Invited to present the work under development by Institute for Systems and Robotics (ISR-Lisboa) and Interactive Technologies Institute (ITI), the one-hour presentation and discussion were held in the 27th of October 2022. The work was presented remotely to the Department of Rad/Neuroimaging and Neurointervention at Stanford University in California. For this talk, I was invited to present our team, project, and work to the research team of Prof. Greg Zaharchuk. In the end, the presentation proposes and discusses how personalizing and customizing the answers coming from the AI outputs can positively affect the clinical workflow. Moreover, we present how those strategies are promoting the unbiased behavior of clinicians while improving the clinical workflow.
In this work, we describe the field research, design, and comparative deployment of a multimodal medical imaging user interface for breast screening. The main contributions described here are threefold: 1) The design of an advanced visual interface for multimodal diagnosis of breast cancer (BreastScreening); 2) Insights from the field comparison of Single-Modality vs Multi-Modality screening of breast cancer diagnosis with 31 clinicians and 566 images; and 3) The visualization of the two main types of breast lesions in the following image modalities: (i) MammoGraphy (MG) in both Craniocaudal (CC) and Mediolateral oblique (MLO) views; (ii) UltraSound (US); and (iii) Magnetic Resonance Imaging (MRI).
Validation of Clinical Artificial Intelligence: Where We Are and Where We Are...Sean Manion PhD
This is the deck from a presentation I gave to the Pittsburgh Industrial Statisticians Association (PISA) for their PISA23 event in a session on Artificial Intelligence and Machine Learning.
The deck itself is not intended to be stand alone without the accompanying verbal presentation, however many of the slides contain key elements with references, and my contact information is available at the end if anyone has questions.
Artificial intelligence in healthcare quality and its impact by Dr.Mahboob al...Healthcare consultant
Artificial intelligence (AI) has enormous potential to improve the safety of healthcare, from increasing diagnostic accuracy, to optimising treatment planning, to forecasting outcomes of care.However, integrating AI technologies into the delivery of healthcare is likely to introduce a range of new risks and amplify ...
Artificial intelligence (AI) has numerous applications for the healthcare industry. Machine learning, natural language processing, and robotics can predict an individual's risk of contracting HIV, assess a patient’s risk of inpatient violence, and assist in surgeries.
Yearly presentation as part of the Johns Hopkins course: A New View: Improving Public Health through Innovative Social and Behavioral Tools and Approaches. Delivered on june 21, 2018.
Is the increasing availability of automated image analysis a possibility to strengthen the application of diffusion-MRI as a biometric parameter, and to enhance the future of image biobanks? Or is this evolution threatening the position of radiologists as medical doctors. Is a redefinition of radiologist as computer technicians inevitable?
In this work, we describe the field research, design, and comparative deployment of a multimodal medical imaging user interface for breast screening. The main contributions described here are threefold: 1) The design of an advanced visual interface for multimodal diagnosis of breast cancer (BreastScreening); 2) Insights from the field comparison of Single-Modality vs Multi-Modality screening of breast cancer diagnosis with 31 clinicians and 566 images; and 3) The visualization of the two main types of breast lesions in the following image modalities: (i) MammoGraphy (MG) in both Craniocaudal (CC) and Mediolateral oblique (MLO) views; (ii) UltraSound (US); and (iii) Magnetic Resonance Imaging (MRI).
Validation of Clinical Artificial Intelligence: Where We Are and Where We Are...Sean Manion PhD
This is the deck from a presentation I gave to the Pittsburgh Industrial Statisticians Association (PISA) for their PISA23 event in a session on Artificial Intelligence and Machine Learning.
The deck itself is not intended to be stand alone without the accompanying verbal presentation, however many of the slides contain key elements with references, and my contact information is available at the end if anyone has questions.
Artificial intelligence in healthcare quality and its impact by Dr.Mahboob al...Healthcare consultant
Artificial intelligence (AI) has enormous potential to improve the safety of healthcare, from increasing diagnostic accuracy, to optimising treatment planning, to forecasting outcomes of care.However, integrating AI technologies into the delivery of healthcare is likely to introduce a range of new risks and amplify ...
Artificial intelligence (AI) has numerous applications for the healthcare industry. Machine learning, natural language processing, and robotics can predict an individual's risk of contracting HIV, assess a patient’s risk of inpatient violence, and assist in surgeries.
Yearly presentation as part of the Johns Hopkins course: A New View: Improving Public Health through Innovative Social and Behavioral Tools and Approaches. Delivered on june 21, 2018.
Is the increasing availability of automated image analysis a possibility to strengthen the application of diffusion-MRI as a biometric parameter, and to enhance the future of image biobanks? Or is this evolution threatening the position of radiologists as medical doctors. Is a redefinition of radiologist as computer technicians inevitable?
Cancer is a dangerous ailment that influences any part of the body and could produce malignant tumors. One feature of cancer is that abnormal cells create quickly and expand beyond their regular bounds. This could attack various parts of the human body and spread to other organs, which is the primary cause of cancer death. Cancer is becoming a more serious worldwide health concern. In the face of these threats, advanced technologies such as Artificial Intelligence (AI), cognitive systems, and the Internet of Things (IoT) may be insufficient to prevent, predict, diagnose, and treat cancer. Digital Twins (DT) with a combination of IoT, AI, cloud computing, and communications technologies such as 5G and 6G have the potential to significant reduce serious cancer threats. Observing data from DT populations may aid in the improvement of some cancer screening, prediction, prevention, detection, treatment, and research investment strategies. Applications of DT medicine specifically cancer, have been studied and analyzed in this paper using both conceptual and statistical analyses. This paper also shows a tree of some ailments where DT is applicable in their study. To the best of our knowledge, there is no literature research on various illnesses and DT specifically cancer disorders. To show the potential of DT, development hurdles of utilizing DT in cancer diseases are discussed, and then, several open research directions will be explained.
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.
The Future Outlook of Artificial Intelligence in Cancer Prevention, Diagnosis...IJMCERJournal
ABSTRACT: Using Artificial Intelligence technics to prevent, diagnose and treat cancer is a promising
are(Liao, Ding, Jiang, Wang, Zhang & Zhang, 2018). As a matter of fact, the earlier a cancer is diagnosed, the
better the chances of survival. Or, if diagnosed in the late stages of the disease, the faster the patient is treated
with the right treatment, the better the chances of survival. Recently, the availability of a large and diverse
amount of data has made it possible to use intelligent systems to get more precise results for patients. Depending
on the type of cancer, facing metastasis is always a challenge for physicians, scientists and patients (La Porta &
Zapperi,2018). This paper is to illustratehow artificial intelligence algorithms have been improving and boosting
the research to save lives.
KEYWORDS: artificial intelligence, cancer, machine learning, artificial neural networks, prevention, diagnosis,
treatmen
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.
Thank You for referencing this work, if you find it useful!
Citation of a related scientific book:
Wac, K., Wulfovich, S. (2021). Quantifying Quality of Life, Series: Health Informatics, Springer Nature, Cham, Switzerland.
The talk details:
Katarzyna Wac, “Multimodal Machine Learning for Quality of Life Assessment: Throwing Data at a Problem?”, Keynote at the ZHAW Digital Health Lab Day, September 2021, Winterthur, Switzerland
Video: https://www.zhaw.ch/de/forschung/departementsuebergreifende-kooperationen/digital-health-lab/3-digital-health-lab-day/
Cemal H. Guvercin MedicReS 5th World Congress MedicReS
Ethical Issues in Artifical Intelligence Applied to Medicine Presentation to MedicReS 5th World Congress on October 19,25,2015 in New York by Cemal H. Guvercin, MD, PhD
Thank You for referencing this work, if you find it useful!
Citation of a related scientific paper:
Wac, K., Wulfovich, S. (2021). Quantifying Quality of Life, Series: Health Informatics, Springer Nature, Cham, Switzerland.
The talk details:
Katarzyna Wac, "Treated by Computers?- a futuristic perspective of health care”: Keynote at the Congress of the European Association of Hospital Pharmacists (EAHP), March 2021
ENG1044 - Why should AI be Implemented into Healthcare? [Group 6].pptxMegatKieferTan
English for Computer Technology Studies
Group 6
LENISKA SHAHAYA [22006027]
CHEONG CHENG YI DANNY [23029127]
MEGAT KIEFER TAN BIN KAMARULZAMAN TAN [23022452]
SAYYEDA AAKIFAH AIMAN [23018203]
Presentation given to health-care management class discussing how military research impacts medical innovations eventually benefiting the civilian population
Pravir Ishvarlal- Artificial Intelligence in Healthcareitnewsafrica
Pravir Ishvarlal, Data Scientist at Netcare, on Artificial Intelligence in Healthcare, at Healthcare Innovation Summit Africa 2023 hosted by IT News Africa. #HISA2023 #Healthcare #Healthtech #HealthInnovation
General AI for Medical Educators April 2024Janet Corral
Learn how to consider Artificial Intelligence as augmentation, to enhance your work. In this presentation we cover augmentation, cyborgs and critically appraise examples of #AI in #MedEd. We then discuss faculty development and can #AI be an #instructionaldesinger.
How Artificial Intelligence is revolutionizing Personalized Medicine.pdfEnterprise Wired
Here is how artificial intelligence is revolutionizing personalized medicine? 1. The Power of Data 2. Genomic Medicine and AI 3. Diagnostic Accuracy 4. Predictive Analytics 5. Drug Discovery and Development 6. Ethical and Regulatory Considerations
Big data approaches to healthcare systemsShubham Jain
The idea behind this presentation is to explore how big data will revolutionize existing healthcare system effectively by reducing healthcare concerns such as the selection of appropriate treatment paths, quality of healthcare systems and so on. Large amount of unstructured data is available in various organizations (payers, providers, pharmaceuticals). We will discuss all the intricacies involved in massive datasets of healthcare systems and how combination of VPH technologies and big data resulted into some mind-boggling consequences. Major opportunities in healthcare includes the integration of various data pools such as clinical data, pharmaceutical R&D data and patient behaviour and sentiment data. Finding potential insights from big data with the help of medical image processing techniques, predictive modelling etc. will eventually help us to leverage the ever-increasing costs of care, help providers practice more effective medicine, empower patients and caregivers, support fitness and preventive self-care, and to dream about more personalized medicine.
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
Cancer is a dangerous ailment that influences any part of the body and could produce malignant tumors. One feature of cancer is that abnormal cells create quickly and expand beyond their regular bounds. This could attack various parts of the human body and spread to other organs, which is the primary cause of cancer death. Cancer is becoming a more serious worldwide health concern. In the face of these threats, advanced technologies such as Artificial Intelligence (AI), cognitive systems, and the Internet of Things (IoT) may be insufficient to prevent, predict, diagnose, and treat cancer. Digital Twins (DT) with a combination of IoT, AI, cloud computing, and communications technologies such as 5G and 6G have the potential to significant reduce serious cancer threats. Observing data from DT populations may aid in the improvement of some cancer screening, prediction, prevention, detection, treatment, and research investment strategies. Applications of DT medicine specifically cancer, have been studied and analyzed in this paper using both conceptual and statistical analyses. This paper also shows a tree of some ailments where DT is applicable in their study. To the best of our knowledge, there is no literature research on various illnesses and DT specifically cancer disorders. To show the potential of DT, development hurdles of utilizing DT in cancer diseases are discussed, and then, several open research directions will be explained.
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.
The Future Outlook of Artificial Intelligence in Cancer Prevention, Diagnosis...IJMCERJournal
ABSTRACT: Using Artificial Intelligence technics to prevent, diagnose and treat cancer is a promising
are(Liao, Ding, Jiang, Wang, Zhang & Zhang, 2018). As a matter of fact, the earlier a cancer is diagnosed, the
better the chances of survival. Or, if diagnosed in the late stages of the disease, the faster the patient is treated
with the right treatment, the better the chances of survival. Recently, the availability of a large and diverse
amount of data has made it possible to use intelligent systems to get more precise results for patients. Depending
on the type of cancer, facing metastasis is always a challenge for physicians, scientists and patients (La Porta &
Zapperi,2018). This paper is to illustratehow artificial intelligence algorithms have been improving and boosting
the research to save lives.
KEYWORDS: artificial intelligence, cancer, machine learning, artificial neural networks, prevention, diagnosis,
treatmen
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.
Thank You for referencing this work, if you find it useful!
Citation of a related scientific book:
Wac, K., Wulfovich, S. (2021). Quantifying Quality of Life, Series: Health Informatics, Springer Nature, Cham, Switzerland.
The talk details:
Katarzyna Wac, “Multimodal Machine Learning for Quality of Life Assessment: Throwing Data at a Problem?”, Keynote at the ZHAW Digital Health Lab Day, September 2021, Winterthur, Switzerland
Video: https://www.zhaw.ch/de/forschung/departementsuebergreifende-kooperationen/digital-health-lab/3-digital-health-lab-day/
Cemal H. Guvercin MedicReS 5th World Congress MedicReS
Ethical Issues in Artifical Intelligence Applied to Medicine Presentation to MedicReS 5th World Congress on October 19,25,2015 in New York by Cemal H. Guvercin, MD, PhD
Thank You for referencing this work, if you find it useful!
Citation of a related scientific paper:
Wac, K., Wulfovich, S. (2021). Quantifying Quality of Life, Series: Health Informatics, Springer Nature, Cham, Switzerland.
The talk details:
Katarzyna Wac, "Treated by Computers?- a futuristic perspective of health care”: Keynote at the Congress of the European Association of Hospital Pharmacists (EAHP), March 2021
ENG1044 - Why should AI be Implemented into Healthcare? [Group 6].pptxMegatKieferTan
English for Computer Technology Studies
Group 6
LENISKA SHAHAYA [22006027]
CHEONG CHENG YI DANNY [23029127]
MEGAT KIEFER TAN BIN KAMARULZAMAN TAN [23022452]
SAYYEDA AAKIFAH AIMAN [23018203]
Presentation given to health-care management class discussing how military research impacts medical innovations eventually benefiting the civilian population
Pravir Ishvarlal- Artificial Intelligence in Healthcareitnewsafrica
Pravir Ishvarlal, Data Scientist at Netcare, on Artificial Intelligence in Healthcare, at Healthcare Innovation Summit Africa 2023 hosted by IT News Africa. #HISA2023 #Healthcare #Healthtech #HealthInnovation
General AI for Medical Educators April 2024Janet Corral
Learn how to consider Artificial Intelligence as augmentation, to enhance your work. In this presentation we cover augmentation, cyborgs and critically appraise examples of #AI in #MedEd. We then discuss faculty development and can #AI be an #instructionaldesinger.
How Artificial Intelligence is revolutionizing Personalized Medicine.pdfEnterprise Wired
Here is how artificial intelligence is revolutionizing personalized medicine? 1. The Power of Data 2. Genomic Medicine and AI 3. Diagnostic Accuracy 4. Predictive Analytics 5. Drug Discovery and Development 6. Ethical and Regulatory Considerations
Big data approaches to healthcare systemsShubham Jain
The idea behind this presentation is to explore how big data will revolutionize existing healthcare system effectively by reducing healthcare concerns such as the selection of appropriate treatment paths, quality of healthcare systems and so on. Large amount of unstructured data is available in various organizations (payers, providers, pharmaceuticals). We will discuss all the intricacies involved in massive datasets of healthcare systems and how combination of VPH technologies and big data resulted into some mind-boggling consequences. Major opportunities in healthcare includes the integration of various data pools such as clinical data, pharmaceutical R&D data and patient behaviour and sentiment data. Finding potential insights from big data with the help of medical image processing techniques, predictive modelling etc. will eventually help us to leverage the ever-increasing costs of care, help providers practice more effective medicine, empower patients and caregivers, support fitness and preventive self-care, and to dream about more personalized medicine.
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
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
- Video recording of this lecture in English language: https://youtu.be/lK81BzxMqdo
- Video recording of this lecture in Arabic language: https://youtu.be/Ve4P0COk9OI
- Link to download the book free: https://nephrotube.blogspot.com/p/nephrotube-nephrology-books.html
- Link to NephroTube website: www.NephroTube.com
- Link to NephroTube social media accounts: https://nephrotube.blogspot.com/p/join-nephrotube-on-social-media.html
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
Explore natural remedies for syphilis treatment in Singapore. Discover alternative therapies, herbal remedies, and lifestyle changes that may complement conventional treatments. Learn about holistic approaches to managing syphilis symptoms and supporting overall health.
Tom Selleck Health: A Comprehensive Look at the Iconic Actor’s Wellness Journeygreendigital
Tom Selleck, an enduring figure in Hollywood. has captivated audiences for decades with his rugged charm, iconic moustache. and memorable roles in television and film. From his breakout role as Thomas Magnum in Magnum P.I. to his current portrayal of Frank Reagan in Blue Bloods. Selleck's career has spanned over 50 years. But beyond his professional achievements. fans have often been curious about Tom Selleck Health. especially as he has aged in the public eye.
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Introduction
Many have been interested in Tom Selleck health. not only because of his enduring presence on screen but also because of the challenges. and lifestyle choices he has faced and made over the years. This article delves into the various aspects of Tom Selleck health. exploring his fitness regimen, diet, mental health. and the challenges he has encountered as he ages. We'll look at how he maintains his well-being. the health issues he has faced, and his approach to ageing .
Early Life and Career
Childhood and Athletic Beginnings
Tom Selleck was born on January 29, 1945, in Detroit, Michigan, and grew up in Sherman Oaks, California. From an early age, he was involved in sports, particularly basketball. which played a significant role in his physical development. His athletic pursuits continued into college. where he attended the University of Southern California (USC) on a basketball scholarship. This early involvement in sports laid a strong foundation for his physical health and disciplined lifestyle.
Transition to Acting
Selleck's transition from an athlete to an actor came with its physical demands. His first significant role in "Magnum P.I." required him to perform various stunts and maintain a fit appearance. This role, which he played from 1980 to 1988. necessitated a rigorous fitness routine to meet the show's demands. setting the stage for his long-term commitment to health and wellness.
Fitness Regimen
Workout Routine
Tom Selleck health and fitness regimen has evolved. adapting to his changing roles and age. During his "Magnum, P.I." days. Selleck's workouts were intense and focused on building and maintaining muscle mass. His routine included weightlifting, cardiovascular exercises. and specific training for the stunts he performed on the show.
Selleck adjusted his fitness routine as he aged to suit his body's needs. Today, his workouts focus on maintaining flexibility, strength, and cardiovascular health. He incorporates low-impact exercises such as swimming, walking, and light weightlifting. This balanced approach helps him stay fit without putting undue strain on his joints and muscles.
Importance of Flexibility and Mobility
In recent years, Selleck has emphasized the importance of flexibility and mobility in his fitness regimen. Understanding the natural decline in muscle mass and joint flexibility with age. he includes stretching and yoga in his routine. These practices help prevent injuries, improve posture, and maintain mobilit
Prix Galien International 2024 Forum ProgramLevi Shapiro
June 20, 2024, Prix Galien International and Jerusalem Ethics Forum in ROME. Detailed agenda including panels:
- ADVANCES IN CARDIOLOGY: A NEW PARADIGM IS COMING
- WOMEN’S HEALTH: FERTILITY PRESERVATION
- WHAT’S NEW IN THE TREATMENT OF INFECTIOUS,
ONCOLOGICAL AND INFLAMMATORY SKIN DISEASES?
- ARTIFICIAL INTELLIGENCE AND ETHICS
- GENE THERAPY
- BEYOND BORDERS: GLOBAL INITIATIVES FOR DEMOCRATIZING LIFE SCIENCE TECHNOLOGIES AND PROMOTING ACCESS TO HEALTHCARE
- ETHICAL CHALLENGES IN LIFE SCIENCES
- Prix Galien International Awards Ceremony
Pulmonary Thromboembolism - etilogy, types, medical- Surgical and nursing man...VarunMahajani
Disruption of blood supply to lung alveoli due to blockage of one or more pulmonary blood vessels is called as Pulmonary thromboembolism. In this presentation we will discuss its causes, types and its management in depth.
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
Knee anatomy and clinical tests 2024.pdfvimalpl1234
This includes all relevant anatomy and clinical tests compiled from standard textbooks, Campbell,netter etc..It is comprehensive and best suited for orthopaedicians and orthopaedic residents.
3. Team
João Fernandes
HCI MSc
Francisco M. Calisto
HCI PhD
Carlos Santiago
ML Researcher
Nuno Nunes
HCI Professor
Jacinto Nascimento
ML Professor
Clara Aleluia
Radiologist
Margarida Morais
ML MSc
João Maria Abrantes
Radiologist
4.
5. 9.6 million
deaths in 2018
Bray, F., Ferlay, J., Soerjomataram, I., Siegel, R.L., Torre, L.A. and Jemal, A., 2018. Global cancer statistics 2018: GLOBOCAN estimates of
incidence and mortality worldwide for 36 cancers in 185 countries. CA: a cancer journal for clinicians, 68(6), pp.394-424.
6. ~10%
… yielding false-negative results.
Smith, Robert A., Kimberly S. Andrews, Durado Brooks, Stacey A. Fedewa, Deana Manassaram‐Baptiste, Debbie Saslow, Otis W. Brawley, and
Richard C. Wender. Cancer screening in the United States, 2018: a review of current American Cancer Society guidelines and current issues
in cancer screening. CA: A Cancer Journal for Clinicians 68, no. 4 (2018): 297-316.
7. ~40%
… yielding false-positive results.
Smith, Robert A., Kimberly S. Andrews, Durado Brooks, Stacey A. Fedewa, Deana Manassaram‐Baptiste, Debbie Saslow, Otis W. Brawley, and
Richard C. Wender. Cancer screening in the United States, 2018: a review of current American Cancer Society guidelines and current issues
in cancer screening. CA: A Cancer Journal for Clinicians 68, no. 4 (2018): 297-316.
10. MULTIMODALITY WORKFLOW
>
Magnetic Resonance Imaging
(MRI)
UltraSound
(US)
MammoGraphy
(MG)
>
Lesions
Calisto, F.M., Nunes, N. and Nascimento, J.C., 2020, September. “BreastScreening: On the Use of Multi-Modality in Medical Imaging
Diagnosis”. In Proceedings of the International Conference on Advanced Visual Interfaces (pp. 1-5).
11. BREAST SEVERITY
BI-RADS Meaning
0 Needs more information (more exams or waiting for more exams)
1 Negative
2 Benign
3 Probably Benign
4 Suspicious
5 Highly suggestive of malignancy
6 Known biopsy-proven malignancy
Schaekermann, M., Beaton, G., Habib, M., Lim, A., Larson, K. and Law, E., 2019, May. “Capturing Expert Arguments from Medical Adjudication
Discussions in a Machine-readable Format”. In Companion Proceedings of The 2019 World Wide Web Conference (pp. 1131-1137).
13. ✓
PROBLEM
?
Schaekermann, M., 2020. “Human-AI Interaction in the Presence of Ambiguity: From Deliberation-based Labeling to Ambiguity-aware AI”.
14. MEDICAL IMAGE ASSESSMENT
Prior work in behavioral
sciences for medical
relation extraction
substantiate the
disagreement relations
between inter-variability
and intra-variability.
Dumitrache, A., Aroyo, L. and Welty, C., 2018. “Crowdsourcing Ground Truth for Medical Relation Extraction”. ACM Transactions on Interactive
Intelligent Systems (TiiS), 8(2), pp.1-20.
15. EXPERT DISAGREEMENT
Disagreement relations are addressed as a function of three phenomena:
1. Differences among clinical professionals, such as the medical background
of each clinical institution and bias;
2. Heterogeneous characteristics of the dataset to be analyzed, such as noisy
and heterogeneous modalities;
3. Nature of the diagnostic guidelines, such as the subjective and ambiguous
classification of the BI-RADS.
Schaekermann, M., Beaton, G., Habib, M., Lim, A., Larson, K. and Law, E., 2019. “Understanding expert disagreement in medical data analysis
through structured adjudication”. Proceedings of the ACM on Human-Computer Interaction, 3(CSCW), pp.1-23.
16. PROBLEM
In medical imaging, Doctors
need to trust that AI is being
used safely, and for their
benefit during decision-
making.
17. MEDICAL EXPERIENCE
Interns Juniors Middles Seniors
Calisto, Francisco Maria, Nuno Nunes, and Jacinto C. Nascimento. "Modeling adoption of intelligent agents in medical imaging." International
Journal of Human-Computer Studies 168 (2022): 102922.
18. AGENTS
Strategies, such as adapting the agent
communication, to promote the unbiased
behavior per each category of medical
experience, improving medical
performance, AI perception and
experience.
19. MEDICAL ASSISTANCE
Radiologist fatigue levels and
performance are related to
environmental factors such as
number of False-Positives and
False-Negatives.
20. HUMAN-AI DELIBERATION
BI-RADS = 5
with 99.94%
of accuracy
BI-RADS = 4? BI-RADS = 5
Round 1: the
clinician
interprets the
image alone.
Round 2: the
clinician
interprets AI
suggestions.
Round 3: the
clinician
controls the
final result.
Calisto, F. M., Santiago, C., Nunes, N., & Nascimento, J. C. (2021). Introduction of human-centric AI assistant to aid radiologists for multimodal
breast image classification. International Journal of Human-Computer Studies, 150, 102607.
21. 52 clinicians
… from nine public and private medical institutions in
Portugal.
USER STUDIES
22. 491 patients
from a multimodality dataset of medical images.
Calisto, F. M., Santiago, C., Nunes, N., & Nascimento, J. C. (2022). BreastScreening-AI: Evaluating Medical Intelligent Agents for Human-AI
Interactions. Artificial Intelligence in Medicine, 127, 102285.
DATASET
24. 98%
… of clinicians do understand what the system is thinking.
USER EXPECTATIONS
Calisto, F. M., Santiago, C., Nunes, N., & Nascimento, J. C. (2022). BreastScreening-AI: Evaluating Medical Intelligent Agents for Human-AI
Interactions. Artificial Intelligence in Medicine, 127, 102285.
25. 93%
… trust on the system capability.
USER EXPECTATIONS
Calisto, F. M., Santiago, C., Nunes, N., & Nascimento, J. C. (2022). BreastScreening-AI: Evaluating Medical Intelligent Agents for Human-AI
Interactions. Artificial Intelligence in Medicine, 127, 102285.
26. INTER-VARIABILITY vs INTRA-VARIABILITY
Calisto, F. M., Santiago, C., Nunes, N., & Nascimento, J. C. (2022). BreastScreening-AI: Evaluating Medical Intelligent Agents for Human-AI
Interactions. Artificial Intelligence in Medicine, 127, 102285.
27. CLINICAL IMPACT: Clinician-AI vs Clinician-Only
Calisto, F. M., Santiago, C., Nunes, N., & Nascimento, J. C. (2022). BreastScreening-AI: Evaluating Medical Intelligent Agents for Human-AI
Interactions. Artificial Intelligence in Medicine, 127, 102285.
29. AMBIGUITY-AWARE (Future Work)
Schaekermann, M., Beaton, G., Sanoubari, E., Lim, A., Larson, K. and Law, E., 2020, April. “Ambiguity-aware AI Assistants for Medical Data
Analysis”. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1-14).
» What is the potential benefit of communicating ambiguity in AI outputs?
» How ambiguity-aware AI can be implemented for breast cancer diagnosis?
» How to combine techniques to develop AI assistants capable of recognizing
and explaining ambiguous cases?
30. CONCLUSION
In this research work:
1. We identified how the different suggestion levels (i.e., more suggestive or
imposing the AI recommendations) will impact the radiologists' decision-
making process;
2. We are developing a system that enables clinicians to accept or reject the
AI breast analysis with an adaptive communication depending on the
levels of medical profession experience, such as novice or expert
clinicians;
3. Likewise, we will propose a series of recommendations for a human-
centered approach around personalizable and customizable AI in breast
cancer diagnosis;
31.
32. “
Into whatsoever houses I enter, I will enter to
help the sick, and I will abstain from all
intentional wrong-doing and harm, especially
from abusing the bodies of man or woman,
bond or free.
- Hippocratic
Hello, my name is Francisco Calisto and I am presenting our work in progress, titled as the “Personalizing and Customizing AI Explanations for Clinicians” applied to breast cancer.
… I am a researcher from Portugal and currently a visiting scholar of the Human-Computer Interaction Institute at CMU.
While being involved with a great team from Human-Computer Interaction researchers to Machine Learning and Radiologists…
… we are trying to surpass some of the breast cancer challenges.
Every year about 10 million women die from breast cancer. They can be our grandmothers, wifes or even our daughters.
These numbers are just 10% of suspected women from having breast cancer, but…
… radiologists are yielding about 40% of false-positives, which can lead women to unnecessary biopsies.
The cancer burden can be reduced through early detection of cancer, as well as management of patients who develop cancer.
For an early detection, breast cancer is usually diagnosed with several medical imaging modalities.
Across this multimodality workflow, clinicians perform a kind of loop for the inspection of lesions.
From here, clinicians are using the BI-RADS for classification of the lesion severity. However, the severity classification is not trivial and consensus is not always achieved.
AI can help on this…
… but the “black box” nature of AI introduces a large element of opacity into decision-making. Such problem can be solved by the introduction of eXplainable-AI (XAI) techniques.
Prior work in behavioral sciences for medical relation extraction substantiate the disagreement relations between inter-variability and intra-variability.
Disagreement relations are addressed as a function of three phenomena: (1) differences among clinical professionals, such as the medical background and bias; (2) heterogeneous characteristics of the dataset, such as noisy and heterogeneous modalities; and (3) the nature of the diagnostic guidelines, with subjective and ambiguous classifications of the BI-RADS. In fact, clinical experts often rely on complex viewing technology to inspect medical data.
In medical imaging, Doctors need to trust that AI is being used safely, and for their benefit during decision-making. Intrinsically, humans feel the need to understand how decisions are made.
Specifically, decision-making on the radiology reading room is done by professionals with different medical experiences and clinical profile characteristics.
We expect that strategies, such as adapting the agent communication by personalizing and customizing the AI explanations to each clinician, will promote the unbiased behaviour per each category of professional experience, improving the rates of false-positives and false-negatives during diagnostic.
… which will influence the burning rates of clinicians.
Since AI models are developed and measured using a pipeline of several characteristics, we studied the deliberation process of clinicians during diagnosis without and with the introduction of an intelligent agent. So that we can understand the expectation levels of tolerance on satisfaction and acceptance, as well as tolerance of the model accuracy.
Currently, our user studies are involving 52 clinicians from nine public and private medical institutions in Portugal.
We are training our AI models with four hundred ninety one patients from a multimodality dataset of medical images, such as MammoGrams, UltraSound and MRI.
Our demo hour…
While using our agent, 98% of the 52 clinicians answered that they do understand what the AI system is thinking...
… and 93% trust on the system capability.
We also divided our participant results into groups of inter-variability and intra-variability. Not only, the inter-variability was reduced between groups of patients, but also the intra-variability was reduced for the groups of interns, juniors, middles and seniors…
… as well as, we could improve the final clinician’s performance due to the Clinician-AI diagnostic.
With this studies, we could understand the behaviour characteristics of each medical group, while diagnosing the different groups of patients. Now, we have information from whom and when should the intelligent agent provide a suggestion and how an explanation will influence interpretability and the decision-making.
Indeed, as humans, radiologists are exposed to fatigue levels, where performance is related to environmental factors, such as the number of working hours. As a future direction, we will study and implement a system that will give the hardest patient cases in the beginning of the clinician’s shift, while the most trivial cases are diagnosed in the end of the shift. For that, we will need to study and implement an ambiguous-aware AI system that will classify all cases in our dataset as more or less ambiguous to diagnose. This idea will transform the way radiologists are screening and diagnosing breast cancer.
To conclude, we identified how different levels of suggestions will impact the radiologists' decision-making process. We developed a system that enables clinicians to accept or reject the AI breast analysis by adapting the communication through personalization and customization to each medical professional experience. We propose a series of recommendations and future directions for the development of intelligent agents in breast cancer diagnosis.
During this presentation, about 5 women died for breast cancer, while other 10 went to unnecessary biopsies in Portugal, making our NHS lose 50 thousand euros in just 10 minutes…
I humbly ask the help of the most noble professions, healthcare professionals, to help us cross this path together!