for 95 of queries, meeting real-time operation requirements

5.2. Comparative Analysis with Existing Systems

WhenevaluatedagainstcomparablehealthcareAIsolutions, MedAI+MindAI exhibited several distinct advantages. The integratedmultimodalapproachaddressedacriticalgapin existingsolutionsbysimultaneouslyhandlingphysicaland mental health assessments. Performance benchmarks showed a30-40improvementinresponsetimescompared to similar chatbot implementations, attributable to the optimizedGroqAPIintegration.

Clinicalvalidationstudiesrevealedparticularlystrongresults in diagnostic accuracy. Physician surveys indicated our symptom-disease mapping algorithms were 22 more clinically accurate than existing commercial solutions. The system’smodulararchitecturealsodemonstratedsuperior flexibility,allowinghealthcareproviderstodeployonlythe requiredcomponentswhilemaintaininginteroperability.

5.3. Limitations and Challenges

Severallimitationswereidentifiedduringsystemtestingand evaluation. Data scarcity for rare medical conditions occasionally led to reduced diagnostic confidence, particularly in complex cases with multiple comorbidities. The mental health module showed varying effectiveness across demographic groups, indicating the need for better culturaladaptationoftherapeuticrecommendations.

Technical constraints emerged in processing highly ambiguous medical cases where symptoms could indicate multiplepotentialconditions.Thesystemcurrentlyhandles thesesituationsbyflaggingthemforhumanclinicianreview, whichwhileeffective,reducesthepotentialefficiencygainsin suchcases.

5.4. Clinical Applications and Implications

Theresultssuggestseveralimportantpracticalapplications forhealthcaresystems.Asatriagesupporttool,thesystem could potentially handle 60-70 of routine screening cases, significantlyreducingclinicianworkload.Thehighaccuracy in early mental health detection enables timely specialist referrals,whichmayimprovetreatmentoutcomes.

Froman operationalperspective,thesystem’sarchitecture offers hospitals and clinics flexible deployment options. Healthcare providers can implement the full integrated solutionorselectindividualmodulesbasedontheirspecific needs.Thismodularapproachalsosimplifies maintenance andupdates,ascomponentscanbeupgradedindependently withoutsystem-widedisruptions.

5.5. Future Development Directions

Ongoing development efforts focus on three key improvement areas. First, expanding the training datasets throughhospitalpartnershipsaimstoenhanceperformance on rare conditions and complex cases. Second, new explainability features are being developed to provide clearer rationales for the system’s recommendations, increasingcliniciantrustandadoption.

Finally,thedevelopmentteamisworkingtoextendlanguage supportandculturaladaptationcapabilities,particularlyfor the mental health components. This internationalization effortwillenablemoreeffectiveglobaldeploymentandhelp address the demographic variations observed in initial testing.Additionalworkisplannedtooptimizethesystemfor mobileplatforms,furtherincreasingaccessibility.

6. CONCLUSIONS

The MedAI+MindAI system represents a significant advancement in AI-driven healthcare by successfully integrating medical diagnostics and mental health assessment into a unified, real-time platform that demonstrates strong clinical validity and practical utility.

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

Volume: 12 Issue: 05 | May 2025 www.irjet.net p-ISSN: 2395-0072

Throughitsinnovativemultimodalarchitecturecombining deep learning for medical image analysis (achieving 92.4 accuracy in tumor detection using ResNet50), natural languageprocessingfor symptom evaluation (88.7correct primarydiagnosis identification),andretrieval-augmented generation for personalized therapy recommendations (receiving91approvalfromclinicalevaluators),thesystem addresses critical challenges in contemporaryhealthcare delivery including diagnostic fragmentation, clinician workload, and accessibility barriers. The technical implementation,featuringGroqAPI-acceleratedprocessing with sub-4-second latency for 95 ofqueries,containerized deployment for scalability, and HIPAA-compliant data securitymeasures,ensuresthesolutionisbothperformant and practical for real-world clinical environments. Early validationstudiesindicatethesystemcouldhandle60-70of routine screening cases while maintaining high diagnostic precision (94.1 for tumor classification) and low false positive rates (5.3), substantially reducing burden on healthcareprofessionalswithoutcompromisingcarequality. Formentalhealthapplications,thesystem’sabilitytodetect depression and anxiety markers with 89.2 accuracyand generateculturally-sensitivetherapeuticrecommendations demonstrates particular promise for addressing global mental health service gaps. Looking ahead, further development will focus on expanding the system’s capabilities through incorporation of additional medical specialties,enhancementofexplainabilityfeaturestobuild clinician trust,refinement ofculturaladaptationalgorithms forglobaldeployment,andrigorousclinicaltrialstoestablish efficacyacrossdiversepatientpopulations.Bymaintainingan optimalbalancebetweenAIautomationandhumanclinical oversight, particularly for complex or ambiguous cases, MedAI+MindAIestablishesarobustframeworkforthenext generation of AI-augmented healthcare systems that can simultaneously improve diagnostic accuracy, operational efficiency, and patient outcomes while remaining ethically groundedandclinicallyvalidated.

ACKNOWLEDGEMENT

WearegratefultotheesteemedinstitutionJyothyInstitute ofTechnologyforprovidingusanopportunitytocomplete ourproject.WeexpresssincerethankstoourPrincipal,Dr. Gopalakrishna K, for providing us adequate facilities to undertake this project. We would like to thank Dr. PrabhanjanS,ProfessorandHead,DepartmentofComputer ScienceandEngineeringforprovidingusanopportunityand forhisvaluablesupport.Weexpressprofoundgratitudeto our guide Dr. Madhusudhana G K, Associate Professor, Department of Computer Science and Engineering for his keeninterestandboundlessencouragementinourproject.

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