AI‑Driven Doctor Scheduling and Virtual Healthcare Companion

G. Supriya, G. Bhavani Shankar Goud, B. Siva Bhargav, C. Hemalatha and N. Gireesh

Department of Electronics and Communication Engineering, Mohan Babu University (Erstwhile Sree Vidyanikethan

Engineering College), Tirupati‑517102, Andra Pradesh, India

Keywords: AI, Doctor Scheduling, Virtual Healthcare, AI Bot, Symptom Analysis, Appointment Automation, Patient

Guidance, Healthcare Remedies, Real‑Time Data, Patient Experience, Healthcare Automation.

Abstract: The AI-powered appointment management solution, the Future of Telemedicine — the AI-Driven Doctor

Scheduling and Virtual Healthcare Companion. AI is used in this system to evaluate the resulting symptoms

and to recommend whether a visit to a doctor is needed or whether one can take care of themselves. It

simplifies the appointments process, breaking down the severity of symptoms, offering personalized health

recommendations, and routing patients to the appropriate specialists. It provides immediate answers to patient

inquiries, assists with efficient scheduling, and streamlines appointment management. It removes

administrative burdens, improves healthcare access, and improves patient experience through personalized,

timely help. Integrating AI to deliver reliable recommendations that improve the efficacy and accessibility of

healthcare.

1 INTRODUCTION

AI's most powerful in the management of patients and

services in the healthcare There are inefficiencies in

appointment scheduling, assistance with preliminary

diagnosis etc such as enduring waiting queue for

patients, mismanagement of appointment slots among

the patient candidates, and unavailability of

immediate guidance for patients from the medical

staff. Cross checking by hand is replaced by intuitive

algorithms that make sense of data from patient

symptoms and offer actionable suggestions with AI-

based doctor scheduling systems.

Patient queuing at a global level is still one of the

difficult problems worldwide health care systems

face. Late scheduling may lead to undue suffering for

patients and waste of other scarce health care

resources. These delays caused by ineffective patient

booking tools cause overcrowding of hospitals, user

dissatisfaction, and burnout of health professionals

(R. K. Smith, et al., 2022). AI-based scheduling

solutions are being created to aid patient data,

prioritization based on urgency, and automate the

booking process to ensure alleviation of the inquiries

pertaining to these problems.

one of the great challenges in health care is not

getting the right medical advice at the right time.

Many patients have a hard time determining whether

their symptoms necessitate a trip to a doctor’s office

or can simply be treated at home. AI-powered virtual

healthcare assistants fill the gap between patients’

first symptoms and a medical professional’s opinion.

These algorithms utilize NLP and medical historical

data by training machine learning models to provide

recommendations depending on the seriousness of the

symptoms as well as past outcomes from other

patients (J. T. Lee, et al. 2021).

In addition, AI-powered healthcare assistants can

streamline the administrative duties of hospital staff

by coordinating patient records, appointment

reminders, and follow-ups, among others. In this

regard, a research about AI applications in hospital

management highlighted that automation for

scheduling and virtual consultation was able to reduce

patient wait time by almost 30% while enhancing

levels of overall satisfaction (P. D. Patel and M. S.

Agarwal, 2023). Also, it can provide medical

majority or rural areas so that people can get medical

advice without the need to see a doctor immediately

by combining AI bots with telemedicine services.

(M. Fernandez, et al.,2021).

Deploying AI in healthcare also touches on

security and data privacy, two equally essential

factors. And following laws, such as the General Data

Protection Regulation (GDPR) and the Health

Insurance Portability and Accountability Act

866

Supriya, G., Goud, G. B. S., Bhargav, B. S., Hemalatha, C. and Gireesh, N.

AI-Driven Doctor Scheduling and Virtual Healthcare Companion.

DOI: 10.5220/0013874800004919

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies (ICRDICCT‘25 2025) - Volume 1, pages

866-874

ISBN: 978-989-758-777-1

(HIPAA), is key to maintaining patient trust. The

researchers highlighted the importance of response of

encrypting and anonymizing the data to protect

patients' most sensitive medical information from

cyber threats (L. N. Thompson et., al. 2023).

AI-driven doctor scheduling and virtual

healthcare companions are simply the first steps to a

more efficient healthcare system, which increases

accessibility and relies more on patient-centricity. By

unifying intelligent decision-making, automated

scheduling, and real-time data processing these

systems lead not only to better utilization of medical

ressources, but also to enhanced patient experience.

Given the ongoing innovations in Artificial

intelligence, the future of healthcare will be more

smart, predictive and patient-centric (S. Gupta and R.

K. Sharma, 2022).

2 LITERATURE SURVEY

Classic healthcare appointment systems are often

plagued with long waiting periods, non-availability

and administrative inefficiencies. The challenges are

exacerbated by the growing need for healthcare

services and the urgent requirement for timely,

personalized patient care. Patients often experience

frustration over navigating complex scheduling

processes that result in delays in needed care and

decreased satisfaction. Moreover, healthcare

providers grapple with considerable administrative

challenges in appointment management that can

detract from their core mission of providing top-notch

patient care. This gap between what patients need and

what the system can provide underscores the critical

need for innovative solutions. Literature survey on

Modernizing Healthcare Appointment Systems with

AI: A review of Literature on Integrating artificial

intelligence for patient scheduling, triage and patient

assistance.

Patients will have their own profiles with many

options, including the ability to enter medical

information, check past records, schedule online

appointments with registered physicians, and take

medication from the designated physician. Through a

message system, the designated physician can listen to

the patient's health concerns and view the patient's

data (F. Anjum et al., 2018). It functions with mobile

devices like smartphones, offering user interfaces for

setting up medication regimens and notifications to

remind users of the kind and time of their medications

in accordance with the schedule. To ensure that

patients take their medications as directed, several

systems make use of sensors, radio-frequency

identification (RFID), or motion detection technology.

Up to 15 reminders are supported by this free app.

Both repeating and non-repeating alarm patterns can

make advantage of this feature. Any hourly period

between notifications can be selected, with a

minimum of one hour being the first option. (Deepti

Ameta et al.,2015).

If the patient must walk in after making the

appointment online, the front desk staff will still greet

them before sending them to the relevant physician.

Given this issue, numerous methods—including those

found online—have been proposed to enhance

workflow and hence reduce wait times. These systems

do, however, still have certain shortcomings, such as

the inability to prioritize tasks, the lack of a patient

security system, and the absence of an appointment

reminder (Yeo Symey et al., 2013). This system's

primary objective is to generate reports based on

prognoses, which is a unique capability. The

prognosis algorithm will identify and evaluate the vital

signs, which include BMI, metabolic syndrome, and

the Framingham heart study. They quickly determine

a person's level of illness and the urgency of their

medical care needs, such as by measuring metabolic

syndrome, which includes excessive body glucose,

abdominal obesity, HDL, and total cholesterol. The

user is at risk for heart disease if we identify three or

more disorders in him. In emergency departments,

where patients are seen according to their most

pressing requirements rather than on a first-come,

first-served basis, this technique is very useful. For

example, clinicians can use this approach to prioritize

a patient who is bleeding internally, has an abnormally

rapid heart rate, low blood pressure, and needs

immediate attention (Jagannath Aghav et al.,2014).

This system will search availability of nearest

specialized hospital through the EMS server which

provides continues information about hospital to the

patient Additionally, it includes certain helpful

services for users, such as the Clinic model and Blood

Bank Tracking. Therefore, a user can log in to the

system and send their status to the server if they are

experiencing any health issues. Any emergency—

accident, heart attack, burn case, etc.—will be chosen

and forwarded to the server. After accepting the

request, the client looks for the closest hospital

(Rashmi A et al.,2014). By developing and

implementing the "Mwa3edk" system and mobile

application, which introduces fresh concepts for the

process of making doctor's appointments in hospitals

and clinics by moving this process to online

technology, the main goal of this research is to support

the UAE's Smart Cities. Approach Customers can use

this system to find doctors in different locations and

AI-Driven Doctor Scheduling and Virtual Healthcare Companion

867

make appointments that work for them, as well as

connect with a large number of hospitals and clinics

throughout the United Arab Emirates. (Odeh et

al.,2019).

Agent-based systems that search and schedule

appointments via mobile devices and provide a direct

response when the appointment is completed, the next

open day or dates, or is canceled have also been

developed for the hospital service. Nevertheless, no

facility such as patient priority appointments has been

established. Additionally, the recorded scheduling is

solely for general patient appointments and does not

account for emergency events such as accidents, heart

attacks, etc. (Arthur Hylton III and Suresh

Sankaranarayanan et al.,2013). Life is become too

hectic to schedule doctor's appointments and maintain

appropriate health care. People’s medication regimens

can be completely altered by time zone changes

brought on by travel, and those who are dealing with

severe medical issues may find it challenging to

handle such trips and employment. Patients with high

blood pressure and diabetes should take their

medications as prescribed. However, people's current

hectic schedules prevent them from taking their pills

on time. This issue is widespread around the world,

making it challenging for workaholics and travelers to

balance their job and health. Technology has given us

a lot of options, and this research suggests one of

them: utilizing the Android platform to lead a healthy

lifestyle (S. Gavaskar et al.,2013).

The integration of AI into healthcare has ushered

in a new era of opportunities, offering innovative

solutions to long-standing challenges in patient care,

and laying the groundwork for a more connected

future where precision medicine becomes the norm.

One of the primary focuses includes the automation

of the frequently tedious appointment scheduling

process to reduce administrative burden and

significantly enhance overall efficiency (Smith, J., &

Jones, A. (2020)). AI-powered systems have the

ability to assess a large amount of patient data, such as

symptoms reported by patients, wide-ranging medical

history, and even insurance data, in order to ensure

effective scheduling of appointments, and to reduce

patient waiting time (Brown, B., et al. (2021)). Smart

algorithms can immediately use real-time information

about availability and urgency to modify appointment

slots, enabling healthcare systems to deploy precious

supply when needed most. Also, intelligent systems

can match patients with the most appropriate

healthcare provider based on the needs of the patient

such as medical specialty, provider experience, and

patient preferences (Davis, C., & Wilson, D. (2022)).

This intelligent process ensures more efficient

consultations and ultimately better patient outcomes.

Outside of scheduling, AI can help to bridge

treatment by providing real-time care, especially

outside of clinic hours when there might not be limited

availability of care. Chatbots, or virtual assistants that

utilize advanced natural language processing (NLP)

algorithms, are capable of interacting with patients in

a conversational manner, responding to their health-

related queries, and giving personalized, evidence-

based health recommendations tailored to established

and up-to-date clinical guidelines (Garcia, E., et al.

(2023)). Beyond merely answering commonly asked

questions (FAQs), these AI-powered tools can support

the essential act of triaging patients, assessing the

severity of patients’ symptoms, and advising the most

appropriate course of action, whether that be

advocating for self-care advice, making a virtual

appointment for a timely consultation, or

recommending that the patient go directly to a health

facility (Thompson, L., & Richards, M. (2023)). Not

only does this effective triage functionality ensure that

the patient is treated correctly at the right time, it also

enables the optimal allocation of resources and

enhances patient outcomes while relieving some of the

pressure on often overstretched emergency services.

An exciting segment of this integration of AI into

the healthcare appointment system is the ability to

increase patient engagement and satisfaction with the

healthcare experience. Furthermore, AI can provide

real-time appointment reminders and personalized

health information, based on demographics, as well

as providing choice of multiple healthcare providers

across a range of communication streams, enabling

patients to take a more proactive and informed

approach towards their care (Patel, R., & Nguyen, T.

(2022)). Additionally, AI also can behave on

communication issues that happen in the relation

between patients and health-care workers being

patients with health literacy barriers or

anthropophagic language barriers so that all patients

can obtain the correct education and information that

helps them make sensate decisions about their health

(Roberts, M., et al. (2023)). Moreover, AI-based

translation utilities and personalized learning

resources are enhancing patient understanding,

leading to increased adherence to therapeutic

protocols and better health outcomes as a result.

While AI holds great promise for healthcare, the

application of artificial intelligence in this context

poses unique and significant challenges that require

careful attention. Ensuring full privacy of sensitive

health data of patients is of utmost importance (Singh,

A., & Lee, K. (2023)). Strong security measures—

ICRDICCT‘25 2025 - INTERNATIONAL CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION,

COMMUNICATION, AND COMPUTING TECHNOLOGIES

868

including encryptions and access controls—as well as

stringent adherence to various data privacy laws,

including HIPAA, are critical to maintain patient trust

and prevent unwanted access to personal data. It is

essential to proactively eliminate potential biases that

may be present in AI algorithms to prevent

inequalities in patient care and to assure equitable

access to high-quality healthcare services for all

populations, regardless of background or demography

(White, P., et al. (2024)). Specific BI use cases are

mapped to a specific form of data-driven analysis,

which can include mining, predictive, content

analytics & knowledge management, natural language

processing, and data science & predictive analytics.

To ensure the productive and beneficial use of AI

in healthcare, even ethical aspects of responsibility

and transparency, must be carefully considered and

understood. Patients should have the option to decline

AI-led interactions should they wish to, and they

should be fully aware of the way in which AI is being

applied to their care. Moreover, healthcare providers

should ensure human oversight of all intermediary

decisions, and should be liable for decisions taken by

AI.

The development and use of AI algorithms must

also be done in an open and transparent manner to

maintain public trust, but more importantly to ensure

algorithms are developing for the benefit of the

patient and society. The inflow of Artificial

Intelligence into the Healthcare sector is none and

one without the other but an analogous jump in

security by everything and anybody involved,

whether this is a tech engineer, a healthcare

professional or a policy maker. establishing these

questions will require careful thought around the

ethics, social, and legal aspects of employing AI in

healthcare. It is also dependent upon continued

research to improve the accuracy and reliability, as

well as the safety, of AI algorithms in medical

settings, and the development of wider evidence on

their effectiveness. Note that these systems must also

be under additional assessment to ascertain that they

are reaching their target and not causing unforeseen

damage.

There are multiple intricacies of implementing

AI for scheduling in healthcare. The implementation

of these validation checks on the discharge summary

in a non-disruptive manner is a challenge posed by

EHR systems. AI systems need to interoperate with

EHRs to obtain patient information and formulations

of recommendations in the context of a patient’s

complete clinical history. Also, you need to develop

interfaces which should be patient and healthcare

provider friendly. “AI systems should present with

familiar, intuitive layouts so that they are easy to

navigate no matter what the user’s technology history

is.

In addition, to successfully implement AI in

healthcare, careful change management is required.

Training of health care personnel and staff on the

proper use of AI systems and their limitations should

be provided. It is also crucial to build trust in these

technologies; both health care providers and patients

need to have confidence that recommendations

resulting from AI are accurate and reliable.

Addressing these challenges necessitates a

comprehensive strategy that integrates education,

training, and ongoing support.

The success of AI in health care long term will

depend on how we address these challenges and on

whether we can ensure that AI systems complement

human expertise rather than render it obsolete. AI

needs to be viewed as an aid to, rather than a

replacement for, human beings in the provision of

care. The human side of healthcare empathy,

communication and personal connection continues to

be paramount in providing high-quality patient care.

AI the future of healthcare appointments lies in its

smart integration These systems are not only designed

to streamline administrative tasks but also enhance,

significantly, the personalization of the patient

experience. Singling out AI, healthcare providers can

dedicate more time to genuine patient care — care

that is caring, useful, swift. The possibilities of AI to

enhance healthcare are immense, and its continued

generation and responsible application are essential to

creating a more accessible, equitable, and patient-

centred healthcare system for all.

AI-powered healthcare appointment systems

present a promising path forward, with continuous

research and development to improve their accuracy,

reliability, and user experience. As AI technology

develops, we can expect even more sophisticated and

integrated systems that will continue to transform

how patients’ access and engage in care. AI will be a

vital part of that vision as the future of healthcare

becomes more intelligent, personalized and focused

on patients.

AI is an area of active debate in terms of ethics in

healthcare. The relevance of unfiltered and candid

conversations concerning the ethical implications of

AI in the healthcare domain cannot be overstated;

these discussions are needed to motivate the

alignment of such technologies with societal norms

and ethical principles. And that means addressing

challenges such as bias, fairness, transparency and

accountability.

AI-Driven Doctor Scheduling and Virtual Healthcare Companion

869

To scale the integration of AI in clinical practice,

a multidisciplinary approach involving clinicians,

technical specialists, policy makers and patients is

needed to ensure well-grounded outcomes. Together,

we can ensure that AI is deployed responsibly and

ultimately contributes to a safer and fairer world.

The possibilities of how AI can improve

healthcare are vast and varied. Whether streamlining

administrative tasks to providing personalized patient

care, AI has the potential to completely change the

healthcare landscape. By seizing the advantages that

AI provides and adequately mitigating its challenges

we can build a future in which healthcare is more

patient-facing, streamlined and accessible.

The road to AI-empowered healthcare has only

just begun, and the possibilities are endless. As we

explore the potential of AI in healthcare further, let us

remain committed to principles of responsible and

ethical innovation, to ensure that these technologies

are developed in a way that it improves the lives of

patients and serves the best interest of all.

AI: The Brain Powering Intelligent Patient

CareThe future of healthcare is intelligent,

personalized and patient-centric. By recognizing the

opportunities that AI presents and addressing the

challenges, we peuvent des meilleure future

healthcare system accessible, efficient et equitable.

3 EXISTING SYSTEM

The current methods of setting up healthcare

appointments typically utilizes outdated and

inefficient techniques. Many clinics and hospitals

still rely on traditional methods, e.g., phones,

paperwork management for appointments, and paper

work. This can cause long delays for patients, errors

in scheduling, and frustration on the part of both

patients and staff. For issues faced by a modern

healthcare system such as greater integration

between different healthcare providers, diverse types

of appointments, and a wider range of patient

requirements, these manual systems have largely

failed us. Not to mention chronic understaffing where

real time tracking of appointment slots makes no

practical sense on booking appointments.

Another somewhat prevalent practice is the use of

websites with a contact form where a potential

patient can request an appointment. While these

methods represent a step up from a purely manual

system, they tend to lack the robustness necessary to

significantly improve scheduling. Patients, say, still

have to navigate convoluted menus, wait for staff to

affirm the appointment and hunt for times that work

for them. Moreover, such systems seldom integrate

with other critical parts of health care delivery, such

as patient records, telehealth services, or even

automated reminders. As a result of this gap in

integration, the workflows are disjointed and there is

poor communications present between the patients

and the service providers.

AI has enormous potential, across a broad scope

of subsectors, to improve the healthcare industry.

From streamlining administrative tasks to providing

personalized care, AI can transform the delivery of

healthcare. By taking advantage of the benefits AI

brings and carefully navigating its challenges, we can

build a future with more patient-centered, efficient,

and accessible healthcare. The journey towards AI-

empowered healthcare has only begun, and the

possibilities are endless. However, as we continue to

explore the frontiers of what AI can enable, we need

to remain committed to the principles of ethical,

responsible innovation to harness these powerful

technologies to improve the outcomes and care for all

patients and the well-being of all people. AI is Vital

in Patient-Focused Healthcare of the Future By

harnessing the capability that AI provides and

appropriately addressing the challenges, we can build

a more accessible, efficient and equitable healthcare

system for the future.

Even with these incremental reform initiatives, the

type of grand and thoughtful strategy for designing

the health care system remains criminally lacking.

Existing systems often contend with poor integration,

limited automation functions, and the inability of

individual patients to help. The need for a more

intelligent and interconnected approach is evident,

one that leverages the power of AI, to leverage the

power of AI to facilitate communication and

ultimately enhance the accessibility and quality of

the health care system.

4 PROPOSED SYSTEM

The AI-Driven Doctor Scheduling and Virtual

Healthcare Companion, which is currently being

developed, aims to improve the healthcare

appointment system by creating an intelligent

integrated platform. This system seeks to resolve the

shortcomings present in current systems by

automating important processes, enabling

personalized patient interaction, and improving

overall efficacy. This is achieved using an open-

source bot framework, integrated, and tailored to

interact with patients. The bot provides healthcare

advice and patient navigation by screening and

ICRDICCT‘25 2025 - INTERNATIONAL CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION,

COMMUNICATION, AND COMPUTING TECHNOLOGIES

870

guiding patients as they wish to schedule an

appointment. The approach uses open-source

technology’s extreme flexibility and modern

healthcare needs to create a better solution bound to

deliver results.00

It begins with intelligent symptom analysis

triage. The integrated bot allows patients to describe

symptoms in free text. The bot is designed, using

web crawlers, to analyse the data and severity of

patients' conditions. This analysis then informs

patients whether additional self-care or a visit to a

doctor is needed. This type of triage may help limit

unnecessary visits to clinics while empowering

patients to manage their own health. The system

automates appointment scheduling wherever further

investigation with a specialist is required.

It then uses that data to provide a video-based

healthcare companion that helps a patient with an

appointment. It considers the severity of symptoms,

the kind of provider a patient has in mind, which

doctors are available, and their specialties in order to

recommend a suitable provider. The system can

interface directly with in-place electronic RHR

systems and include relevant patient data to ensure

the appointment recommendation addresses the

patient's medical profile. The bot may also perform

appointment cancellations, rescheduling, and

reminders. It also issues pre and post-appointment

instructions specific to the patient.

In addition to switching appointments, this bot also

aids with ongoing patient support. The bot can

respond to patients' inquiries regarding their health,

medications, and treatment plans based on a wide

range of information available. The bot is meant to

give the patient the proper information, however it

also must stress the necessity of visiting healthcare

providers for specific medical treatment. This

functionality of the virtual healthcare assistant

increases patient engagement, communication

between patients, and healthcare providers, and

encourages patients to become more involved in their

treatment. The proposed system also provides

healthcare providers with a strong administrative

interface.

Figure 1: Proposed System.

The proposed system aims to streamline the

process of booking doctor appointments by integrating

an online platform with an AI-powered chatbot. The

system provides three types of users: patients, doctors,

and administrators, each with distinct functionalities.

The system flow is depicted in Figure 1.

4.1 System Flow and User Roles

The process begins with a login module, where users

can either log in as an existing user or create a new

account if they are first-time users. The system

categorizes users into three roles:

4.1.1 Doctor

• Doctors can add chambers where they practice.

• They can set their time schedules for

availability.

• Doctors can view their patient list for better

management of appointments.

AI-Driven Doctor Scheduling and Virtual Healthcare Companion

871

4.1.2 Patient

• Patients can search for doctors based on their

specialization.

• They can select a chamber where the doctor is

available.

• Patients proceed to select a suitable date and

time for their appointment.

• A payment transactionp is required to confirm

the booking.

• The system validates the payment before

finalizing the appointment.

4.1.3 Administrator

• Admins have the authority to manage doctors

and patients registered in the system.

• They can approve or reject doctor registration

requests after verification.

• They also approve or reject appointment

requests, ensuring a controlled and reliable

scheduling system.

4.2 AI Chatbot Integration

To enhance user experience, the system integrates an

AI chatbot that assists patients with:

• Providing doctor recommendations based on

symptoms.

• Answering frequently asked questions

related to health concerns.

• Guiding users through the appointment

booking process.

4.2.1 Payment Validation and Appointment

Confirmation

Once the patient selects a doctor and appointment slot,

they must complete a payment transaction. The system

validates the payment before confirming the booking.

If the transaction is successful, the appointment is

confirmed.

5 METHODOLGY

Online Doctor Appointment Booking System Project

Documentation/Report. It comprises different parts,

like user validation, doctor choosing, appointment,

AI enabled patient help and data base handling. There

is a categorized method by which users can navigate

the system easily and get intelligent support using an

AI chatbot.

The process starts when a patient visits the

appointment booking portal. New users need to create

an account, while existing users can log in directly.

After authentication, the system displays available

doctors based on various filters like specialization,

experience, and availability. The patient selects a

doctor and then picks a suitable time slot from the

options provided. The system checks with the doctor

and time database to confirm the availability of the

chosen slot. If the selected time is open, the

appointment request is processed and recorded in the

database. Unlike traditional systems that depend

heavily on notifications and reminders,

this platform improves user experience by integrating

an AI chatbot, which actively engages with patients,

guiding them through each step of the booking

process. Figure 2 shows the system architecture.

Figure 2: System Architectecture.

One major component is an AI chatbot integrated

into the patient portal. This is a chatbot, an interactive

assistant, designed to help users decide if they should

schedule a regular appointment or if they need

quicker medical attention based on their symptoms.

Instead of static appointment systems, our AI bot

suggests dynamic appointments according to the

patient’s input. It opens a dialogue by asking the

patient about their symptoms, severity and medical

history. Based on established medical guidelines and

expert recommendations, it advises whether the

patient should book an appointment immediately or

whether home remedies and self-care will suffice for

less serious problems. If an appointment is required,

the process flows smoothly.”

ICRDICCT‘25 2025 - INTERNATIONAL CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION,

COMMUNICATION, AND COMPUTING TECHNOLOGIES

872

6 RESULTS AND DISCUSSION

With the introduction of the online booking system

for doctor appointments, along with an integrated AI

chatbot, potentially, the time and accessibility

efficiency has improved and the results have changed.

Patients report the system is user-friendly, and the

time required to book appointments via this method

is significantly less than in other venues. This

includes the AI chatbot that helps to screen patients

and provide them immediate guidance on whether

they need to book an appointment or if there are other

remedies that might serve them. This has made

benefits decision-making more efficient, minimizing

superfluous doctor appointments and optiming the

use of healthcare resources. The login pass-phrase

security of the system also prevents unauthorized

users from changing appointment schedules. Patients

can easily scan through the available doctors based

on their specialty and availability; thus, the selection

process becomes easier. With this automated

scheduling feature, booking errors get minimized

such as double appointments and scheduling

conflicts. Similarly, the real-time database integration

facilitates the storage and retrieval of the patient

information seamlessly, ensuring that the medical

records are always accessible for referenc... Among

the most remarkable findings is how well the AI

powered chatbot helps patients navigate their way

through the appointment process. It has been well

received for its ability to assess symptoms and

recommend if an appointment is necessary. With the

chatbot, patients receive quick answers to their

medical queries instead of waiting for the human to

respond, and they feel more confident in their

healthcare decisions. Additionally, it is a 24/7 system

that allows patients to schedule an appointment

whenever it is convenient for them thus improving

accessibility. Evaluation of the performance of the

system was based on speed, accuracy and

satisfaction of users. The findings show that this AI-

facilitated booking experience is significantly faster

than traditional means, reducing appointment

booking times to less than half. Doctor availability

and slot allocation had also notable high accuracy

checking to reduce mistakes.

REFERENCES

A. Odeh, R. Abdelhadi and H. Odeh, "Medical patient

appointments management using smart software system

in UAE," 2019 International Arab Conference on

Information Technology (ACIT), Al Ain, United Arab

Emirates, 2019, pp. 97-101.

A. M. Fernandez, et al., "Telemedicine and AI: A new era

in remote healthcare," International Journal of

Telehealth, vol. 9, no. 1, pp. 55-72, 2021.

Arthur Hylton III and Suresh Sankaranarayanan

“Application of Intelligent Agent in hospital

Appointment Scheduling System”, International

Journal of Advanced Trends in Computer Science and

Engineering, august 2013.

Brown, B., et al. (2021). AI-driven appointment scheduling:

A pilot study. Healthcare Management Forum, 34(2),

90-98.

Davis, C., & Wilson, D. (2022). Intelligent matching of

patients and providers using machine learning. Journal

of Healthcare Informatics, 15(1), 25-38.

Deepti Ameta, Kalpana Mudaliar and Palak Patel

“Medication Reminder and Healthcare – An Android

Application”, International Journal of Managing Public

Sector Information and Communication Technologies

(IJMPICT) Vol. 6, June 2015, pp. 39- 48.

F. Anjum, A. S. M. Shoaib, A. I. Hossain and M. M. Khan,

"Online health care," 2018 IEEE 8th Annual

Computing and Communication Workshop and

Conference (CWC), Las Vegas, NV, 2018

Garcia, E., et al. (2023). Evaluating the effectiveness of a

chatbot for patient support. *Telemedicine and e-Health

J. T. Lee, et al., "AI-based symptom analysis and

preliminary diagnosis systems: A review," Journal of

Healthcare Informatics, vol. 27, no. 2, pp. 89-105,

2021.

Jagannath Aghav, Smita Sonawane, and Himanshu

Bhambhlani “Health Track: Health Monitoring and

Prognosis System using Wearable Sensors”, IEEE

International Conference on Advances in Engineering

& Technology Research 2014, pp. 1-5.

Kim, J., & Gomez, R. (2024). The role of AI in personalized

medicine: Opportunities and challenges. Precision

Medicine Journal, 12(4), 210-225.

L. N. Thompson, "Data security challenges in AI-powered

healthcare applications," Journal of Cybersecurity in

Medicine, vol. 14, no. 5, pp. 210-225, 2023.

P. D. Patel and M. S. Agarwal, "Reducing patient wait times

through AI-powered hospital management," IEEE

Transactions on Medical Systems, vol. 18, no. 4, pp.

340-355, 2023.

Patel, R., & Nguyen, T. (2022). Ethical considerations in

AI-driven healthcare: A review. AI & Society, 37(4),

789-805.

R. K. Smith, et al., "Optimizing healthcare appointment

scheduling using AI-driven models," International

Journal of Medical Informatics, vol. 15, no. 3, pp. 120-

134, 2022.

Rashmi A. Nimbalkar and R.A. Fadnavis “Domain Specific

Search of Nearest Hospital and Healthcare

Management System”, Recent Advances in

Engineering and Computational Sciences (RAECS),

2014, pp.1-5.

AI-Driven Doctor Scheduling and Virtual Healthcare Companion

873

Roberts, M., et al. (2023). Ensuring data privacy and

security in AI healthcare applications. Journal of

Digital Health, 10(2), 55-70.

S. Gavaskar, A. Sumithra, A. Saranya “Health Portal-An

Android Smarter Healthcare Application”,

International Journal of Research in Engineering and

Technology, Sep-2013.

S. Gupta and R. K. Sharma, "The future of AI in patient-

centric healthcare management," International Journal

of Artificial Intelligence in Medicine, vol. 12, no. 3, pp.

178-190, 2022.

Singh, A., & Lee, K. (2023). Transparency and

accountability in AI-driven medical decision-making.

Journal of AI Ethics, 5(1), 30-45.

Smith, J., & Jones, A. (2020). The impact of artificial

intelligence on healthcare administration. Journal of

Medical Systems, 44(1), 1-10.

Thompson, L., & Richards, M. (2023). Addressing bias in

AI healthcare systems: Challenges and solutions.

Health Informatics Journal, 29(3), 112-130.

White, P., et al. (2024). Mitigating algorithmic bias in

healthcare AI: A framework for fairness. Machine

Learning in Medicine, 18(1), 99-115.

Yeo Symey, Suresh Sankaranarayanan, Siti Nur afifah binti

Sait “Application of Smart Technologies for Mobile

Patient Appointment System”, International Journal of

Advanced Trends in Computer Science and

Engineering, august 2013.

ICRDICCT‘25 2025 - INTERNATIONAL CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION,

COMMUNICATION, AND COMPUTING TECHNOLOGIES

874