AI_for_Health_Professional_Workshop_

AI in Healthcare:
Unleashing the Power in a
One-Day Workshop
Empowering Healthcare Professionals to
Leverage AI in Practice
7 September 2023
Vaikunthan Rajaratnam

– I am not an AI expert,
– nor do I possess coding knowledge specific to the
underlying mechanisms of AI models;
– my expertise lies in the utilisation of these models, such
as ChatGPT,
– based on my extensive experience as a user within the
fields of healthcare, medical education, and related
research, rather than their technical development or
underlying algorithms.
Disclaimer

Platforms Covered
• - Elicit.org
• - Scholarcy.com
• - Typeset.io
• - ChatGPT
• - Botpress.com
• - InVideo.io
• - Night Cafe
• - Genie.io

Introduction to AI in
Healthcare:
Opportunities and
Challenges

Understanding AI, Generative AI, and ChatGPT
• AI (Artificial Intelligence)
– refers to the simulation of human intelligence in machines that are
programmed to think, learn, and make decisions
– Applications: Includes machine learning, natural language processing,
robotics, computer vision, etc.
• Generative AI
– subset of AI that focuses on creating new data instances that are
similar to a set of training examples.
– Techniques: Examples include Generative Adversarial Networks
(GANs), Variational Autoencoders (VAEs), etc.
• ChatGPT:
– State-of-the-art language models developed by OpenAI. It utilizes the
Transformer architecture to generate human-like text based on given
prompts.
– Usage: Widely used in natural language understanding tasks, chatbots,
content creation, and more.

• Rapid multi-disciplinary
stream of authors
researching AI in Medicine
• Skills and data quality
awareness for data-intensive
analysis
• Limitations
– Ethics,
– Data governance, and
– Competencies of the
health workforce.
• Focuses on
– Health services
management
– Predictive medicine
– Patient data and
diagnostics
– Clinical decision-making
Secinaro, S., Calandra, D., Secinaro, A., Muthurangu, V., & Biancone, P. (2021). The role of artificial intelligence in healthcare: A structured
literature review. BMC Medical Informatics and Decision Making, 21(1), 125. https://doi.org/10.1186/s12911-021-01488-9

Healthcare is an
ideal sector for AI
integration
• operational efficiency
• decision-making
• Improving patient
outcomes
• $200 - $360 billion
annually

Health
services
managemen
t
• Optimization of Operational Efficiency
– Example: Scheduling algorithms to optimize staff shifts and patient appointments, reducing wait times.
• Predictive Analytics for Resource Allocation
– Example: Predicting hospital bed occupancy based on patient flow and admission trends for better
resource planning.
• Supply Chain Optimization
– Example: Forecasting the need for medical supplies and automating procurement to reduce inventory
costs.
• Fraud Detection and Compliance
– Example: Detecting fraudulent billing activities and ensuring compliance with healthcare regulations.
• Integration of Care across Providers
– Example: Facilitating seamless information sharing among healthcare providers for coordinated care.
• Enhancing Administrative Decision-Making
– Example: Utilizing data analytics to inform strategic decisions, such as facility expansion or service
prioritization.
• Patient Engagement and Communication
– Example: AI-powered chatbots to handle routine inquiries, appointment scheduling, and patient follow-
ups.
• Workforce Development and Training
– Example: Using AI to identify training needs and deliver personalized learning paths for healthcare staff.
• Performance Monitoring and Quality Assurance
– Example: Implementing AI-driven analytics to monitor performance metrics, identify areas for
improvement, and ensure quality standards.
• Cost Control and Optimization
– Example: Applying AI to analyze cost drivers, identify inefficiencies, and recommend cost-saving
measures.

Predictiv
e
medicine
• Early Disease Detection
– Example: Using AI algorithms to analyze medical imaging for early detection of
cancers, even before symptoms appear.
• Risk Stratification
– Example: Identifying patients at high risk of chronic conditions like heart disease
based on a combination of genetic, lifestyle, and clinical data.
• Personalized Treatment Plans
– Example: Creating tailored treatment regimens by predicting individual responses
to specific drugs or therapies.
• Epidemic Outbreak Prediction
– Example: Analyzing social media, travel patterns, and other data sources to
predict the spread of infectious diseases like flu or COVID-19.
• Hospital Readmission Prediction
– Example: Determining the likelihood of a patient's readmission to the hospital,
allowing for targeted interventions to reduce readmissions.
• Drug Response Prediction
– Example: Predicting how individual patients will respond to certain medications,
minimizing adverse effects, and improving treatment efficacy.
• Genomic Medicine and Genetic Risk Prediction
– Example: Analyzing genetic data to predict susceptibility to genetic disorders and
guide preventive measures.
• Mental Health Outcome Prediction
– Example: Utilizing AI to predict mental health crises or progression of conditions
like depression based on patient behavior and medical history.
• Chronic Disease Management
– Example: Continuous monitoring and prediction of disease progression in chronic
conditions like diabetes, allowing for timely interventions.

Patient
data and
diagnostics
• Automated Data Analysis and Interpretation
– Example: Using AI to analyze complex laboratory results, such as genetic sequencing, to identify patterns and
anomalies.
• Real-Time Monitoring and Alerting
– Example: Continuously tracking vital signs and alerting medical staff to potential issues, such as deterioration in a
patient's condition.
• Enhanced Medical Imaging Interpretation
– Example: Applying AI algorithms to interpret radiological images, such as X-rays and MRIs, with increased accuracy
and speed.
• Predictive Analytics for Personalized Care
– Example: Analyzing patient data to predict individual responses to treatments, enabling more personalized and
effective care plans.
• Data Integration and Holistic Patient Views
– Example: Aggregating data from various sources (e.g., EMRs, wearables) to provide a comprehensive view of a
patient's health status.
• Telemedicine and Remote Diagnostics
– Example: Utilizing AI-powered tools to diagnose and manage patients in remote locations, increasing healthcare
accessibility.
• Natural Language Processing for Clinical Notes
– Example: Extracting valuable information from unstructured clinical notes through AI, enhancing data usability.
• Genomic and Precision Medicine
– Example: Integrating genomic data with clinical information to provide precise diagnoses and personalized treatment
recommendations.
• Chronic Condition Management and Monitoring
– Example: Using AI to diagnose and monitor chronic conditions, such as diabetes, through continuous data analysis.
• Ethical and Security Considerations in Data Handling
• Example: Implementing AI-driven security protocols to ensure patient data privacy and compliance with

Clinical
decision-
making
• Evidence-Based Recommendations
– Example: AI systems can analyze vast medical literature to provide
evidence-based treatment recommendations tailored to individual patient
profiles.
• Diagnostic Support Tools
– Example: AI algorithms can assist physicians in diagnosing complex
conditions by analyzing clinical data, medical imaging, and laboratory
results.
• Predicting Patient Outcomes
– Example: Using AI to predict patient responses to various treatments,
aiding in selecting the most effective therapy.
• Treatment Pathway Optimization
– Example: AI can suggest optimal treatment pathways based on patient
characteristics, medical history, and current clinical guidelines.
• Enhancing Multidisciplinary Collaboration
– Example: AI-driven platforms can facilitate collaboration among
specialists, integrating insights from various disciplines for comprehensive
care.
• Ethical Considerations in Decision Making
– Example: Implementing AI algorithms that consider ethical principles, such
as fairness and transparency, in clinical decisions.

Challenges
• Data
• Trust
• Ethics
• Readiness for change
• Expertise
• Buy-in
• Regulatory strategy
• Scalability
• Evaluation
Golhar, S. P., & Kekapure, S. S. (2022). Artificial Intelligence in Healthcare—A Review. International Journal of Scientific
Research in Science and Technology, 9(4), 381–387. https://doi.org/10.32628/IJSRST229454

Governance
Model for AI
S. Reddy, S. Allan, S. Coghlan, and P. Cooper, ‘A governance model for the application of AI in health care’, J. Am. Med. Inform. Assoc., vol. 27, no. 3, pp. 491–497,
Mar. 2020, doi: 10.1093/jamia/ocz192
Rahman, N., Thamotharampillai, T., & Rajaratnam, V. (2023). Ethics, guidelines, and policy for technology in healthcare. In Medical Equipment Engineering:
Design, manufacture and applications (pp. 119–147). IET Digital Library. https://doi.org/10.1049/PBHE054E_ch9

What is ChatGPT?
• Understanding Language
– Reads and comprehends human-written text.
• Generating Text
– Writes human-like text, from answers to creative content.
• Conversation
– Capable of engaging in text-based conversations with users.
• Applications
– Used in virtual assistants, education, content creation, and
more.
• Not a Human
– Generates text through algorithms, without feelings or
consciousness.
AI for Clinical Decision-Making and Patient Care

How Does
ChatGPT Work?
“Don’t cry ………..”
“ Don’t cry over….”
• Reading Text:
• Takes in words, questions, or sentences as input.
• Understands the language like a human reading a book.
• Processing Information:
• Breaks down the input into smaller parts to understand the meaning.
• Uses a complex mathematical model to analyse the text.
• Generating Response:
• Constructs a response based on what it has "learned" from reading lots of text.
• Tries to make the response sound like something a human would say.
• No Personal Knowledge or Opinions:
• Doesn't have thoughts, feelings, or personal experiences.
• Answers are based on patterns in the data it was trained on, not personal beliefs or opinion
• Learning from Data:
• Trained on a vast amount of text from books, websites, and other written materials.
• Learns the structure of language and how to create sentences that make sense.
• Versatility:
• Can be used for various tasks like answering questions, writing stories, or helping with hom
• Adaptable to different subjects and contexts.
• Not Perfect:
• Can make mistakes or provide incorrect information.
• Needs to be used with caution, especially for critical or sensitive topics

Understanding ChatGPT
• Advanced language
model developed by
OpenAI.
• Generates human-like
text based on the
prompts.
• Quality vs prompt.
Quality of Response ∝ Quality of Prompt × Model Understanding
Here:
Quality of Response is the measure of how relevant, accurate, and coherent the response is.
Quality of Prompt represents the clarity, specificity, and relevance of the prompt given to the model.
Model Understanding , model's ability to interpret the prompt, including its training, design, and current context.

ChatGPT : Prompting
refers to the input or question that
you provide to the model. The model
takes this prompt and generates a
response based on the information it
has been trained on.
• Initial Statement or Question
• Context
• Intended Output
• Tone or Formality
• Specificity
• Instructions for Response Format

Prompt
Generation
Review
prompt
Crafting a
good
prompt
Clear and
Specific.
Specify
type of
Response
G-AI is directly dependent on the prompt
https://tinyurl.com/PROMPTGENIUS

Prompt Engineering
• Define the Objective:
• Identify the specific information or assistance
• Be Clear and Precise:
• Use clear language and avoid ambiguity.
• Include essential details without over-
complicating the prompt.
• Consider Context:
• Provide relevant background or context to
guide the response.
• Set the Tone and Style:
• Specify the desired tone (formal, casual) or
style (e.g., summary, explanation) if it matters
for your use case.
• Ask Direct Questions:
• If seeking specific information, formulate your
prompt as a direct question.
• Self Reflective
• Avoid Bias and Leading Questions:
• Craft the prompt neutrally to prevent biased
or skewed responses.
• Test and Refine:
• Experiment with different phrasings and
observe how slight changes can affect the
response.
• Refine the prompt
• Consider Ethical and Privacy Concerns:
• Ethical guidelines and does not request or
reveal sensitive or private information.

Response Validation
• Review response - meets your requirements.
• No access to real-time data
• Vaildate Validate Validate.
• Prompt – response -refine - reprompt.
Relevance
Check
Accuracy
Confirmation
Context
Consistency
Sensitivity
Review
Refinement for
Future Queries

AI_for_Health_Professional_Workshop_

What is the Code
Interpreter in
ChatGPT?
• The Code Interpreter -
execute Python code
• Powerful for code
development
debugging.

Please respond to the following
query with a structured and
academic approach suitable for a
university lecturer. Include bullet-
point answers where applicable,
supported by relevant examples
from scholarly literature. Ensure
that all statements are backed by
credible evidence, and provide
appropriate references and citations
in accordance with standard
academic citation styles (e.g., APA,
MLA, or Chicago). The response
should be clear, concise, and
tailored to an academic audience
engaged in higher education
teaching and research."

How to Use
ChatGPT with
the Code
Interpreter
• Input your code in the
composer area and send it for
execution.
• ChatGPT can provide code
suggestions, help debug your
code, and explain complex
code snippets.
• You can also ask ChatGPT to
generate Python code to solve
specific problems or perform
specific tasks.
Return values of functions or expressions
Printed messages
Data visualizations
DataFrames as tables
Error messages
Execution time
Slide Decks
Diagrams
Interactive Diagrams

67-year-old male has
dizziness every time
he sits up from a lying
position, especially in
the morning. Also,
when he suddenly
moves his head, he
notes the dizziness.
What is the diagnosis

Introduction to Hands-on Session
• Objective: To provide participants with practical experience in
utilizing Elicit.org, Chatbots, and ChatGPT in medical contexts.
• Duration: 45-60 minutes

Practical exploration of ChatGPT's applications
• select specific healthcare scenarios
• designing conversational flows that outline how to interact
• interactive exploration and collaborative experimentation,
• Evaluate response and validate
• Reflection
– How might ChatGPT enhance patient engagement and satisfaction?
– What are the potential risks or challenges in implementing ChatGPT?
– How can healthcare professionals ensure the responsible and ethical
use of this technology?

Part 1 - ChatGPT
• Objective: To demonstrate how ChatGPT can assist in medical
documentation and research.
• Instructions:
• 1. Navigate to ChatGPT interface.
• 2. Generate patient summaries, research abstracts, or FAQs.
• Discussion Points:
• - Streamlining administrative tasks
• - Accuracy and reliability concerns

Using SCISPACE
Analyse your documents with Generative AI

Add SciSpace Copilot to your browser
AI research assistant that explains the text, math, and tables in
scientific literature like research papers, technical blog posts, or
reports. You can also ask follow-up questions, and it will give
you instant answers.

Post your contributions here
https://tinyurl.com/AIPU2023

Part 2 - Elicit.org
• Objective: To familiarize participants with Elicit.org for medical literature summarization.
Instructions:
• 1. Navigate to Elicit.org.
• 2. Sign in or create a new account.
• 3. Craft a clinical problem that you have encountered and look for evidence for the solutions
• 4. Generate a summary in a tabular format.
Discussion Points:
• - Utility in practice
• - Limitations

Chatbots in Healthcare:
An Overview
• Patient and stakeholder
engagement, appointment
scheduling, and information
dissemination.
• Personalised interactions and
responses.
• Potential applications in
diagnostics, telemedicine, and
healthcare education.

Chatbots as
Decision Support
Systems
• making informed decisions.
• Analyzes clinical data and
evidence-based
recommendations.
• Efficiency, accuracy, and
consistency in practice

Chatbots as Personal
Tutors in Healthcare
Education
• Provides personalized
learning experiences for
healthcare students.
• Offers real-time support,
feedback, and resources.
• Enhances engagement,
comprehension, and
retention in medical
education.

Part 3 - Chatbots
• Objective: To engage participants in creating simple medical chatbots.
Instructions:
• 1. Navigate to Botpress.
• 2. Create a basic clinical query chatbot.
• 3. Test the chatbot.
Discussion Points:
• - Integration into healthcare
• - Ethical considerations

Creating your First AI Chatbot for Learning
Botpress.com

Teaching and
Learning
AI can significantly enhance
teaching and learning experiences.
This workshop will explore
methods to integrate AI tools in
educational settings for healthcare
professionals and students.

The brain is the organ of the mind just as the lungs are the
organs for respiration
https://www.tes.com/lessons/N1ICJhPBKCdEtQ/about-the-
brain
• Cognition
• Ego
• Memory
• Senses & Motor
TEMPORO
PARIETEAL LOBE HIPPOCAMPUS
PREFRONTAL
CORTEX
DORSOLATERAL
PREFRONTAL
CORTEX

Stimuli Identification Recall Understand
Association
Knowledge
creation
WIIFM
Affective
change
Life change

•rigid adherence to taught rules or plans"
•no exercise of "discretionary judgment"
•limited "situational perception"
•all aspects of work treated separately with
equal importance
•coping with crowdedness" (multiple activities,
accumulation of information)
•some perception of actions in relation to goals
•deliberate planning
•formulates routines
•holistic view of situation
•prioritizes importance of aspects
•"perceives deviations from the normal pattern"
•employs maxims for guidance, with meanings
that adapt to the
•transcends reliance on rules, guidelines, and maxims
•"intuitive grasp of situations based on deep, tacit understanding"
•has "vision of what is possible"
•uses "analytical approaches" in new situations or in case of problems

Defining Instructional design
“The dynamic structure, process, and system to
facilitate learning and teaching based on cognitive
psychology and neuroscience enabled with technology
incorporating all actors in education and training”
Vaikunthan Rajaratnam 2021

Instructional
Design (ID)
Models
An instructional design model
provides a mental image of the
theoretical foundations as well
as it gives structure and
meaning to an instructional
designer in visualizing the best
approach to take for effective
learning.

Learning Design
An emergent discipline within education that approaches the
creation of learning experiences holistically. While incorporating
elements of instructional design, LD also acknowledges and
integrates considerations like the broader learning context,
individual learner experiences, and the social and collaborative
aspects of learning. The focus is on designing how learners will
interact with the material, each other, and the wider world,
aiming to foster a more immersive and comprehensive learning
experience

Universal Design for Learning
(UDL)
• Multimedia and Multi-sensory.
Multiple Means of
Representation:
• Learner-centric bespoke strategies to
motivate and challenge
Multiple Means of
Engagement:
• Multimodal demonstration of learning by
students
Multiple Means of
Expression:
• Personalised, inclusivity and effectiveness
Overall Impact:

Universal Design for Learning (UDL)

Rubric
Criteria Excellent (4) Good (3) Fair (2) Poor (1)
Understanding of
Subject
Demonstrates deep
understanding;
integrates concepts
seamlessly
Demonstrates good
understanding; minor
confusion in concepts
Some
understanding;
noticeable
confusion in
concepts
Lack of
understanding;
significant confusion
in concepts
Application of
Knowledge
Skillfully applies
knowledge; clear
evidence of critical
thinking
Applies knowledge
with some skill; some
evidence of thinking
Attempts to apply
knowledge; lacks
critical thinking
Fails to apply
knowledge; no
evidence of critical
thinking
Structure &
Organization
Logically structured;
clear introduction,
body, conclusion;
smooth transitions
Generally well-
structured; some
issues with transitions
Structure is present
but flawed;
transitions lack
smoothness
Disorganized; lacks
clear structure or
transitions
Writing Mechanics
Error-free; excellent
grammar,
punctuation, spelling
Minor errors in
grammar,
punctuation, spelling
Several errors
affecting readability
Numerous errors;
significantly impairs
readability
Use of Evidence &
Citations
Comprehensive use of
evidence; citations are
accurate and in
proper format
Good use of evidence;
minor citation errors
Limited or improper
use of evidence;
several citation
errors
Lack of evidence;
incorrect or missing
citations

Relevance to healthcare education
• Adapts to individual student needs
Personalized
Learning:
• Creating diverse and engaging educational materials.
Content Creation:
• Interactive learning experiences (Chatbot)
Student Engagement:
• Provides real-time assessment and feedback .
Assessment and
Feedback:
• content accessible to diverse learners
Accessibility:
• Facilitates collaboration among students and educators,
bridging geographical and language barriers.
Collaboration and
Communication:

Personalized Learning
• Tailors educational content
Adaptive Content Delivery:
• Provides instant feedback and real-time assistance
Real-Time Feedback and
Support:
• Engages with interactive dialogues and Simulates scenarios.
Interactive Learning
Environments:
• Analyses - identify strengths and weaknesses for personalized learning.
Data-Driven Insights:
• Adapts content to diverse learners & multiple languages.
Accessibility and Inclusivity:
• Facilitates collaborative learning experiences and peer interactions.
Collaboration and Peer
Interaction:
• Seamlessly integrates with Learning Management Systems (LMS)
Integration with Existing
Platforms:
• Supports lifelong learning and Assists in tracking and maintaining
professional development
Continuous Learning and Skill
Development:
• Ensures ethical guidelines and privacy regulations.
Ethical and Privacy
Considerations:
• Aligns personalized learning experiences and Ensures relevance to real-
world medical practice
Alignment with Healthcare
Objectives:

Act like a
virtual
patient and
provide me
symptoms
and history
so that I can
improve my
clinical skills

I have been asked
to create a
module for the
examination of
the abdomen for
organomegaly for
medical students.
Create a
curriculum and
include learning
outcomes and the
pedagogy and a
lesson plan

Create an
assessment
task and
provide
rubrics for
the
assessment

Creating Images with AI

https://creator.nightcafe.studio/

• Be clear & descriptive
• Specific styles or
techniques
• Create a test version
• Evaluate the result
• Refine the prompt
• Iterative process

AI for Video Production
Draft
Learning
Outcomes
LO to
Prompt
ChatGPT
for video
script
Import/edi
t script to
AI Video
Generator
Add
personalised
media
Choose
Voiceover
type
Produce
Review
and
Upload

Write a script for
the introduction
of the anatomy
of the
organomegaly
medical student
module. This will
be a 90 second
video script. Just
provide the
narration

AI generated Instructional Video

Assessment and
Feedback
• Automated Grading:
• Grading objective assessments (multiple-choice, fill-in-
the-blank, etc.)
• Evaluating subjective assessments (short answers,
essays) with predefined criteria
• Personalized Feedback:
• Providing tailored feedback on strengths and areas for
improvement
• Engaging in interactive dialogues to reinforce learning
concepts
• Real-time Support:
• Offering instant feedback on performance
• Available 24/7 for flexible learning schedules
• Data-Driven Insights:
• Tracking performance over time for individual and class
insights
• Designing adaptive learning paths based on student
needs
• Enhancing Human Interaction:
• Freeing up educators' time for complex student
interactions
• Facilitating structured peer review processes

What are the
antibiotics for
leprosy
treatment

Based on
this
question
and answer,
create a
rubrics to
mark
answers to
the
question

“the antibiotics used
in leprosy are
rifampicin and
streptomycin.
Sometimes you can
use dapsone for
resistant cases.
Rifampicin is the first
line drug” - based on
this answer provide a
grade for it

Transforming
Teaching with AI
• Tailoring educational materials
• Enhancing teacher collaboration
and efficiency
• Facilitating personalized learning
paths

Enhancing Student
Experience with
LLMs
• Engaging and interactive
learning
• Support for diverse
learning styles
• Opportunities for self-
paced learning

Introduction to Hands-on Session on Educational
Materials
• Objective: To equip participants with skills to use AI tools for
crafting learning outcomes, lesson plans, instructional
materials, and assessments including video-based content.
• Duration: 60-90 minutes

Part 1 - Crafting Learning Outcomes with AI
• Objective: To use AI-based text generators for creating learning outcomes.
Instructions:
• 1. Navigate to an AI-based text generator (e.g., ChatGPT).
• 2. Input keywords or topic areas.
• 3. Generate learning outcomes.
Discussion Points:
• - Quality of AI-generated outcomes
• - Refinement best practices

Part 2 - Creating Lesson Plans with AI
• Objective: To understand how AI can assist in lesson planning.
Instructions:
• 1. Use ChatGPT as a planning tool
• 2. Input course objectives and learning outcomes.
• 3. Generate a lesson plan.
Discussion Points:
• - Pros and cons of AI in lesson planning
• - Customization options

Part 3 - Crafting Instructional Materials
• Objective: To explore AI tools for creating instructional materials.
Instructions:
• 1. Navigate to Night Cafe and In Video.
• 2. Choose the type of material to create.
• 3. Use the AI tool to generate content.
Discussion Points:
• - Quality and relevance assessment
• - Legal and ethical considerations

Part 4 - Creating Assessments and Videos
• Objective: To utilize AI in creating assessments and videos.
Instructions:
• 1. Use ChatGPT to generate assessment.
• 2. Use an InVideo.io) to craft educational videos.
Discussion Points:
• - Effectiveness of AI-generated assessments
• - Quality and utility of AI-crafted videos

Session Summary and Takeaways
• Objective: To summarize the skills and knowledge acquired.
• Discussion Points:
• - Key takeaways
• - Implementation in practice
• - Limitations and future prospects

AI in Healthcare
Research
This segment will focus on
how AI can streamline tasks
such as literature review,
data analytics, and
manuscript development.

AI Tools
• Elicit for Literature Search
• Scholarcy and Typeset for data extraction and summary
• Genei.io for summarisation and key points highlighting
• Keyword generation with ChatGPT ( targeted prompt
engineering)

Goals
• AI writing tools and their functionalities
• How to use them
• When to use them
– idea generation, drafting, editing, and finalizing

ChatGPT
"prompting" refers to the input or
question that you provide to the
model. The model takes this
prompt and generates a response
based on the information it has
been trained on.
• Initial Statement or Question
• Context
• Intended Output
• Tone or Formality
• Specificity
• Instructions for Response Format

Practical Exercise
• Use Elicit to research a question
• Export the bibliography generated
• Export data extraction table as
Excel sheet
• Import bibliography into Scholarch
• Extract relevant data to augment
Excel data extraction table
• Summarise synthesised data( 15K
characters @ a time) with ChatGPT
with appropriate prompts
• Copy response to Word document
• Edit and cite as necessary

Using SCISPACE
Analyse your documents with
Generative AI

Add SciSpace Copilot to your
browser
AI research assistant that explains the text, math, and tables in
scientific literature like research papers, technical blog posts, or
reports. You can also ask follow-up questions, and it will give
you instant answers.

Add to
your
collectio
n of
choice
and click
DONE

Interrog
ate the
paper as
per
needs

Outcome
for Topic
Inserted in
search box

Click
Summarized
Abstract to
ADD to
COLUMN in
table

CLICK on a
paper to
see
summaries
and
Interrogate
as per
needs

Genie.io

AI Platform
• Extracting/organising information - Web & PDF
• Generate abstractive summaries
• Queries using advanced natural language
• Analyse keywords in relation to other documents,

Key features
 Extraction of keywords and defining them
 Search within and across documents based on keywords
and queries.
 Summaries of whole documents and parts of those
documents
 Sort documents into sections called “projects”.
 Add comments and highlights to articles.
 Add custom definitions to words and phrases used in the
article.
 Make separate notes.

Create and login in into your account.

Practical Exercise
• Use Elicit to research a question
• Export the bibliography generated
• Export data extraction table as Excel sheet
• Import bibliography into Scholarch
• Extract relevant data to augment Excel data
extraction table
• Summarise synthesised data( 15K characters
@ a time) with ChatGPT with appropriate
prompts
• Copy response to Word document
• Edit and cite as necessary

Resources
• https://tinyurl.com/LDBOT23
• https://tinyurl.com/AIAWRITEBOT
AI CHAT BOT for AI_POWERED ACADEMIC WRITING

AI-Powered Academic Writing Write Your Research Paper in a Day
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