{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Welcome to the start of your adventure in Agentic AI" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", " \n", " \n", " \n", " \n", "
\n", " \n", " \n", "

Are you ready for action??

\n", " Have you completed all the setup steps in the setup folder?
\n", " Have you read the README? Many common questions are answered here!
\n", " Have you checked out the guides in the guides folder?
\n", " Well in that case, you're ready!!\n", " \n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", " \n", " \n", " \n", " \n", "
\n", " \n", " \n", "

This code is a live resource - keep an eye out for my updates

\n", " I push updates regularly. As people ask questions or have problems, I add more examples and improve explanations. As a result, the code below might not be identical to the videos, as I've added more steps and better comments. Consider this like an interactive book that accompanies the lectures.

\n", " I try to send emails regularly with important updates related to the course. You can find this in the 'Announcements' section of Udemy in the left sidebar. You can also choose to receive my emails via your Notification Settings in Udemy. I'm respectful of your inbox and always try to add value with my emails!\n", " \n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### And please do remember to contact me if I can help\n", "\n", "And I love to connect: https://www.linkedin.com/in/eddonner/\n", "\n", "\n", "### New to Notebooks like this one? Head over to the guides folder!\n", "\n", "Just to check you've already added the Python and Jupyter extensions to Cursor, if not already installed:\n", "- Open extensions (View >> extensions)\n", "- Search for python, and when the results show, click on the ms-python one, and Install it if not already installed\n", "- Search for jupyter, and when the results show, click on the Microsoft one, and Install it if not already installed \n", "Then View >> Explorer to bring back the File Explorer.\n", "\n", "And then:\n", "1. Click where it says \"Select Kernel\" near the top right, and select the option called `.venv (Python 3.12.9)` or similar, which should be the first choice or the most prominent choice. You may need to choose \"Python Environments\" first.\n", "2. Click in each \"cell\" below, starting with the cell immediately below this text, and press Shift+Enter to run\n", "3. Enjoy!\n", "\n", "After you click \"Select Kernel\", if there is no option like `.venv (Python 3.12.9)` then please do the following: \n", "1. On Mac: From the Cursor menu, choose Settings >> VS Code Settings (NOTE: be sure to select `VSCode Settings` not `Cursor Settings`); \n", "On Windows PC: From the File menu, choose Preferences >> VS Code Settings(NOTE: be sure to select `VSCode Settings` not `Cursor Settings`) \n", "2. In the Settings search bar, type \"venv\" \n", "3. In the field \"Path to folder with a list of Virtual Environments\" put the path to the project root, like C:\\Users\\username\\projects\\agents (on a Windows PC) or /Users/username/projects/agents (on Mac or Linux). \n", "And then try again.\n", "\n", "Having problems with missing Python versions in that list? Have you ever used Anaconda before? It might be interferring. Quit Cursor, bring up a new command line, and make sure that your Anaconda environment is deactivated: \n", "`conda deactivate` \n", "And if you still have any problems with conda and python versions, it's possible that you will need to run this too: \n", "`conda config --set auto_activate_base false` \n", "and then from within the Agents directory, you should be able to run `uv python list` and see the Python 3.12 version." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# First let's do an import. If you get an Import Error, double check that your Kernel is correct..\n", "\n", "from dotenv import load_dotenv\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Next it's time to load the API keys into environment variables\n", "# If this returns false, see the next cell!\n", "\n", "load_dotenv(override=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Wait, did that just output `False`??\n", "\n", "If so, the most common reason is that you didn't save your `.env` file after adding the key! Be sure to have saved.\n", "\n", "Also, make sure the `.env` file is named precisely `.env` and is in the project root directory (`agents`)\n", "\n", "By the way, your `.env` file should have a stop symbol next to it in Cursor on the left, and that's actually a good thing: that's Cursor saying to you, \"hey, I realize this is a file filled with secret information, and I'm not going to send it to an external AI to suggest changes, because your keys should not be shown to anyone else.\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", " \n", " \n", " \n", " \n", "
\n", " \n", " \n", "

Final reminders

\n", " 1. If you're not confident about Environment Variables or Web Endpoints / APIs, please read Topics 3 and 5 in this technical foundations guide.
\n", " 2. If you want to use AIs other than OpenAI, like Gemini, DeepSeek or Ollama (free), please see the first section in this AI APIs guide.
\n", " 3. If you ever get a Name Error in Python, you can always fix it immediately; see the last section of this Python Foundations guide and follow both tutorials and exercises.
\n", " \n", "
" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "OpenAI API Key exists and begins sk-proj-\n" ] } ], "source": [ "# Check the key - if you're not using OpenAI, check whichever key you're using! Ollama doesn't need a key.\n", "\n", "import os\n", "openai_api_key = os.getenv('OPENAI_API_KEY')\n", "\n", "if openai_api_key:\n", " print(f\"OpenAI API Key exists and begins {openai_api_key[:8]}\")\n", "else:\n", " print(\"OpenAI API Key not set - please head to the troubleshooting guide in the setup folder\")\n", " \n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# And now - the all important import statement\n", "# If you get an import error - head over to troubleshooting in the Setup folder\n", "\n", "from openai import OpenAI" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "# And now we'll create an instance of the OpenAI class\n", "# If you're not sure what it means to create an instance of a class - head over to the guides folder (guide 6)!\n", "# If you get a NameError - head over to the guides folder (guide 6)to learn about NameErrors - always instantly fixable\n", "# If you're not using OpenAI, you just need to slightly modify this - precise instructions are in the AI APIs guide (guide 9)\n", "\n", "openai = OpenAI()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "# Create a list of messages in the familiar OpenAI format\n", "\n", "messages = [{\"role\": \"user\", \"content\": \"what is 19 * 22 * 0?\"}]" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Any number multiplied by zero equals zero. Therefore, 19 * 22 * 0 = 0.\n" ] } ], "source": [ "# And now call it! Any problems, head to the troubleshooting guide\n", "# This uses GPT 4.1 nano, the incredibly cheap model\n", "# The APIs guide (guide 9) has exact instructions for using even cheaper or free alternatives to OpenAI\n", "# If you get a NameError, head to the guides folder (guide 6) to learn about NameErrors - always instantly fixable\n", "\n", "response = openai.chat.completions.create(\n", " model=\"gpt-4.1-nano\",\n", " messages=messages\n", ")\n", "\n", "print(response.choices[0].message.content)\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "# And now - let's ask for a question:\n", "\n", "question = \"Please propose a hard, challenging question to assess someone's IQ. Respond only with the question.\"\n", "messages = [{\"role\": \"user\", \"content\": question}]\n" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "If two typists can type two pages in two minutes, how many typists will it take to type 18 pages in six minutes?\n" ] } ], "source": [ "# ask it - this uses GPT 4.1 mini, still cheap but more powerful than nano\n", "\n", "response = openai.chat.completions.create(\n", " model=\"gpt-4.1-mini\",\n", " messages=messages\n", ")\n", "\n", "question = response.choices[0].message.content\n", "\n", "print(question)\n" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "# form a new messages list\n", "messages = [{\"role\": \"user\", \"content\": question}]\n" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Let's analyze the problem step-by-step.\n", "\n", "**Given:**\n", "- 2 typists can type 2 pages in 2 minutes.\n", "\n", "**Find:**\n", "- How many typists are needed to type 18 pages in 6 minutes?\n", "\n", "---\n", "\n", "### Step 1: Find the rate of work per typist\n", "\n", "If 2 typists can type 2 pages in 2 minutes, then:\n", "\n", "- Total pages typed by 2 typists in 2 minutes: 2 pages\n", "- So, pages typed by 1 typist in 2 minutes: \\(\\frac{2 \\text{ pages}}{2} = 1 \\text{ page}\\)\n", "- Therefore, 1 typist types 1 page in 2 minutes.\n", "\n", "From this, the typing rate of 1 typist is:\n", "\n", "\\[\n", "\\frac{1 \\text{ page}}{2 \\text{ minutes}} = \\frac{1}{2} \\text{ pages per minute}\n", "\\]\n", "\n", "---\n", "\n", "### Step 2: Use this rate to find how many typists are needed for 18 pages in 6 minutes\n", "\n", "Suppose the number of typists needed is \\(x\\).\n", "\n", "- Total pages needed: 18\n", "- Total time available: 6 minutes\n", "- Pages per minute per typist: \\(\\frac{1}{2}\\)\n", "- Total pages typed by \\(x\\) typists in 6 minutes: \n", "\n", "\\[\n", "x \\times \\frac{1}{2} \\times 6 = 3x \\quad \\text{pages}\n", "\\]\n", "\n", "We need this to be equal to 18 pages:\n", "\n", "\\[\n", "3x = 18\n", "\\]\n", "\n", "Solving for \\(x\\):\n", "\n", "\\[\n", "x = \\frac{18}{3} = 6\n", "\\]\n", "\n", "---\n", "\n", "### **Answer:**\n", "\n", "\\[\n", "\\boxed{6}\n", "\\]\n", "\n", "It will take 6 typists to type 18 pages in 6 minutes.\n" ] } ], "source": [ "# Ask it again\n", "\n", "response = openai.chat.completions.create(\n", " model=\"gpt-4.1-mini\",\n", " messages=messages\n", ")\n", "\n", "answer = response.choices[0].message.content\n", "print(answer)\n" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/markdown": [ "Let's analyze the problem step-by-step.\n", "\n", "**Given:**\n", "- 2 typists can type 2 pages in 2 minutes.\n", "\n", "**Find:**\n", "- How many typists are needed to type 18 pages in 6 minutes?\n", "\n", "---\n", "\n", "### Step 1: Find the rate of work per typist\n", "\n", "If 2 typists can type 2 pages in 2 minutes, then:\n", "\n", "- Total pages typed by 2 typists in 2 minutes: 2 pages\n", "- So, pages typed by 1 typist in 2 minutes: \\(\\frac{2 \\text{ pages}}{2} = 1 \\text{ page}\\)\n", "- Therefore, 1 typist types 1 page in 2 minutes.\n", "\n", "From this, the typing rate of 1 typist is:\n", "\n", "\\[\n", "\\frac{1 \\text{ page}}{2 \\text{ minutes}} = \\frac{1}{2} \\text{ pages per minute}\n", "\\]\n", "\n", "---\n", "\n", "### Step 2: Use this rate to find how many typists are needed for 18 pages in 6 minutes\n", "\n", "Suppose the number of typists needed is \\(x\\).\n", "\n", "- Total pages needed: 18\n", "- Total time available: 6 minutes\n", "- Pages per minute per typist: \\(\\frac{1}{2}\\)\n", "- Total pages typed by \\(x\\) typists in 6 minutes: \n", "\n", "\\[\n", "x \\times \\frac{1}{2} \\times 6 = 3x \\quad \\text{pages}\n", "\\]\n", "\n", "We need this to be equal to 18 pages:\n", "\n", "\\[\n", "3x = 18\n", "\\]\n", "\n", "Solving for \\(x\\):\n", "\n", "\\[\n", "x = \\frac{18}{3} = 6\n", "\\]\n", "\n", "---\n", "\n", "### **Answer:**\n", "\n", "\\[\n", "\\boxed{6}\n", "\\]\n", "\n", "It will take 6 typists to type 18 pages in 6 minutes." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from IPython.display import Markdown, display\n", "\n", "display(Markdown(answer))\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Congratulations!\n", "\n", "That was a small, simple step in the direction of Agentic AI, with your new environment!\n", "\n", "Next time things get more interesting..." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", " \n", " \n", " \n", " \n", "
\n", " \n", " \n", "

Exercise

\n", " Now try this commercial application:
\n", " First ask the LLM to pick a business area that might be worth exploring for an Agentic AI opportunity.
\n", " Then ask the LLM to present a pain-point in that industry - something challenging that might be ripe for an Agentic solution.
\n", " Finally have 3 third LLM call propose the Agentic AI solution.
\n", " We will cover this at up-coming labs, so don't worry if you're unsure.. just give it a try!\n", " \n", "
" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Certainly! One promising business area for an agentic AI opportunity is **Personalized Healthcare Management**.\n", "\n", "### Why Personalized Healthcare Management?\n", "\n", "- **Complex Decision-Making:** Managing chronic illnesses, medication schedules, diet, exercise, and mental health requires complex, ongoing decisions that vary by individual.\n", "- **Data-Driven:** There's abundant personal health data (wearables, medical records, lifestyle inputs) that an AI can utilize.\n", "- **High Impact:** Improved health outcomes and reduced healthcare costs are strong motivators for adoption.\n", "- **Agentic AI Role:** An agentic AI could proactively monitor patient data, identify health risks in real time, suggest lifestyle adjustments, schedule appointments, and even communicate with healthcare providers autonomously—acting as a personal health assistant.\n", "\n", "### Potential Features of an Agentic AI in this Space\n", "\n", "- **Continuous Monitoring:** Analyze inputs from devices and self-reports to detect anomalies or patterns.\n", "- **Personalized Recommendations:** Suggest actionable insights tailored to the user’s current conditions and lifestyle.\n", "- **Autonomous Scheduling:** Arrange doctor visits, lab tests, and medication refills.\n", "- **Behavioral Nudges:** Encourage adherence to treatment plans through timely reminders and motivational prompts.\n", "- **Crisis Response:** Detect emergencies (e.g., heart irregularities) and autonomously alert medical services or caretakers.\n", "\n", "### Why Agentic AI?\n", "\n", "Unlike reactive systems, an agentic AI can take initiative—it can plan, act, and adapt based on evolving health data, without needing explicit instructions at every step. This autonomy can greatly enhance user engagement and health outcomes.\n", "\n", "---\n", "\n", "If you'd like, I can help brainstorm specific product ideas or market strategies within this domain!\n" ] } ], "source": [ "# First create the messages:\n", "\n", "messages = [{\"role\": \"user\", \"content\": \"can you pick a business area that might be worth exploring for an agentic Ai opportunity\"}]\n", "\n", "# Then make the first call:\n", "\n", "response = openai.chat.completions.create(\n", " model=\"gpt-4.1-mini\",\n", " messages=messages\n", ")\n", "\n", "# Then read the business idea:\n", "\n", "business_idea = response.choices[0].message.content\n", "\n", "print(business_idea)\n", "\n", "# And repeat! In the next message, include the business idea within the message" ] }, { "cell_type": "markdown", "metadata": {}, "source": [] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "A significant pain point in the personal healthcare management industry is **patient adherence and engagement** with prescribed treatment plans and lifestyle recommendations. Many patients struggle to consistently follow medication schedules, attend follow-up appointments, or maintain lifestyle changes such as diet and exercise, which can lead to suboptimal health outcomes and increased healthcare costs.\n", "\n", "This challenge arises from factors like forgetfulness, lack of motivation, confusion about instructions, and insufficient personalized support. Traditional interventions—like reminder calls or generic educational materials—often fail to address the nuanced and dynamic nature of individual patient needs.\n", "\n", "### How Agentic AI Can Address This Pain Point\n", "\n", "**Agentic AI**, with its ability to act autonomously, understand context, and interact proactively, can revolutionize patient adherence by offering personalized, adaptive, and continuous support:\n", "\n", "1. **Personalized Interaction:** An agentic AI can engage patients via conversation, tailoring communication style, frequency, and content to match their preferences, health literacy, and emotional state.\n", "\n", "2. **Proactive Reminders & Monitoring:** Beyond static reminders, the AI can sense when a patient may be at risk of non-adherence (e.g., missed doses, declining engagement) and intervene with timely prompts, motivational messages, or even escalate to healthcare providers when necessary.\n", "\n", "3. **Dynamic Care Plan Adaptation:** Based on patient feedback and real-world data (e.g., biometrics, activity levels), the AI can suggest adjustments or clarify instructions to improve understanding and feasibility.\n", "\n", "4. **Emotional and Social Support:** The AI can provide encouragement, address concerns or misconceptions, and simulate empathetic interactions that bolster motivation.\n", "\n", "5. **Integration with Healthcare Systems:** Acting autonomously, the AI agent can update healthcare providers with adherence data and patient status, enabling timely clinical decisions.\n", "\n", "### Summary\n", "\n", "**Pain Point:** Low patient adherence and engagement with personal health management.\n", "\n", "**Solution via Agentic AI:** Autonomous, context-aware AI agents that provide personalized, proactive, and adaptive support to patients, improving adherence rates, health outcomes, and reducing provider burden.\n", "\n", "This type of solution is challenging because it requires sophisticated sensing, natural language understanding, empathy simulation, and data privacy safeguards, but advances in agentic AI make it increasingly feasible and promising.\n" ] } ], "source": [ "messages = [{\"role\": \"user\", \"content\": \"what is a painpoint in personal healthcare management industry that is challenging but can be fixed using agentic ai\"}]\n", "\n", "# Then make the first call:\n", "\n", "response = openai.chat.completions.create(\n", " model=\"gpt-4.1-mini\",\n", " messages=messages\n", ")\n", "\n", "# Then read the business idea:\n", "\n", "business_idea = response.choices[0].message.content\n", "\n", "print(business_idea)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "To address the crucial painpoint of **low patient adherence and engagement** in personal healthcare management, I propose an **Agentic AI-powered Personalized Health Engagement Assistant**. This solution leverages agentic AI capabilities—autonomous, proactive, and context-aware decision-making—to act as a personalized, intelligent health companion that continuously motivates, supports, and adapts to individual patient needs and behaviors.\n", "\n", "---\n", "\n", "### Proposed Agentic AI Solution: Personalized Health Engagement Assistant\n", "\n", "#### Key Features:\n", "\n", "1. **Context-Aware Personalization**\n", " - The agent learns individual patient routines, preferences, health goals, and barriers.\n", " - Uses multimodal data (wearables, health records, behavioral patterns) to understand context.\n", " - Dynamically tailors recommendations, reminders, and motivational prompts to the patient’s lifestyle and emotional state.\n", "\n", "2. **Proactive and Adaptive Reminders**\n", " - Sends timely medication reminders, appointment alerts, and health activity nudges.\n", " - Adapts communication channels and messaging tone based on patient responsiveness (e.g., text, voice, app notifications).\n", " - Can reschedule and reprioritize tasks autonomously when conflicts or missed actions are detected.\n", "\n", "3. **Behavioral Coaching & Motivational Support**\n", " - Employs cognitive behavioral techniques and positive reinforcement to encourage healthy behaviors.\n", " - Provides instant feedback and rewards for adherence (gamification elements).\n", " - Detects signs of disengagement or health deterioration and escalates with personalized interventions or alerts to caregivers/providers.\n", "\n", "4. **Continuous Engagement Through Conversational AI**\n", " - Engages patients via natural language conversations, answering questions, offering health tips, and empathizing with struggles.\n", " - Enables two-way interaction so patients can express concerns or update their health status.\n", " - Integrates with smart home devices and wearables enhancing engagement through ambient reminders.\n", "\n", "5. **Data-Driven Insights and Reporting**\n", " - Tracks adherence trends, identifies risk factors for non-adherence.\n", " - Shares actionable insights with healthcare providers to inform care plans.\n", " - Respects privacy and ensures compliance with health data regulations (HIPAA, GDPR).\n", "\n", "---\n", "\n", "### Why Agentic AI?\n", "\n", "- **Autonomy:** The agent independently manages scheduling, messaging, and engagement strategies without constant manual input.\n", "- **Adaptability:** Learns from ongoing patient interactions and health outcomes to improve its support over time.\n", "- **Proactiveness:** Anticipates potential adherence challenges and intervenes early, rather than passively waiting.\n", "- **Human-like Engagement:** Conversational and empathetic interactions improve patient trust and willingness to adhere.\n", "\n", "---\n", "\n", "### Potential Impact:\n", "\n", "- Increased medication and lifestyle adherence rates.\n", "- Enhanced patient satisfaction and empowerment in health management.\n", "- Reduced complications and hospital readmissions.\n", "- Better patient-provider communication and personalized care.\n", "\n", "---\n", "\n", "If you’d like, I can also outline a tech stack, implementation plan, or discuss integration strategies with existing healthcare ecosystems!\n" ] } ], "source": [ "messages = [{\"role\": \"user\", \"content\": \"what agentic ai solution do you propose for a crucial painpoint in personal healthcare management industry which is Low patient adherence and engagement with personal health management\"}]\n", "\n", "# Then make the first call:\n", "\n", "response = openai.chat.completions.create(\n", " model=\"gpt-4.1-mini\",\n", " messages=messages\n", ")\n", "\n", "# Then read the business idea:\n", "\n", "business_idea = response.choices[0].message.content\n", "\n", "print(business_idea)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/markdown": [ "To address the crucial painpoint of **low patient adherence and engagement** in personal healthcare management, I propose an **Agentic AI-powered Personalized Health Engagement Assistant**. This solution leverages agentic AI capabilities—autonomous, proactive, and context-aware decision-making—to act as a personalized, intelligent health companion that continuously motivates, supports, and adapts to individual patient needs and behaviors.\n", "\n", "---\n", "\n", "### Proposed Agentic AI Solution: Personalized Health Engagement Assistant\n", "\n", "#### Key Features:\n", "\n", "1. **Context-Aware Personalization**\n", " - The agent learns individual patient routines, preferences, health goals, and barriers.\n", " - Uses multimodal data (wearables, health records, behavioral patterns) to understand context.\n", " - Dynamically tailors recommendations, reminders, and motivational prompts to the patient’s lifestyle and emotional state.\n", "\n", "2. **Proactive and Adaptive Reminders**\n", " - Sends timely medication reminders, appointment alerts, and health activity nudges.\n", " - Adapts communication channels and messaging tone based on patient responsiveness (e.g., text, voice, app notifications).\n", " - Can reschedule and reprioritize tasks autonomously when conflicts or missed actions are detected.\n", "\n", "3. **Behavioral Coaching & Motivational Support**\n", " - Employs cognitive behavioral techniques and positive reinforcement to encourage healthy behaviors.\n", " - Provides instant feedback and rewards for adherence (gamification elements).\n", " - Detects signs of disengagement or health deterioration and escalates with personalized interventions or alerts to caregivers/providers.\n", "\n", "4. **Continuous Engagement Through Conversational AI**\n", " - Engages patients via natural language conversations, answering questions, offering health tips, and empathizing with struggles.\n", " - Enables two-way interaction so patients can express concerns or update their health status.\n", " - Integrates with smart home devices and wearables enhancing engagement through ambient reminders.\n", "\n", "5. **Data-Driven Insights and Reporting**\n", " - Tracks adherence trends, identifies risk factors for non-adherence.\n", " - Shares actionable insights with healthcare providers to inform care plans.\n", " - Respects privacy and ensures compliance with health data regulations (HIPAA, GDPR).\n", "\n", "---\n", "\n", "### Why Agentic AI?\n", "\n", "- **Autonomy:** The agent independently manages scheduling, messaging, and engagement strategies without constant manual input.\n", "- **Adaptability:** Learns from ongoing patient interactions and health outcomes to improve its support over time.\n", "- **Proactiveness:** Anticipates potential adherence challenges and intervenes early, rather than passively waiting.\n", "- **Human-like Engagement:** Conversational and empathetic interactions improve patient trust and willingness to adhere.\n", "\n", "---\n", "\n", "### Potential Impact:\n", "\n", "- Increased medication and lifestyle adherence rates.\n", "- Enhanced patient satisfaction and empowerment in health management.\n", "- Reduced complications and hospital readmissions.\n", "- Better patient-provider communication and personalized care.\n", "\n", "---\n", "\n", "If you’d like, I can also outline a tech stack, implementation plan, or discuss integration strategies with existing healthcare ecosystems!" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from IPython.display import Markdown, display\n", "\n", "display(Markdown(business_idea))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": ".venv", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.10" } }, "nbformat": 4, "nbformat_minor": 2 }