Doctor Recommendation through AI

Requirements to Design an AI App

The system must understand what diseases patient have
The system must tell appropriate medicine or treatment to a patient based on the precept of diagnostics from various questions through soft-bot.
The system must set an Alarm for the patient to take medicine on time.

How to measure the authority degree of a doctor?
How to mine patient-doctor relationships from the network and further measure doctors’ real authorities would become one of the main challenges in this work?
How to make individual doctor recommendations according to patients’ preferences information and doctors’ AD-CDs since traditional information retrieval models, such as Boolean Model and Vector Space Model, all compute similarity degrees between query keywords and destination doctors?
How to evaluate the recommendation accuracies of our method because it is hard to obtain solid results via evaluating AD-CDs for traditional methods. Besides, these methods themselves just are subjective processes?
User privacy.
Resistant to cyber-attacks.
Voice Recognition.
Text Formatting.
Understand symptoms of related problems.
Bias and inequality.
Machine learning (Dataset shift).
Accidentally fitting confounders versus true signal.
Blind spots in machine learning can reflect the worst societal biases.
Susceptibility to adversarial attack or manipulation.
Logistical difficulties.
Achieving robust regulation and rigorous quality control.

A time-constraint probability factor graph model (TPFG)[3] to mine doctor-patient relationships.
Formalize four network features considering doctor recommendation requirements and Compute them for ranking nodes.
Novel hybrid multi-layer architecture.
Doctor recommendation model (namely RWR-Model) via the random walk with restart method (RWR), and evaluate the recommendation precision according to an information retrieval index.

This (AI) technology used in medical contexts promises to ease the burden on medical professionals.
AI improves the provider’s ability to diagnose consistently and accurately.
Better organization of patient pathways.
medication management.
instant help in emergency situations or first aid,
offering solutions for simpler medical issues.
Medical chatbots have emerged as useful tools to provide additional information to patients.
Medical chatbots can provide initial assistance over one-on-one conversations.
AI-powered chatbots can attend to queries from patients round-the-clock.
AI chatbot better using Machine Learning (ML). This enables the chatbot to provide more relevant information to the patients.
Medical chatbots can gather feedback from patients about the websites of healthcare organizations.
Health chatbots can remind patients about their medicines, moreover, these chatbots can monitor their health parameters.
Medical chatbots can set up appointments with doctors, furthermore, they can find the nearest pharmacy or doctor‘s office.
Availability and ongoing health monitoring.
Providing information fast when there is not a moment to lose.
Gaining the trust of patients.

The aim to help ease the life of those involved in the fight against cancer with the information they need.
The app empowers them by providing a comprehensive list of diets, exercises, and post-cancer practices
Curated by Integrative Medicine experts, so that they don’t need to constantly rely on a doctor.

The app features personalized meditations as well as the ability to track mood and monitor emotional health.
As users communicate with the chatbot, it will learn more about them and fine-tune the experience in order to fit their needs.

Safedrugbot is a chat messaging service that offers assistant-like support to health professionals.
Doctors who need appropriate data about the use of drugs during breastfeeding.
It provides information about the active ingredients present in the medication and alternative medicines.

Users report the symptoms of their illness to the app, which checks them against a database of diseases using speech recognition, and then offers an appropriate course of action.
AI doctors listen and look carefully to diagnose the patient and then write prescriptions or refer to a specialist if required.

Florence can track the user’s health, for example, body weight, mood, or period, thus helping them to reach their goals.
The chatbot also has the skills to find the nearest pharmacy or doctor’s office in case you need it.

It is basically a symptom checker powered by artificial intelligence.
It’s an excellent source of online medical service providers, no matter whether you are looking for pharmacies, test centers, doctors’ offices.

Assess the user’s health based on the indicated symptoms using its vast, A.I.-based database.

Based on the gathered data as well as the information fed to its smart algorithm
Sensely interprets the user’s symptoms and recommends a diagnosis.
It offers a local service discovery and rich resources for self-care.

The chatbot thoroughly asks you about the details of your medical state and offers you various solutions and actionable steps to take.

Infermedica leverages machine learning technology to power the symptom-checker chatbot, Symptomatic.
It assesses the user’s health status and based on the symptoms
It sets up a possible diagnosis and gives actionable recommendations.

It is a helpful resource for cancer patients, caregivers, friends, and family on Facebook Messenger
It provides resources for caregivers to ease the burden of caring and making their lives easier.
It offers friends and families advice on what to say and how to help cancer patients best.
It’s a sophisticated and well-thought-out solution.

1. Creation of User Profile

This model collects details to create a new user authentication into the Firebase authentication console.
The UI is made interactive to get the user details like email, personal username, and password this, in turn, generates a User id connecting the user to the firebase console.

2. Setup User Profile

This section takes personal details as input form the user related to their age, gender, and if any preexisting medical problem.
All the option is listed in the form of a checkbox

3. Setup Medical Records

4. Application Setup Complete

Once all the data is entered, they are saved in real-time for each user separately according to the user id generated these helps in rendering these data repeatedly when used by the tensor flow custom model.

5. Symptom analysis