| Journal of Information and Communications Technology:
Algorithms, Systems and Applications
Received: 04 May 2026; Revised: 21 June 2026; Accepted: 29 June 2026; Published Online: 30 June 2026.
J. Inf. Commun. Technol. Algorithms Syst. Appl., 2026, 2(2), 26309 | Volume 2 Issue 2 (June 2026) | DOI: https://doi.org/10.64189/ict.26309
© The Author(s) 2026
This article is licensed under Creative Commons Attribution NonCommercial 4.0 International (CC-BY-NC 4.0)
An AI-Based Menu-Driven Healthcare Chatbot for
Underserved Communities
Sayyed Mohammed Abbas,* Amaan Shaikh, Shrushti Sapte, Zara Shaikh, Yumna Qureshi
and Awij Shaikh
Department of Electronics and Telecommunication, Anjuman-I-Islam’s M.H. Saboo Siddik College of Engineering, Byculla 400008,
Maharashtra, India
*Email: abbas.241644.et@mhssce.ac.in (Sayyed Mohammed Abbas)
Abstract
Language barriers, low levels of digital literacy, and inadequate medical resources limit people's access to
quality healthcare information, particularly in rural and semi-urban areas. In this research study, we present
SehatBot, an AI-driven healthcare chatbot that utilizes the WhatsApp Business platform as its delivery vehicle.
The goal of SehatBot is to provide multilingual, structured healthcare services in developing regions. Users can
ask questions about symptoms they are experiencing, view vaccination schedules, and access health alerts
issued by local governments through a user-friendly messaging interface. SehatBot was developed using Flask
as the backend framework with a modular architecture. Additionally, it uses webhook technology to
communicate with the Meta WhatsApp Business Cloud API, eliminating the need for users to install any
additional applications. To provide reliable and context-aware responses, a hybrid NLP approach has been
implemented by combining rule-based methods, a menu-driven finite-state mechanism, and optional machine
learning-based intent classification. The healthcare knowledge base is stored in JSON format and managed using
PostgreSQL in the production environment and SQLite in the development environment. Preliminary
evaluations were conducted with 1015 participants, who reported that SehatBot operated reliably, effectively
supported multiple languages, and responded within 0.5 to 1 second. These findings suggest that SehatBot
provides a scalable solution for improving health awareness, reducing misinformation, and increasing access to
trusted healthcare information in underserved communities.
Keywords: Healthcare Chatbot, WhatsApp Business API, Natural Language Processing (NLP), Multilingual Healthcare,
Underserved Communities.
1. Introduction





        

    


           








              
         

 


             



Despite significant advances in chatbot technology, relatively few healthcare chatbot systems successfully
integrate controlled interaction, multilingual support, and adaptability to resource-constrained environments.
Existing solutions often prioritize conversational capabilities at the expense of response consistency, while
others fail to address the accessibility needs of underserved populations. Consequently, there is a need for a
solution that balances the delivery of structured and reliable healthcare information with ease of use, ensuring
that healthcare communication is accurate, accessible, and inclusive for all users. To address these challenges,
this research develops and evaluates SehatBot, an AI-powered hybrid conversational chatbot designed
specifically for the WhatsApp messaging platform. SehatBot employs a menu-driven, rule-based finite-state
architecture, chosen for its simplicity, ease of implementation, and high usability among users. It also selectively
incorporates AI-based intent analysis to provide greater flexibility in interpreting user inputs. This hybrid
design combines a structured healthcare knowledge base with intelligent intent recognition to ensure the
consistent, accurate, and clear dissemination of healthcare information through a scalable and adaptable
system. The feasibility and effectiveness of the proposed SehatBot are evaluated through a controlled pilot study
conducted at the prototype level. This research contributes to the development of a multilingual healthcare
interaction framework, a structured healthcare knowledge organization strategy, and a messaging-based
deployment model designed for low-bandwidth and resource-constrained environments. The study focuses on
the conceptual design, system architecture, implementation, and prototype evaluation of the proposed chatbot.
Consequently, the system is not intended to perform clinical diagnoses, provide medical prescriptions, or initiate
emergency interventions.
[6]
Furthermore, the proposed solution is designed exclusively for deployment on the
WhatsApp messaging platform and does not include integration with other digital communication channels.
Due to the exploratory nature of this research, the study is limited to prototype-level evaluation and does not
include large-scale field deployment or statistical benchmarking of the system's performance.
2. Methodology
The proposed SehatBot system will be a modular, messaging-based healthcare chatbot that can provide
multilingual medical information via a structured conversational interface. The architecture of the system is
layered in separate modules or layers so each layer can be developed independently, scaled as needed, and
maintained separately. These include Messaging interface, Processing unit, Knowledge base, Data storage layer.
The backend of the system will utilize Python programming language and the Flask web framework to handle
incoming http request(s) and webhook communication.
[7]
Incoming user messages from the WhatsApp Business
API will be accepted via secure POST request.
[8]
Those POST requests will be passed and sent to the processing
layer.
[9]
For development & testing purposes, Ngrok will be used to expose the local Flask server to the internet,
allowing real-time webhook communication. However, in a production-oriented setup, deployment will be
configured on an Ubuntu-based virtual private server (VPS), where NGINX will act as a reverse proxy and
Gunicorn will manage application execution.
[10,11]
In addition to configuration of the VPS, Docker
containerization will be utilized to ensure portability of the application across multiple environments and
consistent deployment across multiple platforms.
[12]
The messaging layer is responsible for integrating with the
WhatsApp Business API, which provides encrypted communication using HTTPS & JSON payloads. It handles
only message transmission & reception, separated from internal processing logic. Once the received message is
processed by the core processing module, it performs intent recognition, state tracking, and response selection.
2.1 System workflow representation
In order to give a holistic overview of the interaction pipeline, the overall operational workflow of the chatbot
is depicted in Fig. 1. Fig. 1 gives a representation of the stages of processing in a sequential manner from the
user initiation to the response generation and then to the continuation of the session. Instead of giving a
description of the technologies, the workflow is centered on transitions between user interaction and response
generation.
2.1.1 Explanation
User input either via text or voice is routed through a central processing unit. Voice input is converted to text by
Speech-To-Text (STT), whereas text input is sent through to analysis. To generate the appropriate response path,
a finite state machine (FSM) is utilized to control conversation flow; it will ensure that users advance through a
series of steps in a logical sequence that includes collecting input, analyzing input and generating a response.
[2,13]
Once the relevant information has been retrieved, the response generation process utilizes contextually
tracked state along with knowledge retrieval to format the response into readable sections in the form of
structured messages suited for messaging interfaces. Upon providing a response, the system presents options
to allow users to proceed with their interaction or end the interaction thus controlling the conversational loop.
The Natural Language Processing (NLP) component of the system employs a hybrid method that combines rule-
based methodology with NLP based on models. Rule-based methodologies are employed when there are
predefined questions/queries and when consistency in medical responses is required, whereas NLP models can
be applied to more general user input to analyze them. In this manner, the system maintains both reliability and
flexibility in its usage scenarios. Multilingual support is implemented within the system utilizing a translation
layer. Internal processing is conducted utilizing English, while all user input from other languages is translated
prior to processing and then translated back after processing to provide the final response. Pretranslated static
content related to critical medical information are stored to ensure accurate translations; however, dynamic
conversational components are translated in real-time. Thus, this hybrid translation approach strikes balance
between efficiency and reliability.
Fig.1: Technical flow chart of the system.
2.2 Knowledge base representation
The knowledge base uses Hierarchical JSON Schemas. Each disease entry in the schema includes attributes for
symptoms, cause, preventative measure, and treatment guidance. The hierarchical structure enables efficient
retrieval and consistent formatting of responses during interactions. When developing the knowledge base,
SQLite was utilized.
[14]
The prototype interface was developed using a WhatsApp-based interface that provides
an easy to follow structured and user-friendly interaction process; including language selection and then a menu
driven navigation process. The design minimizes user confusion, especially among those who are new to
chatbots. Manual testing evaluates multilingual responses, and how the application handles different user
scenarios, including state transitions. The end-to-end testing tests the entire interaction pipeline from when the
message is received to when it is processed and delivered back to the user. Using third-party tools like PostMan
to perform API testing verifies proper webhook communication, as well as the format and structure of payloads.
Using unit testing on each Flask route ensures the applications can correctly handle requests. For production
purposes, we would use PostgreSQL (a large-scale relational database) for managing our larger dataset and
supporting JSON based queries.
[15]
For our application we will store temporary conversation states in memory
and record all interactions logged into our database for further analysis and improving our overall system.
2.3 Prototype interface and user interaction flow
The prototype has some functional capabilities; for example, it enables users to retrieve data about diseases by
entering the name of a disease and obtain a category-based list of symptoms associated with the entered
disease, also preventive measures associated with the entered disease, and general health related information
for the entered disease. Each response is broken up into smaller sections that are easily distinguishable from
one another so they are easy to read on a limited screen like those found on mobile devices or messaging
platforms. The prototype also demonstrates the ability of an integrated system (for example it uses state
management, multi-lingual processing, and database driven information) which is a good indicator of whether
the system design will be effective in a deployed environment. In addition, this prototype includes actual images
of the screens used during interactions with the system, which provide concrete evidence that the prototype
was tested in a real-world deployed environment versus simulated testing, therefore increasing the amount of
confidence that other developers would have in implementing the same system design.
3. Results and discussion
This pilot test had about 10-15 individuals from the development team. Functional correctness, multi-language
capabilities to respond to user queries, menu-based navigation and the ability of the system to operate stably
and perform reliably under normal operating conditions were tested. Realtime testing occurred using a
smartphone device as the client (via WhatsApp) and the server was running in development mode. Automated
benchmarking tool(s) were not used manual observation records were kept. Working Prototype of the System
is shown in Fig. 2.
Fig. 2: Working prototype of the system.
The system performed well in all tests that were run. There were no navigational "dead ends" in the workflow
of core functions such as accessing disease information and checking symptoms. The Webhook communication
pipeline successfully sent and received messages (requests & responses) via the WhatsApp Business API.
Average response time per query ranged approximately 0.5 seconds when there were minimal network delays,
which is comparable to near real-time interaction that can be practically used.
[16]
Testing for multiple languages
was done in separate sessions. When an individual chose a language for their session, that language choice
continued for the entire length of their session. There were no apparent problems with grammar or semantics
related to how the system responded back to users. A hybrid translation method was employed where pre-
translated medical content was provided with some degree of dynamically translated content, and it served its
purpose to provide clarity and reliability while minimizing potential problems with full automation translation.
On average, users could find what they needed by asking questions and receiving answers in 2 to 3 iterations.
Because the finite state machine-based design of the system defined the order in which things should happen
as part of a conversation and/or transaction, it helped create a structure and direction to conversations thereby
reducing confusion for users with less experience with digital technology.
[1]
No failures due to loss of
connectivity were experienced during testing but no extreme load testing or very large scale testing has been
completed at this point. In terms of being flexible yet consistent, the Hybrid Conversational Model worked well
because it allowed for rule-based logic to generate consistent responses as well as allowed for limited Natural
Language Processing (NLP) to allow for slight variations in inputs.
From an implementation standpoint the system is planned to be implemented upon lower cost infrastructure
using well known communication systems like WhatsApp and SMS therefore decreasing the dependency upon
custom applications or high-end user device requirements. A comprehensive feature comparison between
SehatBot and existing digital health platforms is detailed in Table 1. The primary cost of operations will be
related to message transmission services, back-end infrastructure, and maintenance. The system has been built
around a sustainability model that does not charge the end-user; but rather generates income from
relationships with health care organizations/providers, service integrations and partnerships with institutions.
This strategy provides the necessary resources for the longevity of the system while at the same time allowing
for accessibility. However, widespread use of SMS in low connectivity areas could increase operational costs and
require optimization. While there have been many benefits, there are some significant limitations. Due to the
fact that this study was based on a small number of participants who all worked within the
company/organization, this study does not provide results on how the system would perform in a larger scale
or in-field testing environment. In addition, since no formal quantitative measures (i.e., accuracy rate,
completion times) were collected during testing, it is difficult to compare different types of input (e.g., keyboard
vs. touch screen). Currently, the system is only able to send messages over the WhatsApp application and thus
comparison data does not exist. In general terms, these results support the concept of utilizing a structurally
organized multi-lingual chat bot deployed on a messaging platform to enhance access to quality healthcare
information within resource-poor communities.
[17]
The system achieved its objectives of providing usable
functionality, timely responses, and controlling what information is provided to the user while demonstrating
potential for scalable and sustainable real-world implementations.
Table 1: Comprehensive feature comparison between SehatBot, MFine, DocsApp and Tata1mg
Parameter
SehatBot
MFine
DocsApp
Tata1mg
Platform
Accessibility
WhatsApp-Based (no
app installation)
Mobile application
Mobile application
Mobile application
Multilingual
Support
Extensive (English,
Hindi, Odia, Regional
languages)
Partial
Limited
Partial
Support for low
Digital Literacy
Menu-driven interaction
Limited
Limited
Limited
Disease
Information and
Awareness
Available
Available
Available
Available
Symptom based
Disease Guidance
Available
Available
Available
Available
Vaccination
Schedule Guidance
Available
Limited
Not Available
Partial
Government
Health-Data
Integration
Available (IDSP,
MoHFW)
Not Available
Not Available
Not Available
Public Health
Outbreak Alerts
Available
Not Available
Not Available
Not Available
Primary
Healthcare Focus
Preventive healthcare
and awareness
Online
consultation
Online consultation
Pharmacy and consultation
Cost to End User
Free
Consultation
charges apply
Consultation charges
apply
Basic service free
(Consultation charges apply)
4. Conclusion
SehatBot was a messaging-based chat bot created as part of this research project to help provide access to
structured/reliable health care information in low resource areas. To ensure both consistent and usable
responses from the chat bot a hybrid conversation model was developed combining three different approaches
including; Rule-Based Logic (RBL) Finite State Machine (FSM) Interaction Limited Natural Language Processing
(LNLP). By using the WhatsApp platform to deploy the chat bot, the researchers were able to leverage an
existing common mode of communication which would assist in reducing barriers to adoption. An internal pilot
level assessment indicated that the system performed well under normal operating conditions provided
effective multilingual functionality and produced near real time results. Additionally, because of the use of a
structured, menu driven approach to designing the user interface, users are better able to navigate through the
application and avoid confusion which can be a challenge for many who have limited digital experience.
Although there has been some evidence of success identified as part of this research, there are limitations
related to this study. Due to the nature of this being an internal pilot level assessment with a relatively small
number of participants, the researchers did not include a larger scale or external quantitative analysis.
Therefore, future research will need to investigate how to deploy the system at a wider scale, integrate the
system into multiple platforms and create performance metrics that allow researchers to assess the
effectiveness of the system. In general, this research provides evidence that using structured chat bots may be
one way to increase access to healthcare information in communities with limited availability of traditional
sources of information.
CRediT Author Contribution Statement
Mohammed Abbas Sayyed: Conceptualization, Methodology, Software, Validation, Investigation, Data
curation, Visualization, Writing Original draft. Amaan Shaikh: Conceptualization, Methodology, Software,
Validation, Investigation, Data curation, Visualization, Writing Original draft. Shrushti Sapte:
Conceptualization, Methodology, Software, Validation, Investigation, Data curation, Visualization, Writing
Review & editing. Zara Shaikh: Conceptualization, Methodology, Software, Validation, Investigation, Data
curation, Visualization, Writing Review & editing. Yumna Qureshi: Conceptualization, Methodology, Software
development, System implementation, Validation, Investigation, Data curation, Visualization, Writing Original
draft. Awij Shaikh: Conceptualization, Methodology, Software, Validation, Investigation, Data curation,
Visualization, Writing Review & editing. All authors participated in the design and development of the
SehatBot prototype, reviewed the manuscript critically for important intellectual content, approved the final
manuscript, and agree to be accountable for all aspects of the work. All authors have read and agreed to the
published version of the manuscript.
Data Availability Statement
The datasets and implementation details supporting the findings of this study are available from the
corresponding author upon reasonable request.
Conflict of Interest
There is no conflict of interest.
Artificial Intelligence (AI) Use Disclosure
The authors declare that artificial intelligence (AI)-assisted tools were used only for language refinement,
grammar improvement, and manuscript structuring purposes during the preparation of this work. All technical
content, experimental implementation, results, and interpretations were independently developed and verified
by the authors.
Supporting Information
Not applicable.
References
[1]
[2]
[3]
[4]
[5]
[6]
[7]
[8]
[9]
[10]
[11]
[12]
[13]
[14]
[15]
[16]
[17]
Publisher Note: The views, statements, and data in all publications solely belong to the authors and
contributors. GR Scholastic is not responsible for any injury resulting from the ideas, methods, or products
mentioned. GR Scholastic remains neutral regarding jurisdictional claims in published maps and institutional
affiliations.
Open Access
This article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License, which
permits the non-commercial use, sharing, adaptation, distribution and reproduction in any medium or format,
as long as appropriate credit to the original author(s) and the source is given by providing a link to the Creative
Commons License and changes need to be indicated if there are any. The images or other third-party material
in this article are included in the article's Creative Commons License, unless indicated otherwise in a credit line
to the material. If material is not included in the article's Creative Commons License and your intended use is
not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly
from the copyright holder. To view a copy of this License, visit: https://creativecommons.org/licenses/by-
nc/4.0/
© The Author(s) 2026