Advanced AI Workshop for Doctors: Evidence Review and Reliable Workflows
Updated: 3 days ago
Quick answer
Updated 20 October 2026. For healthcare and pharma teams, Parikshit Khanna runs hands-on ChatGPT, Claude, Microsoft 365 Copilot and Gemini training for non-clinical work, onsite across India or live online. Patient data never goes into unapproved tools, and clinical decisions always stay with qualified clinicians. See the full programme page: AI training for healthcare and pharma teams. Trainer-reported reach: 3,50,000+ professionals since 2020.
Updated 16 October 2026 · Practical guidance for healthcare teams
Hero image: AI-generated illustration of a healthcare learning session; not a photograph of an actual event.
If you already use an AI assistant, the next useful step may be better checking rather than more tools. You may want to compare several supplied papers, maintain a reusable research instruction or understand why the same question produces different answers. This guide outlines advanced learning priorities to discuss with Parikshit Khanna. Here, advanced means making the workflow clearer, easier to inspect and easier for a colleague to repeat. It does not mean handing diagnosis or treatment decisions to a general chatbot. Bring a task that you understand and an example that went wrong. Working through the cause of that error can be more valuable than another impressive demonstration.
Start here
You do not need a technical background. Choose one small task, use fictional or approved public information, and check the result yourself. This guide covers a practical example, tool costs, and how guided training can help.
Make evidence and uncertainty visible
Start by defining exactly what the assistant may use. A bounded comparison of supplied documents is easier to review than an open request for the latest evidence on a broad subject. Agree the question, included sources and extraction fields. Ask the tool to mark absent information rather than fill gaps from memory or assumption.
Build a review set containing straightforward examples and deliberate traps. Include a missing date, an ambiguous table heading or two sources with different populations. Check whether the workflow preserves those differences. Repeating the same method across several examples helps identify failure patterns that a single successful demonstration may conceal from the group.
Document the working instruction, source list, output format and reviewer responsibilities. Version the template when you change it and keep a corrected example. If a specialised clinical system is discussed, treat its validation, intended use and local approval as a separate evaluation. General AI training does not provide clinical certification or establish a product's suitability.
Test a workflow with fictional study summaries
Write three fictional study summaries with different populations, follow-up periods and limitations. Make one summary omit its sample size and clearly label all three as invented teaching material.
Ask the assistant for an extraction table that quotes each supplied sample size, identifies missing information and keeps the populations separate, without drawing a treatment conclusion.
Check every table cell against the summaries. Add one correction to your reusable instruction and rerun the same test to see whether the problem persists.
Check each extracted claim against its source and keep conflicting findings separate. Record omissions and unsupported additions even when the answer sounds plausible. A reliable research workflow makes uncertainty visible and gives a qualified reviewer enough information to challenge the draft before use.
AI and generative AI, in ordinary words
AI is the broad name for computer tools that help with tasks such as finding patterns, sorting information or making predictions. Generative AI is one kind that creates a draft of text, images or other content from an instruction. That instruction is often called a prompt. A workflow simply means the steps used to complete a task. A fluent answer can still contain mistakes.
A useful instruction says what you want, who will read it, which approved facts to use, and what must stay unchanged. For example: “Rewrite this fictional clinic notice in friendly, short sentences. Keep every date unchanged. Do not add medical advice. Flag anything unclear.”
What are Claude, ChatGPT and Microsoft Copilot?
Claude is Anthropic’s AI assistant. You can ask it to draft, simplify or organise information, then review its work.
ChatGPT is OpenAI’s AI assistant. Use the same careful approach: give clear instructions, check sources, and review the draft.
Microsoft Copilot is a family of AI assistants. Personal Copilot and workplace Copilot have different access and controls. Ask IT which work account and licence your organisation has approved. Dragon Copilot is a separate healthcare product; ordinary Copilot is not a clinical system.
Free or paid: what should you choose?
Start with a free option and fictional examples. Pay only when a specific limit or feature is stopping useful work. A personal paid subscription does not automatically permit patient information. Check account controls, organisational approval and total cost before buying.
Claude reference prices checked 16 October 2026: Free is US$0 with limits; Pro is US$20 a month or US$200 billed yearly. Team Standard is US$20 per person per month billed yearly, or US$25 monthly, for 2–150 people. Taxes and local checkout terms may differ. More expensive plans are available; beginners do not need to start there.
Microsoft Copilot has a free personal version. Copilot Chat is included with eligible Microsoft 365 work plans; some agents can add usage charges. The India business page lists a first-year offer for existing eligible Microsoft 365 Business customers: ₹1,495.73 per user per month paid yearly, versus regular ₹1,745. An annual commitment is required. The qualifying base licence costs extra, and GST is extra. The offer runs 1 July–31 December 2026. Confirm renewal terms before buying.
ChatGPT has free and paid plans. Check the official plan information and your local checkout before paying; features and limits can change. Tool subscriptions are separate from workshop fees.
Draft with AI. Check it yourself. Share only after approval.
Protect patient information and keep a person responsible
For practice, leave out real names, reports, prescriptions, phone numbers, appointment lists and other identifying details. Use invented information. Before any patient information is used, your organisation should approve the tool, account, contracts, settings and workflow. Removing a name alone may not remove every identifying detail.
WHO warns that generated answers may be false, incomplete or biased. Read the original source, check dates and numbers, and have the appropriate professional approve the final text. General chatbots should not make diagnosis, treatment, dosage, triage or urgent-care decisions.
Follow AI news without feeling left behind
You do not need every new model or subscription. Pick one useful task and practise it for a week. Set aside a short weekly news-reading slot. For each announcement, ask: Is it available here? Is our organisation allowed to use it? Does it solve a task we actually have?
Two developments worth understanding: Anthropic announced Claude for Healthcare on 11 January 2026; Microsoft described Dragon Copilot expansion on 5 March 2026. These are vendor announcements with market, licence and feature limits, not proof that a general chatbot can deliver better patient outcomes.
How Parikshit Khanna’s training can help
Parikshit Khanna is an AI trainer and Founder of Digital Training Jet. A guided session gives your team time to practise clear instructions, spot mistakes, check sources and agree who approves the final work. Ask for examples based on your department’s everyday tasks and time for beginner questions.
A participant account published by OncoDaily describes learning from Parikshit at a ChatGPT and AI tools workshop for healthcare professionals held at IIT Delhi. This describes a workshop experience; it does not claim institutional endorsement or clinical certification.
AI-generated illustrative portrait based on supplied references.
Plan a session around your team’s work
Share your team’s roles, group size, preferred language, two or three repetitive tasks, approved tools and possible dates. Ask for a proposal covering practice time, take-home examples, review responsibilities and follow-up support. Workshop fees, delivery format and travel are confirmed in the proposal. Results should be measured in your own setting.
Common questions
Do I need coding skills? No. Begin with a clearly described task and a short draft you can check.
Will AI replace professional judgement? Your team remains responsible for the final output. Training focuses on careful use and review.
Should everyone buy a paid account? Start with the tools your organisation already approves. Buy additional access only for a demonstrated need.
Book healthcare and pharma AI training: contact Parikshit Khanna
Send your organisation name, roles, headcount, location, approved AI tools, preferred dates and two priority tasks. You will get a written proposal with scope, dates and fees. Please share anonymised examples only.
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