Generative AI in Healthcare Market - Copy - Copy.pptxGayatriGadhave1
Generative AI holds significant promise in healthcare, there are also challenges related to data privacy, model interpretability, and regulatory compliance that need to be addressed. Ethical considerations and thorough validation processes are crucial to ensure the responsible and safe application of generative AI techniques in healthcare.
Large amounts of heterogeneous medical data have become available in various healthcare organizations (payers, providers, pharmaceuticals). Those data could be an enabling resource for deriving insights for improving care delivery and reducing waste. The enormity and complexity of these datasets present great challenges in analyses and subsequent applications to a practical clinical environment. More details are available here http://dmkd.cs.wayne.edu/TUTORIAL/Healthcare/
Generative AI in Healthcare Market - Copy - Copy.pptxGayatriGadhave1
Generative AI holds significant promise in healthcare, there are also challenges related to data privacy, model interpretability, and regulatory compliance that need to be addressed. Ethical considerations and thorough validation processes are crucial to ensure the responsible and safe application of generative AI techniques in healthcare.
Large amounts of heterogeneous medical data have become available in various healthcare organizations (payers, providers, pharmaceuticals). Those data could be an enabling resource for deriving insights for improving care delivery and reducing waste. The enormity and complexity of these datasets present great challenges in analyses and subsequent applications to a practical clinical environment. More details are available here http://dmkd.cs.wayne.edu/TUTORIAL/Healthcare/
AI in Healthcare | Future of Smart Hospitals Renee Yao
In this talk, I specifically talk about how NVIDIA healthcare AI software and hardware were used to support healthcare AI startups' innovation. Three startups featured: Caption Health, Artisight, and Hyperfine. Audience: healthcare systems CXOs.
Explore how different industries are embracing the utility of AI to create and deliver new value for their customers and organisation
* Discuss the state of maturity of AI across industries
* Get an appreciation of business posture to AI projects
We also review the utility of AI across several industries including:
* Healthcare
* Newsroom & Journalism
* Travel
* Finance
* Supply Chain / eCommerce / Retail
* Streaming & Gaming
* Transportation
* Logistics
* Manufacturing
* Agriculture
* Defense & Cybersecurity
Part of the What Matters in AI series as published on www.andremuscat.com
Healthcare stands to gain significant ground with the help of domain-specific AI capabilities that were historically powered by humans. As a result, the next generation of healthcare has already begun, and it’s being revolutionized by AI.
A changing market landscape and open source innovations are having a dramatic impact on the consumability and ease of use of data science tools. Join this session to learn about the impact these trends and changes will have on the future of data science. If you are a data scientist, or if your organization relies on cutting edge analytics, you won't want to miss this!
Functionalities in AI Applications and Use Cases (OECD)AnandSRao1962
This presentation was given at the OECD Network of AI Specialists (ONE) held in Paris on February 26 and 27. It covers the methodology for assessing AI use cases by technology, value chain, use, business impact, business value, and effort required.
Artificial Intelligence In Medical IndustryDataMites
Medical artificial intelligence (AI) mainly uses computer techniques to perform clinical diagnoses and suggest treatments. AI has the capability of detecting meaningful relationships in a data set and has been widely used in many clinical situations to diagnose, treat, and predict the results.
visit : https://datamites.com/artificial-intelligence-course-training-pune/
Artificial intelligence in health care by Islam salama " Saimo#BoOm "Dr-Islam Salama
A Lecture about basics and concepts of Artificial Intelligence in health care & there applications
محاضرة عامة حول الذكاء الإصطناعي وأساسياته في الرعاية الصحية والطبية وتطبيقاته
When it comes to creating an enterprise AI strategy: if your company isn’t good at analytics, it’s not ready for AI. Succeeding in AI requires being good at data engineering AND analytics. Unfortunately, management teams often assume they can leapfrog best practices for basic data analytics by directly adopting advanced technologies such as ML/AI – setting themselves up for failure from the get-go. This presentation explains how to get basic data engineering and the right technology in place to create and maintain data pipelines so that you can solve problems with AI successfully.
This presentation is about basics of Big data Analytics along with Characteristics,Challenges,Structures,Differences between Traditional and Big data,How Big data is getting benefited in Healthcare Industry,Big data in Real time
Data Catalog as the Platform for Data IntelligenceAlation
Data catalogs are in wide use today across hundreds of enterprises as a means to help data scientists and business analysts find and collaboratively analyze data. Over the past several years, customers have increasingly used data catalogs in applications beyond their search & discovery roots, addressing new use cases such as data governance, cloud data migration, and digital transformation. In this session, the founder and CEO of Alation will discuss the evolution of the data catalog, the many ways in which data catalogs are being used today, the importance of machine learning in data catalogs, and discuss the future of the data catalog as a platform for a broad range of data intelligence solutions.
IBM Watson Health: How cognitive technologies have begun transforming clinica...Maged N. Kamel Boulos
Cite as: Kamel Boulos MN. IBM Watson Health: how cognitive technologies have begun transforming clinical medicine and healthcare (Oral session IV – Patient safety tools, Thursday 19 May 2016, 15:45-16:45, Hotel Puijonsarvi, Kuopio). In: Proceedings of the 4th Nordic Conference on Research in Patient Safety and Quality in Healthcare (NSQH2016), Kuopio, Finland, 18-20 May 2016 (organised by University of Eastern Finland), p.29. URL: http://www.uef.fi/NSQH2016 (In: Nykanen I (ed.). The 4th Nordic Conference on Research in Patient Safety and Quality in Healthcare. Kuopio, Finland, May 18-20, 2016. Program and Abstracts. Publications of the University of Eastern Finland. Report and Studies in Health Sciences 21. 2016, p.29 (of 119 p.). ISBN: 978-952-61-2130-7 (nid.), ISSNL: 1798-5722, ISSN: 1798-5730.)
IBM Watson health: how cognitive technologies have begun transforming clinical medicine and healthcare
Maged N Kamel Boulos
ABSTRACT
Background: IBM Watson Health (http://www.ibm.com/smarterplanet/us/en/ibmwatson/health/) belongs to a new generation of smart cognitive computing technologies (a type of artificial intelligence) that are poised to transform the way healthcare is delivered, and to vastly improve clinical outcomes, quality of care and patient safety.
Objectives: Our goal was to collect and document the huge potential of a range of emerging and exemplary uses of IBM Watson in healthcare in both developed and developing country settings.
Methods: A survey of current peer reviewed and grey literature has been conducted, looking for reports and case studies involving the use of IBM Watson in different health and healthcare applications.
Results, conclusions and clinical implications: With its ability to make sense of unstructured medical information by analysing the meaning and context of natural language, and uncovering important knowledge buried within large volumes of data and information, including medical images, IBM Watson is exceptionally well suited for clinical and healthcare decision support, where there are often elements of ambiguity and uncertainty. It has been (or is currently being) successfully deployed in many developed countries in the West, as well as in developing countries, such as India and South Africa. IBM Watson unlocks a complex case by acquiring information from multiple sources, e.g., accessing the electronic patient record, then parsing all related medical evidence at up to 60 million pages per second. After processing all of this information, Watson offers relevant and prioritised suggestions to the decision-maker, e.g., helping clinicians identify the best diagnosis and treatment options in complex oncology cases, and providing hospital managers with new operational insights. The ultimate goals are to reduce cost, medical errors, mortality rates, and help improve patients' quality of life.
AI in Healthcare | Future of Smart Hospitals Renee Yao
In this talk, I specifically talk about how NVIDIA healthcare AI software and hardware were used to support healthcare AI startups' innovation. Three startups featured: Caption Health, Artisight, and Hyperfine. Audience: healthcare systems CXOs.
Explore how different industries are embracing the utility of AI to create and deliver new value for their customers and organisation
* Discuss the state of maturity of AI across industries
* Get an appreciation of business posture to AI projects
We also review the utility of AI across several industries including:
* Healthcare
* Newsroom & Journalism
* Travel
* Finance
* Supply Chain / eCommerce / Retail
* Streaming & Gaming
* Transportation
* Logistics
* Manufacturing
* Agriculture
* Defense & Cybersecurity
Part of the What Matters in AI series as published on www.andremuscat.com
Healthcare stands to gain significant ground with the help of domain-specific AI capabilities that were historically powered by humans. As a result, the next generation of healthcare has already begun, and it’s being revolutionized by AI.
A changing market landscape and open source innovations are having a dramatic impact on the consumability and ease of use of data science tools. Join this session to learn about the impact these trends and changes will have on the future of data science. If you are a data scientist, or if your organization relies on cutting edge analytics, you won't want to miss this!
Functionalities in AI Applications and Use Cases (OECD)AnandSRao1962
This presentation was given at the OECD Network of AI Specialists (ONE) held in Paris on February 26 and 27. It covers the methodology for assessing AI use cases by technology, value chain, use, business impact, business value, and effort required.
Artificial Intelligence In Medical IndustryDataMites
Medical artificial intelligence (AI) mainly uses computer techniques to perform clinical diagnoses and suggest treatments. AI has the capability of detecting meaningful relationships in a data set and has been widely used in many clinical situations to diagnose, treat, and predict the results.
visit : https://datamites.com/artificial-intelligence-course-training-pune/
Artificial intelligence in health care by Islam salama " Saimo#BoOm "Dr-Islam Salama
A Lecture about basics and concepts of Artificial Intelligence in health care & there applications
محاضرة عامة حول الذكاء الإصطناعي وأساسياته في الرعاية الصحية والطبية وتطبيقاته
When it comes to creating an enterprise AI strategy: if your company isn’t good at analytics, it’s not ready for AI. Succeeding in AI requires being good at data engineering AND analytics. Unfortunately, management teams often assume they can leapfrog best practices for basic data analytics by directly adopting advanced technologies such as ML/AI – setting themselves up for failure from the get-go. This presentation explains how to get basic data engineering and the right technology in place to create and maintain data pipelines so that you can solve problems with AI successfully.
This presentation is about basics of Big data Analytics along with Characteristics,Challenges,Structures,Differences between Traditional and Big data,How Big data is getting benefited in Healthcare Industry,Big data in Real time
Data Catalog as the Platform for Data IntelligenceAlation
Data catalogs are in wide use today across hundreds of enterprises as a means to help data scientists and business analysts find and collaboratively analyze data. Over the past several years, customers have increasingly used data catalogs in applications beyond their search & discovery roots, addressing new use cases such as data governance, cloud data migration, and digital transformation. In this session, the founder and CEO of Alation will discuss the evolution of the data catalog, the many ways in which data catalogs are being used today, the importance of machine learning in data catalogs, and discuss the future of the data catalog as a platform for a broad range of data intelligence solutions.
IBM Watson Health: How cognitive technologies have begun transforming clinica...Maged N. Kamel Boulos
Cite as: Kamel Boulos MN. IBM Watson Health: how cognitive technologies have begun transforming clinical medicine and healthcare (Oral session IV – Patient safety tools, Thursday 19 May 2016, 15:45-16:45, Hotel Puijonsarvi, Kuopio). In: Proceedings of the 4th Nordic Conference on Research in Patient Safety and Quality in Healthcare (NSQH2016), Kuopio, Finland, 18-20 May 2016 (organised by University of Eastern Finland), p.29. URL: http://www.uef.fi/NSQH2016 (In: Nykanen I (ed.). The 4th Nordic Conference on Research in Patient Safety and Quality in Healthcare. Kuopio, Finland, May 18-20, 2016. Program and Abstracts. Publications of the University of Eastern Finland. Report and Studies in Health Sciences 21. 2016, p.29 (of 119 p.). ISBN: 978-952-61-2130-7 (nid.), ISSNL: 1798-5722, ISSN: 1798-5730.)
IBM Watson health: how cognitive technologies have begun transforming clinical medicine and healthcare
Maged N Kamel Boulos
ABSTRACT
Background: IBM Watson Health (http://www.ibm.com/smarterplanet/us/en/ibmwatson/health/) belongs to a new generation of smart cognitive computing technologies (a type of artificial intelligence) that are poised to transform the way healthcare is delivered, and to vastly improve clinical outcomes, quality of care and patient safety.
Objectives: Our goal was to collect and document the huge potential of a range of emerging and exemplary uses of IBM Watson in healthcare in both developed and developing country settings.
Methods: A survey of current peer reviewed and grey literature has been conducted, looking for reports and case studies involving the use of IBM Watson in different health and healthcare applications.
Results, conclusions and clinical implications: With its ability to make sense of unstructured medical information by analysing the meaning and context of natural language, and uncovering important knowledge buried within large volumes of data and information, including medical images, IBM Watson is exceptionally well suited for clinical and healthcare decision support, where there are often elements of ambiguity and uncertainty. It has been (or is currently being) successfully deployed in many developed countries in the West, as well as in developing countries, such as India and South Africa. IBM Watson unlocks a complex case by acquiring information from multiple sources, e.g., accessing the electronic patient record, then parsing all related medical evidence at up to 60 million pages per second. After processing all of this information, Watson offers relevant and prioritised suggestions to the decision-maker, e.g., helping clinicians identify the best diagnosis and treatment options in complex oncology cases, and providing hospital managers with new operational insights. The ultimate goals are to reduce cost, medical errors, mortality rates, and help improve patients' quality of life.
The Hive Think Tank: Unpacking AI for Healthcare The Hive
In this The Hive Think Tank talk, Ash Damle, CEO of Lumiata takes a deep dive into Lumiata’s core technological engine - the Lumiata Medical Graph, which applies graph-based machine learning to compute the complex relationships between health data in the same way that a physician would, and how this medical AI engine powers personalization and automation within risk and care management.
Predictive Analytics - Big Data & Artificial IntelligenceManish Jain
Quick overview of the latest in big data and artificial intelligence. A lot of buzzwords being thrown around, hopefully this presentation will demystify many of the terms.
This presentation summarizes our research on 40 companies from around the world that are leveraging Artificial Intelligence to improve the Healthcare Industry. They are all well-funded, have highly qualified CEOs & Boards, and are poised to achieve their product development milestones.
The I-Square Ventures proprietary rating algorithm indicates that almost all of these companies will receive more funding, and/or be acquired by larger companies.
Precision Algorithms in Healthcare: Improving treatments with AIDay1 Technologies
It’s 2020 and we can safely say that the year hasn’t been our best or what we wanted it to be like. The alarming spread of COVID-19, and its aftermath has people unrooted and shaken to their toes, and literally everyone is looking at technology and healthcare innovations to find an answer to the pandemic. And fast.
These are the upcoming life science trends we can expect to see more in 2019. While healthcare research in immunomodulation and gene therapy is relevant; data-sharing, purpose-driven analytics and AI is gaining more popularity within the industry. With these technological aspects in place, the research community hopes to drive for more discoveries and medical breakthroughs.
The healthcare industry is experiencing a revolution due to artificial intelligence, which is revolutionizing the way doctors diagnose, treat, and manage patient care. Doctor Referral Network helps in increasing accuracy to streamlining processes; AI is paving the way for innovative innovations.
The Future of mHealth - Jay Srini - March 2011LifeWIRE Corp
Jay Srini's presentation of her take on the Future of mHealth, presented at the 3rd mHealth Networking Conference, March 30, 2011. Aside from being one of the preeminent thought leader in the area of innovation and mhealth, she holds a number of positions including Assistant Professor at the University of Pittsburgh and CIO for LifeWIRE Corp.
The future of healthcare will see a shift from treating illness to sustaining wellness. Ageing could become a treatable disease in the future. Find out more: http://bit.ly/2wD13dL
Artificial Intelligence in Healthcare.pdfayushiqss
Imagine a parallel world, where everyone could know about their future health and any diseases they might have in later years. Now, come back to the real world where you no longer need to imagine anything. Everything is possible now with the integration of Artificial Intelligence in healthcare. Humans are developing the best AI and ML-powered devices that can predict your future health.
12 Gifts of Digital Health: How Futuristic Technologies Changed Healthcare an...Enspektos, LLC
When people talk about how digital technologies will influence health, many assume changes will happen years or decades into the future. Yet, in 2014 a range of digital tech, from Big Data to genomics, gave people the gift of life, knowledge and more. Look back at the year that was in digital health and understand that he future is now.
Promise and peril: How artificial intelligence is transforming health careΔρ. Γιώργος K. Κασάπης
AI has enormous potential to improve the quality of health care, enable early diagnosis of diseases, and reduce costs. But if implemented incautiously, AI can exacerbate health disparities, endanger patient privacy, and perpetuate bias. STAT, with support from the Commonwealth Fund, explored these possibilities and pitfalls during the past year and a half, illuminating best practices while identifying concerns and regulatory gaps. This report includes many of the articles we published and summarizes our findings, as well as recommendations we heard from caregivers, health care executives, academic experts, patient advocates, and others.
Optimising maternal & child healthcare in India through the integrated use of...Skannd Tyagi
This paper is a literature review on the present condition of pre-natal and post-natal Maternal and Child healthcare in Rural India. This is a first step on finding the several possibilities using AI, Big Data and Telemedicine in identifying patterns and provide more structured and streamlined support to rural and semi-urban communities. Our endeavour with this research paper is to identify the pain points and attempt to find solutions using current technologies.
Similar to Big Data to Artificial Intelligence in Healthcare (20)
Title: Sense of Taste
Presenter: Dr. Faiza, Assistant Professor of Physiology
Qualifications:
MBBS (Best Graduate, AIMC Lahore)
FCPS Physiology
ICMT, CHPE, DHPE (STMU)
MPH (GC University, Faisalabad)
MBA (Virtual University of Pakistan)
Learning Objectives:
Describe the structure and function of taste buds.
Describe the relationship between the taste threshold and taste index of common substances.
Explain the chemical basis and signal transduction of taste perception for each type of primary taste sensation.
Recognize different abnormalities of taste perception and their causes.
Key Topics:
Significance of Taste Sensation:
Differentiation between pleasant and harmful food
Influence on behavior
Selection of food based on metabolic needs
Receptors of Taste:
Taste buds on the tongue
Influence of sense of smell, texture of food, and pain stimulation (e.g., by pepper)
Primary and Secondary Taste Sensations:
Primary taste sensations: Sweet, Sour, Salty, Bitter, Umami
Chemical basis and signal transduction mechanisms for each taste
Taste Threshold and Index:
Taste threshold values for Sweet (sucrose), Salty (NaCl), Sour (HCl), and Bitter (Quinine)
Taste index relationship: Inversely proportional to taste threshold
Taste Blindness:
Inability to taste certain substances, particularly thiourea compounds
Example: Phenylthiocarbamide
Structure and Function of Taste Buds:
Composition: Epithelial cells, Sustentacular/Supporting cells, Taste cells, Basal cells
Features: Taste pores, Taste hairs/microvilli, and Taste nerve fibers
Location of Taste Buds:
Found in papillae of the tongue (Fungiform, Circumvallate, Foliate)
Also present on the palate, tonsillar pillars, epiglottis, and proximal esophagus
Mechanism of Taste Stimulation:
Interaction of taste substances with receptors on microvilli
Signal transduction pathways for Umami, Sweet, Bitter, Sour, and Salty tastes
Taste Sensitivity and Adaptation:
Decrease in sensitivity with age
Rapid adaptation of taste sensation
Role of Saliva in Taste:
Dissolution of tastants to reach receptors
Washing away the stimulus
Taste Preferences and Aversions:
Mechanisms behind taste preference and aversion
Influence of receptors and neural pathways
Impact of Sensory Nerve Damage:
Degeneration of taste buds if the sensory nerve fiber is cut
Abnormalities of Taste Detection:
Conditions: Ageusia, Hypogeusia, Dysgeusia (parageusia)
Causes: Nerve damage, neurological disorders, infections, poor oral hygiene, adverse drug effects, deficiencies, aging, tobacco use, altered neurotransmitter levels
Neurotransmitters and Taste Threshold:
Effects of serotonin (5-HT) and norepinephrine (NE) on taste sensitivity
Supertasters:
25% of the population with heightened sensitivity to taste, especially bitterness
Increased number of fungiform papillae
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Ve...kevinkariuki227
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Verified Chapters 1 - 19, Complete Newest Version.pdf
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Verified Chapters 1 - 19, Complete Newest Version.pdf
Title: Sense of Smell
Presenter: Dr. Faiza, Assistant Professor of Physiology
Qualifications:
MBBS (Best Graduate, AIMC Lahore)
FCPS Physiology
ICMT, CHPE, DHPE (STMU)
MPH (GC University, Faisalabad)
MBA (Virtual University of Pakistan)
Learning Objectives:
Describe the primary categories of smells and the concept of odor blindness.
Explain the structure and location of the olfactory membrane and mucosa, including the types and roles of cells involved in olfaction.
Describe the pathway and mechanisms of olfactory signal transmission from the olfactory receptors to the brain.
Illustrate the biochemical cascade triggered by odorant binding to olfactory receptors, including the role of G-proteins and second messengers in generating an action potential.
Identify different types of olfactory disorders such as anosmia, hyposmia, hyperosmia, and dysosmia, including their potential causes.
Key Topics:
Olfactory Genes:
3% of the human genome accounts for olfactory genes.
400 genes for odorant receptors.
Olfactory Membrane:
Located in the superior part of the nasal cavity.
Medially: Folds downward along the superior septum.
Laterally: Folds over the superior turbinate and upper surface of the middle turbinate.
Total surface area: 5-10 square centimeters.
Olfactory Mucosa:
Olfactory Cells: Bipolar nerve cells derived from the CNS (100 million), with 4-25 olfactory cilia per cell.
Sustentacular Cells: Produce mucus and maintain ionic and molecular environment.
Basal Cells: Replace worn-out olfactory cells with an average lifespan of 1-2 months.
Bowman’s Gland: Secretes mucus.
Stimulation of Olfactory Cells:
Odorant dissolves in mucus and attaches to receptors on olfactory cilia.
Involves a cascade effect through G-proteins and second messengers, leading to depolarization and action potential generation in the olfactory nerve.
Quality of a Good Odorant:
Small (3-20 Carbon atoms), volatile, water-soluble, and lipid-soluble.
Facilitated by odorant-binding proteins in mucus.
Membrane Potential and Action Potential:
Resting membrane potential: -55mV.
Action potential frequency in the olfactory nerve increases with odorant strength.
Adaptation Towards the Sense of Smell:
Rapid adaptation within the first second, with further slow adaptation.
Psychological adaptation greater than receptor adaptation, involving feedback inhibition from the central nervous system.
Primary Sensations of Smell:
Camphoraceous, Musky, Floral, Pepperminty, Ethereal, Pungent, Putrid.
Odor Detection Threshold:
Examples: Hydrogen sulfide (0.0005 ppm), Methyl-mercaptan (0.002 ppm).
Some toxic substances are odorless at lethal concentrations.
Characteristics of Smell:
Odor blindness for single substances due to lack of appropriate receptor protein.
Behavioral and emotional influences of smell.
Transmission of Olfactory Signals:
From olfactory cells to glomeruli in the olfactory bulb, involving lateral inhibition.
Primitive, less old, and new olfactory systems with different path
Anti ulcer drugs and their Advance pharmacology ||
Anti-ulcer drugs are medications used to prevent and treat ulcers in the stomach and upper part of the small intestine (duodenal ulcers). These ulcers are often caused by an imbalance between stomach acid and the mucosal lining, which protects the stomach lining.
||Scope: Overview of various classes of anti-ulcer drugs, their mechanisms of action, indications, side effects, and clinical considerations.
The prostate is an exocrine gland of the male mammalian reproductive system
It is a walnut-sized gland that forms part of the male reproductive system and is located in front of the rectum and just below the urinary bladder
Function is to store and secrete a clear, slightly alkaline fluid that constitutes 10-30% of the volume of the seminal fluid that along with the spermatozoa, constitutes semen
A healthy human prostate measures (4cm-vertical, by 3cm-horizontal, 2cm ant-post ).
It surrounds the urethra just below the urinary bladder. It has anterior, median, posterior and two lateral lobes
It’s work is regulated by androgens which are responsible for male sex characteristics
Generalised disease of the prostate due to hormonal derangement which leads to non malignant enlargement of the gland (increase in the number of epithelial cells and stromal tissue)to cause compression of the urethra leading to symptoms (LUTS
New Directions in Targeted Therapeutic Approaches for Older Adults With Mantl...i3 Health
i3 Health is pleased to make the speaker slides from this activity available for use as a non-accredited self-study or teaching resource.
This slide deck presented by Dr. Kami Maddocks, Professor-Clinical in the Division of Hematology and
Associate Division Director for Ambulatory Operations
The Ohio State University Comprehensive Cancer Center, will provide insight into new directions in targeted therapeutic approaches for older adults with mantle cell lymphoma.
STATEMENT OF NEED
Mantle cell lymphoma (MCL) is a rare, aggressive B-cell non-Hodgkin lymphoma (NHL) accounting for 5% to 7% of all lymphomas. Its prognosis ranges from indolent disease that does not require treatment for years to very aggressive disease, which is associated with poor survival (Silkenstedt et al, 2021). Typically, MCL is diagnosed at advanced stage and in older patients who cannot tolerate intensive therapy (NCCN, 2022). Although recent advances have slightly increased remission rates, recurrence and relapse remain very common, leading to a median overall survival between 3 and 6 years (LLS, 2021). Though there are several effective options, progress is still needed towards establishing an accepted frontline approach for MCL (Castellino et al, 2022). Treatment selection and management of MCL are complicated by the heterogeneity of prognosis, advanced age and comorbidities of patients, and lack of an established standard approach for treatment, making it vital that clinicians be familiar with the latest research and advances in this area. In this activity chaired by Michael Wang, MD, Professor in the Department of Lymphoma & Myeloma at MD Anderson Cancer Center, expert faculty will discuss prognostic factors informing treatment, the promising results of recent trials in new therapeutic approaches, and the implications of treatment resistance in therapeutic selection for MCL.
Target Audience
Hematology/oncology fellows, attending faculty, and other health care professionals involved in the treatment of patients with mantle cell lymphoma (MCL).
Learning Objectives
1.) Identify clinical and biological prognostic factors that can guide treatment decision making for older adults with MCL
2.) Evaluate emerging data on targeted therapeutic approaches for treatment-naive and relapsed/refractory MCL and their applicability to older adults
3.) Assess mechanisms of resistance to targeted therapies for MCL and their implications for treatment selection
Couples presenting to the infertility clinic- Do they really have infertility...Sujoy Dasgupta
Dr Sujoy Dasgupta presented the study on "Couples presenting to the infertility clinic- Do they really have infertility? – The unexplored stories of non-consummation" in the 13th Congress of the Asia Pacific Initiative on Reproduction (ASPIRE 2024) at Manila on 24 May, 2024.
These simplified slides by Dr. Sidra Arshad present an overview of the non-respiratory functions of the respiratory tract.
Learning objectives:
1. Enlist the non-respiratory functions of the respiratory tract
2. Briefly explain how these functions are carried out
3. Discuss the significance of dead space
4. Differentiate between minute ventilation and alveolar ventilation
5. Describe the cough and sneeze reflexes
Study Resources:
1. Chapter 39, Guyton and Hall Textbook of Medical Physiology, 14th edition
2. Chapter 34, Ganong’s Review of Medical Physiology, 26th edition
3. Chapter 17, Human Physiology by Lauralee Sherwood, 9th edition
4. Non-respiratory functions of the lungs https://academic.oup.com/bjaed/article/13/3/98/278874
Ozempic: Preoperative Management of Patients on GLP-1 Receptor Agonists Saeid Safari
Preoperative Management of Patients on GLP-1 Receptor Agonists like Ozempic and Semiglutide
ASA GUIDELINE
NYSORA Guideline
2 Case Reports of Gastric Ultrasound
Ozempic: Preoperative Management of Patients on GLP-1 Receptor Agonists
Big Data to Artificial Intelligence in Healthcare
1.
2. Why we study Big Data in healthcare system?
Big Data Sources and technique
Some Examples of Artificial Intelligence in Healthcare
3. Electronic health data sets are large,
continuously growing, complex, difficult to
manage with traditional software
90% Unstructured Data
25 X as much data over coming
decade(One Exabyte by 2020) -Kaiser
4.
5.
6.
7.
8. Designing treatment plans:
IBM Watson launched program for oncologists
Provides evidence-based treatment options
Assisting repetitive jobs:
Medical Sieve : Next generation “cognitive assistant”
with analytical, reasoning capabilities and wide range
of clinical knowledge.
Assist clinical decision making in radiology and
cardiology.
9. Health assistance and medication management:
AiCure App supported by The National Institutes of
Health is a HIPAA-compliant software
Use smartphone’s webcam and AI to autonomously
confirm that patients are adhering to their
prescriptions
Precision medicine:
Deep Genomics aims at identifying patterns in huge
data sets of genetic information and medical record
Tell doctors what will happen within a cell when DNA
is altered by genetic variation
10. Stephen Hawking said that development of full
artificial intelligence could spell the end of the
human race
Unraveling Big Data : make right decision at right
time for right patients
Editor's Notes
Highlights and summarize the basic concepts of the work presented by Dr. James Tcheng and Dr. Jason Burke in previous seminar series.
Big Data in healthcare is being used to predict epidemics, cure disease, improve quality of life and avoid preventable deaths.
Boundless data in healthcare about patient condition, procedures and drugs across multiple providers, that are stored by organizations at different locations under different formats.
Subjective Decision to Evidence Based Medicine
More incentives to professionals to use EHR
To answer the questions preciously unanswered
Why we need to study big data? The answer is to achieve stage 4
Feature Selection is a process that chooses an optimal subset of features according to a certain criterion
The theory and development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.
Analyze meaning and context of structured and unstructured data in clinical notes
Combines the attributes from the patient’s file with clinical expertise, external research data
It is able to analyze radiology images to spot and detect problems faster and more reliably.
captures evidence of medication ingestion. Real-time data are centralized for immediate intervention and longitudinal tracking of adherence patterns.
Look for mutations and linkages to disease.
Healthcare is a data-rich domain. As more data is collected, there is increasing demand for big data analytics and AI
Efficient utilization of healthcare can yield immediate returns in terms of patient outcomes and lowering cost.
