Description:FIELD OF THE INVENTION

[0001] The present invention relates to a healthcare assessment system that is capable of performing automated, multimodal clinical evaluation through integrated physiological, behavioural, and analytical data fusion, offering real time pre diagnosis, personalized risk prediction, and adaptive learning capabilities that ensures continuous improvement while maintaining stringent patient data privacy and diagnostic accuracy.

BACKGROUND OF THE INVENTION

[0002] Evaluating an individual’s physical and mental condition is a fundamental aspect of modern medical practice, enabling timely identification of health concerns and informed clinical decisions. Comprehensive evaluation processes help determine risk factors, monitor ongoing conditions, and guide appropriate treatment plans. The importance lies in promoting preventive care, improving patient outcomes, and supporting efficient resource allocation within medical facilities. In real-life scenarios, structured health evaluations assist during routine checkups, hospital admissions, workplace screenings, and insurance reviews, ensuring accurate documentation, early intervention, and enhanced overall well-being for individuals and communities.

[0003] The traditional evaluation practices rely largely on manual record-keeping, in-person consultations, and periodic checkups conducted at fixed intervals. However, the methods rely extensively on information provided by patients and assessments made by healthcare professionals, both of which differs in precision and thoroughness. Assessments are time-consuming and limited to scheduled visits, restricting continuous insight into an individual’s condition. Additionally, fragmented documentation hinders coordinated care and delays timely intervention. The methods reduce efficiency, limit proactive monitoring, and make it challenging to obtain comprehensive, real-time understanding of a person’s overall health status across different care settings.

[0004] US20160232299A1 discloses a systems and methods for patient health assessment are disclosed. A method includes: receiving medical data related to a patient stored in a database; identifying assessments to be performed by a case manager for the patient, each including questions to be answered; displaying, in a user interface of a case manager terminal, visual indicators that indicate a level of completeness of the assessments; displaying a listing of questions to be asked to the patient for a current assessment; receiving, in a data input field included in the user interface, input data from the case manager; and, in response to the input data, displaying in the user interface an updated listing of questions to be asked to the patient, where the updated listing of questions includes one or more questions that, when answered by the patient, advance the progress of completion of at least one of the one or more assessments.

[0005] US20040158486A1 discloses a healthcare solution system includes electronic appointment scheduling, medical inquiry session customization, patient response message customization, and broadcast messaging. The appointment scheduling allows patients to use a single front end user interface to communicate with plural medical providers who have disparate backend appointment systems, or no appointment system. The medical inquiry session customization allows medical providers to individually tailor queries and response options related to electronically solicited medical inquiries, such as an electronically requested patient history. The patient response message customization allows the provider to individually tailor responses to patient e-mail inquiries generated within the healthcare solution system. Broadcast messaging allows medical providers to target medical-related information to specific patients who may benefit from the message without compromising the confidentiality of the patients.

[0006] Conventionally, many systems are disclosed in the prior art provides a means for conducting health evaluations that rely on manual record-keeping, in-person consultations, and periodic checkups. However, the systems are time-intensive, inconsistent, and dependent on subjective judgment, resulting in incomplete data and delayed clinical decisions. Moreover, the system limits continuity of care, reduces operational efficiency, and hinders timely and comprehensive health monitoring.

[0007] In order to overcome the aforementioned drawbacks, there exists a need in the art to develop a system that requires to be capable of performing automated, multi-source clinical evaluations, analyzing physiological, behavioural, and analytical data in real time. Additionally, the developed system also needs to provide personalized risk assessments, support early diagnosis, enable adaptive learning for continuous improvement, and ensure strict data privacy and accurate, reliable healthcare insights for both routine and critical medical decision-making.

OBJECTS OF THE INVENTION

[0008] An object of the present invention is to develop a system that is capable of performing automated and consistent preliminary evaluations, reducing manual dependency and improving efficiency in routine patient health checkups.

[0009] Another object of the present invention is to develop a system that is capable of delivering accurate, personalized risk analysis by integrating diverse patient data sources and generating reliable, data based diagnostic insights for early detection and preventive care.

[0010] Another object of the present invention is to develop a system that is capable of enhancing diagnostic accuracy through continuous learning using decentralized model training methods that maintain strict patient privacy protection and secure medical data handling.

[0011] Yet another object of the present invention is to develop a system that is capable of assisting clinicians with clear, evidence based decision support information, improving evaluation speed, consistency, and overall quality of patient assessment across multiple healthcare environments.

[0012] The foregoing and other objects, features, and advantages of the present invention will become readily apparent upon further review of the following detailed description of the preferred embodiment as illustrated in the accompanying drawings.

SUMMARY OF THE INVENTION

[0013] The present invention relates to a healthcare assessment system that is capable of performing automated and consistent preliminary evaluations, generating personalized risk analysis, thereby enabling continuous model improvement using privacy preserving decentralized learning, and providing clinicians with reliable, evidence based decision support to enhance diagnostic accuracy and assessment efficiency.

[0014] According to an aspect of the present invention, a healthcare assessment system, comprises a vertically oriented diagnostic kiosk within a clinic to receive a patient for automated pre checkup, an interface within the kiosk to present a structured questionnaire regarding at least current symptoms, duration, progression and prior treatment, and receive patient responses and uploads of prior reports including laboratory results, scan images and prescription data, an input display with the kiosk interface to display guidance and preliminary assessment results, an acoustic transducer with the kiosk to capture the patient’s spoken responses, at least one artificial intelligence enabled camera with the kiosk to capture facial expressions and behavioral cues and also to perform facial and ocular analysis to detect visual indicators including at least pale conjunctiva, yellow sclera, perioral or peripheral cyanosis, signs of dehydration, rashes and wound healing abnormalities, a compact CT scanning unit on a vertically aligned dual axis lead screw arrangement with the kiosk to move the scanning unit along vertical and radial axes to perform focused scanning of selected body regions, and a plurality of gentle suction elements with the kiosk to hold the plate stably against a patient’s chest.

[0015] According to another aspect of the present invention, the system further comprises an acoustic sensor with the kiosk to capture heart and lung sounds and short cough and speech samples, a communication interface with the microcontroller with the kiosk to exchange anonymised or pseudonymised model updates and summary statistics with a remote aggregation service, a learning engine executed by the remote aggregation service and coupled to a plurality of such local computing modules deployed at different clinics, the learning engine to receive locally trained model updates together with associated data quality and anomaly detection metrics from each clinic, and assign and update trust weights for each clinic based on at least update consistency, data quality and anomaly scores, at least one ECG sensor with the kiosk to detect cardiac electrical activity, and at least one PPG sensor with the kiosk to measure optical pulse signals.

[0016] While the invention has been described and shown with particular reference to the preferred embodiment, it will be apparent that variations might be possible that would fall within the scope of the present invention.

BRIEF DESCRIPTION OF THE DRAWINGS

[0017] These and other features, aspects, and advantages of the present invention will become better understood with regard to the following description, appended claims, and accompanying drawings where:
Figure 1 illustrates an isometric view of a healthcare assessment system.

DETAILED DESCRIPTION OF THE INVENTION

[0018] The following description includes the preferred best mode of one embodiment of the present invention. It will be clear from this description of the invention that the invention is not limited to these illustrated embodiments but that the invention also includes a variety of modifications and embodiments thereto. Therefore, the present description should be seen as illustrative and not limiting. While the invention is susceptible to various modifications and alternative constructions, it should be understood, that there is no intention to limit the invention to the specific form disclosed, but, on the contrary, the invention is to cover all modifications, alternative constructions, and equivalents falling within the spirit and scope of the invention as defined in the claims.

[0019] In any embodiment described herein, the open-ended terms "comprising," "comprises,” and the like (which are synonymous with "including," "having” and "characterized by") may be replaced by the respective partially closed phrases "consisting essentially of," consists essentially of," and the like or the respective closed phrases "consisting of," "consists of, the like.

[0020] As used herein, the singular forms “a,” “an,” and “the” designate both the singular and the plural, unless expressly stated to designate the singular only.

[0021] The present invention relates to a healthcare assessment system that integrates multimodal physiological, behavioural, and imaging data with adaptive privacy preserving intelligence to enable automated, personalized, and continuously self improving pre diagnosis and risk analysis, advancing clinical accuracy, efficiency, rare condition detection, and cross institution learning without compromising patient confidentiality.

[0022] Referring to Figure 1, an isometric view of a healthcare assessment system is illustrated, comprising a vertically oriented diagnostic kiosk 101 installed within a clinic, an interface disposed within the kiosk 101, the kiosk 101 interface comprising an input display 102, an acoustic transducer 103 arranged with the kiosk 101, at least one artificial intelligence enabled camera 104 positioned with the kiosk 101 and oriented toward the patient, a compact CT scanning unit 105 mounted on a vertically aligned dual axis lead screw arrangement 106 with the kiosk 101, an articulated probe 107 positioned with the kiosk 101 and terminating in a circular contact plate 108, the contact plate 108 comprising a plurality of gentle suction elements 109.

[0023] The system described herein comprises a vertically oriented diagnostic kiosk 101 installed within a clinical environment and configured to accommodate a patient for an automated preliminary health checkup.

[0024] The diagnostic kiosk 101 includes an interface disposed within within the kiosk 101 and operatively coupled to an embedded microcontroller within the kiosk 101. The interface incorporates an input display 102 that is configured to present a structured questionnaire covering at least the patient’s current symptoms, their duration and progression, and any prior treatments undertaken. The display 102 further enables the patient to enter responses and upload previous medical records, such as laboratory test results, imaging scans, and prescription data. In addition, the display 102 is arranged to provide on screen guidance and to present preliminary assessment results.

[0025] The input display 102 operates as an interactive touch based interface that enables two way communication between the patient and the system’s embedded microcontroller. The display 102 comprises of a high resolution capacitive touch panel integrated with a graphical user interface (GUI) module, allowing the patient to navigate structured questionnaires, select answers, and input textual information through an on screen keyboard or voice to text functionality. The display 102 is further supported by a local processing unit and memory buffer for real time rendering of questions, guidance prompts, and visual indicators. Data entered or uploaded by the patient, such as responses, laboratory reports, scan images, and prescription records are temporarily stored within the memory and then transmitted to the microcontroller through a secure internal communication bus. The microcontroller processes this information to generate intermediate analysis or guidance messages, which are rendered back on the input display 102 for patient feedback. The display 102 is also configured to adapt its interface dynamically based on user responses, ensuring intuitive interaction, accessibility, and a seamless flow during the automated pre checkup procedure.

[0026] An acoustic transducer 103 is integrated within the diagnostic kiosk 101 and configured to capture the patient’s spoken responses during interaction with the system. The transducer 103 is operatively coupled to the embedded microcontroller, enabling real time acquisition and processing of voice signals. The acoustic transducer 103 comprises a high sensitivity microphone capsule enclosed within an acoustically insulated housing, designed to minimize ambient noise interference inside the kiosk 101. The transducer’s diaphragm vibrates in response to airborne sound waves produced by the patient’s voice; these mechanical vibrations are translated into analog voltage signals through an internal electret or MEMS based conversion element. The analog output is then routed through a preamplifier and analog to digital converter (ADC) circuit integrated with the embedded microcontroller. The digitized signal is processed in real time by microcontroller, which extract acoustic features such as pitch, tone, intensity, stress level, and hesitation intervals. These extracted features are subsequently utilized to analyse vocal characteristics including at least tone, stress, hesitation, and behavioural assessment, response consistency evaluation, preliminary diagnostic inference and extract vocal features including pitch, energy, pauses and prosodic variability.

[0027] At least one artificial intelligence–enabled camera 104 is positioned within the diagnostic kiosk 101 and oriented toward the patient. The camera 104 is configured to capture image sequences representing facial expressions, ocular movements, and behavioural cues during the interaction process. The artificial intelligence–enabled camera 104 consists of a high resolution image sensor, an adaptive lens assembly, and an illumination unit configured to maintain consistent lighting conditions within the kiosk 101. The captured image frames are transmitted to an onboard processing unit or directly to the embedded microcontroller. Within the processing pipeline, pre processing operations such as noise reduction, exposure correction, and face detection are first performed. Thereafter, embedded AI (Artificial Intelligence) protocols implemented through convolutional neural networks (CNNs) or similar architectures analyze key facial landmarks, micro expressions, and ocular motion to infer emotional state, stress level, attentiveness, and signs of fatigue or discomfort. Computer vision routines further examine skin tone and texture to detect clinical indicators such as pallor, jaundice, cyanosis, dehydration, or rashes. The processed visual features are subsequently converted into numerical descriptors and relayed to the microcontroller for fusion with other data modalities, including voice features and questionnaire responses, to enhance assessment accuracy and generate comprehensive diagnostic insights. By performing multimodal data fusion, the microcontroller generates numerical confidence scores and inconsistency indicators corresponding to each reported symptom. These results are compiled into a structured visual or tabular inconsistency map, which is stored within the patient’s electronic record and presented to the attending clinician as contextual decision support information.

[0028] An articulated probe 107 is mounted to the diagnostic kiosk 101 and terminates in a circular contact plate 108 designed for attachment to the patient’s chest region during operation. The contact plate 108 incorporates a plurality of gentle suction elements 109 that stabilize the interface against the skin surface while maintaining patient comfort. Embedded within the contact plate 108 is an acoustic sensor configured to capture physiological sounds, including heartbeats, respiratory sounds, and short cough or speech samples. The corresponding signals are transmitted to the embedded microcontroller, which executes digital signal processing protocols to identify and characterize acoustic features such as wheezes, crackles, rhonchi, and cardiac murmurs, enabling classification of respiratory infection likelihood, asthma exacerbation risk, or chronic obstructive pulmonary disease (COPD) risk. The contact plate 108 further integrates at least one electrocardiography (ECG) sensor and one photoplethysmography (PPG) sensor positioned to acquire synchronized cardiac electrical and optical pulse signals. The microcontroller processes these signals in real time to derive heart rate, heart rate variability, and pulse transit metrics, as well as to detect conditions such as arrhythmia or bradycardia. Based on the combined ECG and PPG feature deviations, the system additionally computes an early sepsis risk score indicative of possible systemic physiological stress.

[0029] The articulated probe 107 comprises a multi jointed or motor assisted arm that allows controlled extension, retraction, and angular adjustment under guidance from the kiosk’s control module or operator input. At the distal end of the probe 107, a circular contact plate 108 is mounted, incorporating flexible suction elements 109 that create a gentle, temporary seal with the skin to ensure stable contact and minimize motion artifacts during measurement. The contact plate 108 houses multiple embedded sensors, including the acoustic transducer 103 for auscultatory signal capture, electrocardiography (ECG) electrodes for electrical activity detection, and photoplethysmography (PPG) elements for optical pulse measurement. Once activated, the acoustic sensor converts mechanical sound vibrations, such as cardiac and respiratory sounds into electrical signals, while the ECG and PPG sensors acquire synchronous bio electrical and optical waveforms. The acquired physiological signals are routed through shielded cables or wireless links to the embedded microcontroller, which performs concurrent analysis of acoustic, electrical, and optical data.

[0030] The plurality of suction elements 109 are formed of soft, biocompatible silicone rubber, each engineered to readily expel internal air and thereby generate a localized vacuum between the cup surface and the patient’s skin. This vacuum effect creates an air tight seal that firmly anchors the circular contact plate 108 in place, preventing slippage during sensing operations while maintaining patient comfort. The suction elements 109 are designed for smooth release and reattachment, enabling the contact plate 108 to be repositioned easily without degradation of suction performance or loss of adhesion integrity during successive measurement cycles,

[0031] The acoustic sensor operates as a miniature electronic stethoscope that converts mechanical sound vibrations produced by cardiac and pulmonary activity into corresponding electrical signals. The acoustic sensor consists of a sensitive diaphragm coupled to an electret or MEMS microphone within an acoustically insulated housing. Once placed against the chest, pressure waves from heartbeats, respiration, coughs, or spoken sounds cause diaphragm deflection, generating analog voltage signals. These signals are routed through a preamplifier and analog to digital converter for refinement and digitization. The embedded microcontroller subsequently processes the audio data to extract frequency spectra and temporal patterns used for clinical analysis.

[0032] The electrocardiography (ECG) sensor functions by detecting the bioelectric potentials generated by the depolarization and repolarization of myocardial cells. The electrocardiography (ECG) sensor consists of conductive electrodes embedded within the contact plate 108, an instrumentation amplifier, and a signal conditioning circuit. Once adhered to the patient’s skin, the electrodes capture micro volt electrical signals representing cardiac cycles. The amplifier enhances these weak signals while filters remove noise and baseline drift. The conditioned waveform is digitized for the microcontroller, which identifies QRS complexes, heart rate, rhythm irregularities, and morphology deviations for detecting arrhythmia, bradycardia, or other cardiac abnormalities.

[0033]  The photoplethysmography (PPG) sensor measures volumetric changes in blood flow beneath the skin using a light based technique. The photoplethysmography (PPG) sensor comprises a light emitting diode (LED) transmitting near infrared or red light into the tissue and a photodiode detector placed adjacent to the LED. As the cardiac cycle progresses, variations in blood volume alter the intensity of reflected or transmitted light. The photodiode converts these optical fluctuations into electrical current signals, which pass through amplification and filtering circuits before digitization. The microcontroller analyses the resulting waveform to compute pulse rate, pulse transit time, and additional hemodynamic metrics related to vascular health.

[0034] A compact computed tomography (CT) scanning unit 105 is integrated within the diagnostic kiosk 101 and mounted on a vertically aligned dual axis lead screw arrangement 106. This dual axis lead screw arrangement 106 enables precise movement of the scanning assembly along both vertical and radial axes, allowing targeted imaging of selected anatomical regions as required. Once the preliminary assessment indicates a need for imaging, the CT scanning unit 105 is automatically positioned to align with the designated body area and perform a focused scan. The acquired imaging data are processed to generate summarized scan outputs, which are stored locally and linked to the corresponding patient assessment record for subsequent review.

[0035] The vertically aligned dual axis lead screw arrangement 106 consists of two orthogonally oriented lead screws, one aligned vertically and the other radially, each driven by a stepper or servo motor. The vertical lead screw controls the up and down movement of the scanning unit 105, while the radial lead screw adjusts its in and out displacement toward or away from the patient. Both screws are supported by linear bearings to ensure smooth, vibration free travel and precise spatial alignment. During operation, the control module synchronizes both axes to achieve the optimal scanning trajectory for the targeted body region. After image acquisition, the motors reverse in a controlled manner to retract the CT scanning unit 105 to the standby position, ensuring mechanical stability and repeatable performance across repeated diagnostic sessions.

[0036] The compact computed tomography (CT) scanning unit 105 operates as a miniaturized imaging module designed to generate cross sectional views of selected body regions with reduced footprint and radiation exposure compared to conventional systems. The compact computed tomography (CT) scanning unit 105 comprises an X ray source, collimator, rotating gantry, and a ring of high sensitivity detectors positioned opposite the source. During operation, the X ray tube emits a fan shaped beam that passes through the target anatomical region as the gantry performs a controlled rotational sweep under motorized drive. The detectors measure the attenuated X ray intensity at multiple angular positions, producing a set of projection data. These raw signals are transmitted to the embedded microcontroller or an associated imaging processor, that generate tomographic slice images. The compact design employs radiation shielding, optimized cooling, and motion synchronization with the dual axis lead screw arrangement 106 to ensure precise alignment and patient safety. The reconstructed images and summary metrics are stored locally and linked to the corresponding diagnostic record for further analysis.

[0037] A local data storage unit is operatively coupled to the embedded microcontroller and configured to retain all patient specific information generated or collected during the assessment process. This includes questionnaire responses, uploaded laboratory reports, imaging data, prescription records, CT scan summaries, and preliminary diagnostic results, thereby enabling longitudinal tracking and subsequent review. Upon completion of all measurement and analysis steps, the microcontroller executes a data fusion routine that integrates multimodal inputs, comprising questionnaire responses, physiological parameters derived from the acoustic, ECG, and PPG sensors, imaging outputs from the CT scanning unit 105, and relevant historical records. Through this integration, the microcontroller constructs a digital twin representation of the patient, expressed as a dynamic data model encapsulating physiological attributes, symptom progression, imaging findings, and treatment history. The digital twin is instantiated by organizing and correlating parameter sets within structured data frames, which simulates patient status under varying physiological or therapeutic conditions. The complete digital twin dataset is then stored within the local data storage unit for longitudinal comparison, progression analysis, and use in subsequent encounters to improve personalized risk estimation and treatment outcome prediction.

[0038]  During subsequent patient visits or treatment cycles, the embedded microcontroller retrieves the previously stored digital twin data and compares the patient’s actual clinical outcomes with the earlier simulated predictions. This comparison involves analyzing deviations across key physiological parameters, symptom progressions, and treatment responses. The magnitude and pattern of these deviations are quantified as a twin divergence score, representing the variance between predicted and observed outcomes. The microcontroller employs this score to recalibrate the digital twin’s internal parameters and adaptive models, thereby refining the accuracy of future physiological simulations, enhancing personalized risk forecasting, and continuously improving the reliability of patient specific diagnostic assessments.

[0039] A communication interface functions as a secure data exchange module that enables controlled connectivity between the diagnostic kiosk 101 and a remote aggregation service. The communication interface comprises Ethernet, Wi Fi, or cellular transceivers integrated with a cryptographic controller for authenticating communication sessions and encrypting transmitted payloads. Operating under the supervision of the embedded microcontroller, the interface packages locally trained model updates, summary statistics, and performance metrics while excluding any raw or personally identifiable patient data. Prior to transmission, data undergo anonymization or pseudonymization processes managed by the microcontroller to ensure privacy preservation. The formatted packets are then transmitted through secured network protocols such as HTTPS or MQTT over TLS. Upon receipt of aggregated updates from the remote service, the communication interface facilitates synchronization by downloading calibrated global or specialty models back into the kiosk 101 for local inference. This bidirectional exchange allows adaptive model improvement across multiple deployments while maintaining strict compliance with medical data protection standards.

[0040] The remote aggregation service hosts a learning engine operatively connected to a plurality of local computing modules deployed across different clinics. The engine is designed to coordinate distributed model training without transferring any raw patient data. During operation, each clinic periodically transmits locally trained model updates accompanied by data quality indicators and anomaly detection metrics through the secure communication interface. The learning engine evaluates these metrics to assign and continuously adjust trust weights for each participating clinic, reflecting update reliability and data integrity. Using a trust weighted aggregation process, the engine combines model parameters from all clinics to refine a shared backbone model while maintaining separate specialty focused sub models. Updates from clinics with demonstrated domain expertise are preferentially used to enhance corresponding specialty models. Once aggregated, the updated global and sub models are redistributed to the local kiosks 101, ensuring progressive improvement of diagnostic accuracy and adaptability across the network, all while preserving complete patient privacy through decentralized learning.

[0041] The learning engine operates as a distributed machine learning coordination framework designed to train and optimize diagnostic models across multiple clinical sites without exposing raw patient data. The learning engine comprises a central aggregation server equipped with high performance processors, secure data ingestion modules, and a trust management subsystem. Each local kiosk 101 using its own anonymized data, generating model weight updates together with associated metadata such as data quality measures and anomaly detection scores. The engine receives these encrypted updates and evaluates their reliability through a trust scoring protocol, such as a Bayesian trust estimator or weighted consistency index, that quantifies consistency, variance, and anomaly frequency for each source. Using these trust weights, the engine executes a weighted aggregation procedure, typically Federated Averaging (FedAvg) or adaptive gradient fusion protocols, to update a global model. Simultaneously, the learning engine routes relevant parameter subsets to specialty sub models aligned with specific clinical domains. The updated global and domain specific models are then validated, versioned, and securely distributed back to each local site for inference and continued cyclical training, enabling network wide model improvement while maintaining data confidentiality and compliance with privacy standards.

[0042] Within the remote aggregation service, a specialized rare disease amplification engine operates in conjunction with the learning engine to improve recognition of infrequent but clinically important conditions. The rare disease amplification engine consists of a pattern detection unit, statistical frequency analyser, and parameter weighting controller integrated into the aggregation processing pipeline. As locally trained model updates are received from multiple clinics, the pattern detection unit scans incoming parameter gradients and feature embeddings for low occurrence yet highly discriminative patterns associated with rare or atypical diseases. The frequency analyser quantifies their statistical representation across the network, while the weighting controller dynamically increases the aggregation influence of parameters linked to these rare signatures. By amplifying under represented but diagnostically relevant signals during the averaging process, the engine enhances the system’s sensitivity to rare pathologies without compromising overall model stability. The refined parameters are then incorporated into the updated global and specialty models distributed back to clinic level kiosks 101, thereby improving their capacity to detect uncommon clinical presentations that might otherwise remain unrecognized in data sparse environments.

[0043] The kiosk 101 interface displays 102 the system generated preliminary pre diagnosis, personalized risk analysis, and visually structured inconsistency map to the attending clinician. These outputs function as decision support tools, assisting in clinical interpretation and streamlining the checkup process while ensuring consistency, efficiency, and evidence based evaluation without replacing professional medical judgment.

[0044] The present invention works best in the following manner, where the vertically oriented diagnostic kiosk 101 installed within the clinic accommodates the patient for the automated preliminary health checkup. The kiosk 101 includes the interface operatively connected to the embedded microcontroller, the interface incorporating the input display 102 that presents the structured questionnaire, receives patient responses and prior medical uploads, and displays preliminary guidance. The acoustic transducer 103 captures spoken responses, while the artificial intelligence–enabled camera 104 records facial expressions and behavioural cues, both transmitting data to the microcontroller for multimodal feature extraction and fusion. The articulated probe 107, terminating in the circular contact plate 108 with silicone based suction elements 109, provides stable contact with the patient’s chest for concurrent acquisition of acoustic, ECG, and PPG signals. The compact CT scanning unit 105 mounted on the vertically aligned dual axis lead screw arrangement 106 performs targeted imaging of selected body regions under microcontroller control. The microcontroller integrates questionnaire, physiological, and imaging data to construct the dynamic digital twin representation of the patient, stored in the local data storage unit for longitudinal tracking and recalibration through computation of the twin divergence score in subsequent visits. Through the communication interface, anonymised or pseudonymised model updates and summary statistics are securely transmitted to the remote aggregation service hosting the learning engine. The learning engine conducts trust weighted aggregation and specialty model updating, while the rare disease amplification engine detects and amplifies low frequency but clinically significant features. This integrated configuration ensures automated pre diagnosis, personalized risk analysis, and continuously improving, privacy preserving clinical decision support.

[0045] Although the field of the invention has been described herein with limited reference to specific embodiments, this description is not meant to be construed in a limiting sense. Various modifications of the disclosed embodiments, as well as alternate embodiments of the invention, will become apparent to persons skilled in the art upon reference to the description of the invention. , Claims:1) A healthcare assessment system, comprising:

i) a vertically oriented diagnostic kiosk 101 installed within a clinic and configured to receive a patient for automated pre checkup;

ii) an interface disposed within the kiosk 101 and operatively connected to a linked microcontroller, the interface of the kiosk 101, comprising an input display 102 configured to:
a) present a structured questionnaire regarding at least current symptoms, duration, progression and prior treatment;
b) receive patient responses and uploads of prior reports including laboratory results, scan images and prescription data; and
c) display guidance and preliminary assessment results.

iii) an acoustic transducer 103 arranged with the kiosk 101 and configured to capture patient’s spoken responses, the acoustic transducer 103 being operatively coupled to the microcontroller to analyse vocal characteristics including at least tone, stress and hesitation;

iv) at least one artificial intelligence enabled camera 104 positioned with the kiosk 101 and oriented toward the patient, the AI camera 104 being configured to capture facial expressions and behavioral cues and to provide image data to the microcontroller for analysis of response consistency and symptom related visual indicators;

v) a compact CT (computed tomography) scanning unit 105 mounted on a vertically aligned dual axis lead screw arrangement 106 with the kiosk 101, the arrangement 106 being configured to move the scanning unit 105 along vertical and radial axes to perform focused scanning of selected body regions;

vi) an articulated probe 107 positioned with the kiosk 101 and terminating in a circular contact plate 108, the contact plate 108, comprising:
a) a plurality of gentle suction elements 109 configured to hold the plate 108 stably against a patient’s chest;
b) an acoustic sensor configured to capture heart and lung sounds and short cough and speech samples;
c) at least one ECG (electrocardiogram) sensor arranged to detect cardiac electrical activity; and
d) at least one PPG (Photoplethysmography) sensor configured to measure optical pulse signals.

vii) a communication interface configured to exchange anonymised or pseudonymised model updates and summary statistics with a remote aggregation service; and

viii) a learning engine executed by the remote aggregation service and operatively coupled to a plurality of such local computing modules deployed at different clinics, the learning engine being configured to:
a) receive locally trained model updates together with associated data quality and anomaly detection metrics from each clinic;
b) assign and update trust weights for each clinic based on at least update consistency, data quality and anomaly scores;
c) perform trust weighted aggregation of model parameters across clinics without accessing raw patient data;
d) route and update specialty specific sub models preferentially using updates from clinics with corresponding domain expertise while maintaining a shared backbone model; and
e) distribute updated global and specialty models back to the local computing modules for improved diagnostic performance;
wherein the system is configured to provide automated pre diagnosis, personalized risk analysis and continuously improving decision support while preserving patient privacy.

2) The system as claimed in claim 1, wherein the microcontroller is configured to:

a) extract vocal features including pitch, energy, pauses and prosodic variability from signals captured by the acoustic transducer 103;
b) extract facial expression and behavioral features from images captured by the artificial intelligence enabled camera 104; and
c) fuse the extracted features with questionnaire responses to generate, for each reported symptom, a numerical confidence score and an inconsistency indication between self report and physiological/behavioral signals.

3) The system as claimed in claim 1, wherein the microcontroller is further configured to compile the inconsistencies into a visual or tabular “inconsistency map” that is stored with the patient record and presented to a clinician as decision support context.

4) The system as claimed in claim 1, wherein the artificial intelligence enabled camera 104 is additionally configured to perform facial and ocular analysis to detect visual indicators including at least pale conjunctiva, yellow sclera, perioral or peripheral cyanosis, signs of dehydration, rashes and wound healing abnormalities, and to output corresponding symptom evidence flags to the microcontroller.

5) The system as claimed in claim 1, wherein the acoustic sensor in the circular contact plate 108 is configured to capture heart and lung sounds and the microcontroller is configured to analyse the captured acoustic signals to detect at least wheezes, crackles, rhonchi and cardiac murmurs and to classify respiratory infection risk, asthma exacerbation risk or COPD risk.

6) The system as claimed in claim 1, wherein the ECG sensor and PPG sensor are configured to provide synchronized signals to the microcontroller, and the microcontroller is configured to:

a) derive heart rate, heart rate variability and pulse transit metrics;
b) detect arrhythmias and bradycardia; and
c) compute an early sepsis risk score based on combined ECG/PPG features and vital sign deviations.

7) The system as claimed in claim 1, wherein further comprising a local data storage unit operatively coupled to the microcontroller, the data storage unit being configured to store questionnaire responses, uploaded laboratory reports, scan images and prescription data, CT scan summaries and preliminary assessment results, for longitudinal tracking and later review.

8) The system as claimed in claim 1, wherein the microcontroller is further configured to construct, for each patient encounter, a digital twin representation including at least physiological parameters, symptom profile, imaging findings and treatment history, and to store the digital twin parameters in the local data storage unit.

9) The system as claimed in claim 1, wherein the microcontroller is configured, after a subsequent treatment cycle, to compare the patient’s actual clinical response with the previously simulated digital twin predictions and to compute a twin divergence score that is stored and used to recalibrate future simulations and risk estimates.

10) The system as claimed in claim 1, wherein the remote aggregation service further includes a rare disease amplification engine configured to:

a) scan incoming local updates for low frequency but clinically significant patterns and rare condition markers; and
b) amplify the contribution of model parameters associated with such patterns during aggregation, thereby improve recognition of rare and a typical condition that may be under represented in single clinic data.