Artificial Intelligence in Pharmaceutical IndustryKevin Lee
This presentation will show the introduction of AI and its possible implementation in Pharmaceutical Industry such as drug discovery, personalized medicine, molecular target prediction, site selection, patient recruitment, process automation, process optimization and more.
We are living in the world of “Big Data”. “Big Data” is mainly expressed with three Vs – Volume, Velocity and Variety. The presentation will discuss how Big Data impacts Pharmaceutical Industry and how drug companies can lead this new Big Data environment.
Artificial Intelligence in Pharmaceutical IndustryKevin Lee
This presentation will show the introduction of AI and its possible implementation in Pharmaceutical Industry such as drug discovery, personalized medicine, molecular target prediction, site selection, patient recruitment, process automation, process optimization and more.
We are living in the world of “Big Data”. “Big Data” is mainly expressed with three Vs – Volume, Velocity and Variety. The presentation will discuss how Big Data impacts Pharmaceutical Industry and how drug companies can lead this new Big Data environment.
Application of artificial intelligence medical robots in healthcare types and...Ruchi Jain
Artificial intelligence robots in the medical field now play an important role in providing automation solutions for medical and other sectors of the industry. AI needs lots of data for training and testing of robots, this data can be obtained using big data and other useful data available in the medical industry to train robots for various purposes.
Trends and issues of artificial intelligence in medical application tutors i...Tutors India
The application of artificial intelligence in healthcare often has a number of ethical implications. In the past, human beings themselves made almost all healthcare decisions. In addition, the use of smart devices to produce or assist with them raises questions about responsibility, openness, consent, and privacy.
Latest trends in medical AI
Aside from merely showing superior effectiveness, emerging innovations that reach the medical sector often need to align with existing procedures, obtain sufficient regulatory clearance, and possibly most significantly, encourage medical professionals and patients to engage in a modern approach. Such problems have given rise to many new developments in study and acceptance of Artificial intelligence.
To learn more visit: http://www.tutorsindia.com/blog/
Contact:
Website: www.tutorsindia.com
Email: info@tutorsindia.com
United Kingdom: +44-1143520021
India: +91-4448137070
Whatsapp Number: +91-8754446690
Perfect partnership - machine learning and CDISC standard dataKevin Lee
The most popular buzz word nowadays in the technology world is “Machine Learning (ML).” Most economists and business experts foresee Machine Learning changing every aspect of our lives in the next 10 years through automating and optimizing processes. This is leading many organizations including drug companies to implement Machine Learning into their businesses.
The presentation will start with the introduction of basic concept of Machine Learning, the computer science technology that provides systems with the ability to learn without being explicitly programmed, and it will discuss what it means by “without being explicitly programmed”. The presentation will also introduce basic ML algorithm -SVM, Decision Tress, Regression, Artificial Neural Network (ANN), and DNN. The presentation will also discuss the impact and potential of Machine Learning in our daily lives and pharmaceutical industry.
The presentation will show how CDISC data can be a perfect match on Machine Learning implementation. In this Machine Learning/AI driven process, data is considered as the most important component. 80 to 90 % of works in Machine Learning is preparing data. Since FDA mandated CDISC standards submission as of Dec 17th, 2016, all the clinical trial data are prepared in CDISC SDTM and ADaM data format. The presentation will show how CDISC data is better choice than Real World Evidence (RWE) data for ML model. The presentation will also show how pharmaceutical industry use CDISC data to build ML model and apply ML model for Real World evidence. Finally, the presentation will show how Pharma industry can use their own in-house data and Machine Learning to build innovative, data-driven business models.
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.
challenges for the big data applications in japanTakushi Otani
Keywords: big data, health care application of big data, efficient traffic support by big data, evacuation assistance by big data application, information ethics, personal information protection, privacy protection, ethics of privacy
We are generating 2.5 Billion GB of data every day. That's a lot of data! We will need super human expertise to make sense out of it. Well, that's exactly what AI can help us do it.
This talk is going to focus on:
i) What is AI?
ii) How AI can help with health care?
iii) How FHIR will help with the adoption of AI
iv) What are the next three steps for any health organization in order to adopt AI?
Presentation from a talk I gave at the Nottingham AI meetup. In this talk I explored some of the practical applications of medical AI, the research surrounding this exciting field and the potential for AI to be utilised as a support tool in healthcare and medicine. The talk will take high level view of the technology and it's application as apposed to a low level technical analysis, making it accessible to everyone.
Please cite as: Kamel Boulos MN. Creating self-aware and smart healthy cities. Invited plenary keynote address followed by sub-plenary round table at WHO 2014 International Healthy Cities Conference, Athens, Greece, 25 October 2014. http://www.healthycities2014.org/ehome/89657/192014/?&
PPT updated in May 2015.
Oct 2017: See also https://www.slideshare.net/sl.medic/how-the-internet-of-things-and-people-can-help-improve-our-health-wellbeing-and-quality-of-life
Big Data and Artificial Intelligence in Critical Care
Anesthesia and Intensive Care
San Raffaele Hospital, Milan, Italy
Vita-Salute San Raffaele University, Milan, Italy
Follow us on Twitter, Facebook and Instagram @SRAnesthesiaICU
10 Common Applications of Artificial Intelligence in HealthcareTechtic Solutions
List of 10 Common Applications of Artificial Intelligence that explain how artificial intelligence is used in healthcare and why it is necessary? To read briefly all common applications of artificial intelligence in healthcare then visit at https://www.techtic.com/blog/applications-of-ai-in-healthcare/
APPLICATION OF ARTIFICIAL INTELLIGENCE TO TRACK PLANT DISEASESABHISEK RATH
this explained how artificial intelligence can be used in agriculture and especially in plant pathology i.e., tracking plant diseases, use of robotics, drone in applying chemicals and other aspects.
Application of artificial intelligence medical robots in healthcare types and...Ruchi Jain
Artificial intelligence robots in the medical field now play an important role in providing automation solutions for medical and other sectors of the industry. AI needs lots of data for training and testing of robots, this data can be obtained using big data and other useful data available in the medical industry to train robots for various purposes.
Trends and issues of artificial intelligence in medical application tutors i...Tutors India
The application of artificial intelligence in healthcare often has a number of ethical implications. In the past, human beings themselves made almost all healthcare decisions. In addition, the use of smart devices to produce or assist with them raises questions about responsibility, openness, consent, and privacy.
Latest trends in medical AI
Aside from merely showing superior effectiveness, emerging innovations that reach the medical sector often need to align with existing procedures, obtain sufficient regulatory clearance, and possibly most significantly, encourage medical professionals and patients to engage in a modern approach. Such problems have given rise to many new developments in study and acceptance of Artificial intelligence.
To learn more visit: http://www.tutorsindia.com/blog/
Contact:
Website: www.tutorsindia.com
Email: info@tutorsindia.com
United Kingdom: +44-1143520021
India: +91-4448137070
Whatsapp Number: +91-8754446690
Perfect partnership - machine learning and CDISC standard dataKevin Lee
The most popular buzz word nowadays in the technology world is “Machine Learning (ML).” Most economists and business experts foresee Machine Learning changing every aspect of our lives in the next 10 years through automating and optimizing processes. This is leading many organizations including drug companies to implement Machine Learning into their businesses.
The presentation will start with the introduction of basic concept of Machine Learning, the computer science technology that provides systems with the ability to learn without being explicitly programmed, and it will discuss what it means by “without being explicitly programmed”. The presentation will also introduce basic ML algorithm -SVM, Decision Tress, Regression, Artificial Neural Network (ANN), and DNN. The presentation will also discuss the impact and potential of Machine Learning in our daily lives and pharmaceutical industry.
The presentation will show how CDISC data can be a perfect match on Machine Learning implementation. In this Machine Learning/AI driven process, data is considered as the most important component. 80 to 90 % of works in Machine Learning is preparing data. Since FDA mandated CDISC standards submission as of Dec 17th, 2016, all the clinical trial data are prepared in CDISC SDTM and ADaM data format. The presentation will show how CDISC data is better choice than Real World Evidence (RWE) data for ML model. The presentation will also show how pharmaceutical industry use CDISC data to build ML model and apply ML model for Real World evidence. Finally, the presentation will show how Pharma industry can use their own in-house data and Machine Learning to build innovative, data-driven business models.
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.
challenges for the big data applications in japanTakushi Otani
Keywords: big data, health care application of big data, efficient traffic support by big data, evacuation assistance by big data application, information ethics, personal information protection, privacy protection, ethics of privacy
We are generating 2.5 Billion GB of data every day. That's a lot of data! We will need super human expertise to make sense out of it. Well, that's exactly what AI can help us do it.
This talk is going to focus on:
i) What is AI?
ii) How AI can help with health care?
iii) How FHIR will help with the adoption of AI
iv) What are the next three steps for any health organization in order to adopt AI?
Presentation from a talk I gave at the Nottingham AI meetup. In this talk I explored some of the practical applications of medical AI, the research surrounding this exciting field and the potential for AI to be utilised as a support tool in healthcare and medicine. The talk will take high level view of the technology and it's application as apposed to a low level technical analysis, making it accessible to everyone.
Please cite as: Kamel Boulos MN. Creating self-aware and smart healthy cities. Invited plenary keynote address followed by sub-plenary round table at WHO 2014 International Healthy Cities Conference, Athens, Greece, 25 October 2014. http://www.healthycities2014.org/ehome/89657/192014/?&
PPT updated in May 2015.
Oct 2017: See also https://www.slideshare.net/sl.medic/how-the-internet-of-things-and-people-can-help-improve-our-health-wellbeing-and-quality-of-life
Big Data and Artificial Intelligence in Critical Care
Anesthesia and Intensive Care
San Raffaele Hospital, Milan, Italy
Vita-Salute San Raffaele University, Milan, Italy
Follow us on Twitter, Facebook and Instagram @SRAnesthesiaICU
10 Common Applications of Artificial Intelligence in HealthcareTechtic Solutions
List of 10 Common Applications of Artificial Intelligence that explain how artificial intelligence is used in healthcare and why it is necessary? To read briefly all common applications of artificial intelligence in healthcare then visit at https://www.techtic.com/blog/applications-of-ai-in-healthcare/
APPLICATION OF ARTIFICIAL INTELLIGENCE TO TRACK PLANT DISEASESABHISEK RATH
this explained how artificial intelligence can be used in agriculture and especially in plant pathology i.e., tracking plant diseases, use of robotics, drone in applying chemicals and other aspects.
Presented at the 32th Naval Medical Department Academic Conference: Medical Challenges in Disruptive Era, Naval Medical Department, Chonburi, Thailand on September 5, 2019
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
Health: to insure or to ensure? Welcome in the new normalKoen Vingerhoets
Slideset about health and how it affects our culture. With the increasing pace of change, new business models emerge. They're supported by new technological evolutions (healthtech), enabling new companies to challenge incumbent insurance companies.
Big Data Provides Opportunities, Challenges and a Better Future in Health and...Cirdan
This presentation was delivered by Ashraf Mina, NSW Pathology at the Pathology Horizons 2017 Conference in Cairns, Australia.
Pathology Horizons 2017 is an annual CPD conference organised by Cirdan on the future of pathology. You can access more information about the event at www.pathologyhorizons.com
The company was founded in 2010 and is headquartered in Lisburn, Northern Ireland and has additional offices in Canada and Australia.
Cirdan is also responsible for organising Pathology Horizons, an annual and open CPD conference on the future of pathology. For more information visit - www.pathologyhorizons.com
Artificial Intelligence (AI) is shaping and reshaping every industry under the sun. The Healthcare industry is not any exception.
In this presentation, I have discussed the basics of AI as well as how it is being used in various branches of the healthcare industry. I presented this topic in my departmental seminar in October 2021 and received appreciation as well as positive feedback in this regard.
Artificial intelligence in field of pharmacyKaustav Dey
AI is a program designed to produce outcome in a manner similar to human intelligence,logic and reasoning.This can be used in field of Pharmacy for betterment of humankind, to save lives,money and time
While E-health is based on networked I-C-T devices of the humans, operated by the humans for human healthcare and wellness, IOMT is a network of the ‘smart-devices’, operated by the devices for human healthcare and wellness. An estimated 160 million smart medical devices are expected to be connected in 2020. This number will increase exponentially. We need to be prepared for the disruptive influence of IOMT on the present-day healthcare paradigm. A major concern is the sheer magnitude of digital healthcare data generated by IOMT. Are we creating a "Digital Black hole" is a question for deep introspection.
Augmented Personalized Health: using AI techniques on semantically integrated...Amit Sheth
Keynote @ 2018 AAAI Joint Workshop on Health Intelligence (W3PHIAI 2018), 2 February 2018, New Orleans, LA [Video: https://youtu.be/GujvoWRa0O8]
Related article: https://ieeexplore.ieee.org/document/8355891/
Abstract
Healthcare as we know it is in the process of going through a massive change - from episodic to continuous, from disease-focused to wellness and quality of life focused, from clinic centric to anywhere a patient is, from clinician controlled to patient empowered, and from being driven by limited data to 360-degree, multimodal personal-public-population physical-cyber-social big data-driven. While the ability to create and capture data is already here, the upcoming innovations will be in converting this big data into smart data through contextual and personalized processing such that patients and clinicians can make better decisions and take timely actions for augmented personalized health. In this talk, we will discuss how use of AI techniques on semantically integrated patient-generated health data (PGHD), environmental data, clinical data, and public social data is exploited to achieve a range of augmented health management strategies that include self-monitoring, self-appraisal, self-management, intervention, and Disease Progression Tracking and Prediction. We will review examples and outcomes from a number of applications, some involving patient evaluations, including asthma in children, bariatric surgery/obesity, mental health/depression, that are part of the Kno.e.sis kHealth personalized digital health initiative.
Background: Background: http://bit.ly/k-APH, http://bit.ly/kAsthma, http://j.mp/PARCtalk
Similar to Digital healthcare show - How will Artificial Intelligence in healthcare will impact patient outcomes in the future? (20)
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.
These lecture slides, by Dr Sidra Arshad, offer a quick overview of physiological basis of a normal electrocardiogram.
Learning objectives:
1. Define an electrocardiogram (ECG) and electrocardiography
2. Describe how dipoles generated by the heart produce the waveforms of the ECG
3. Describe the components of a normal electrocardiogram of a typical bipolar leads (limb II)
4. Differentiate between intervals and segments
5. Enlist some common indications for obtaining an ECG
Study Resources:
1. Chapter 11, Guyton and Hall Textbook of Medical Physiology, 14th edition
2. Chapter 9, Human Physiology - From Cells to Systems, Lauralee Sherwood, 9th edition
3. Chapter 29, Ganong’s Review of Medical Physiology, 26th edition
4. Electrocardiogram, StatPearls - https://www.ncbi.nlm.nih.gov/books/NBK549803/
5. ECG in Medical Practice by ABM Abdullah, 4th edition
6. ECG Basics, http://www.nataliescasebook.com/tag/e-c-g-basics
MANAGEMENT OF ATRIOVENTRICULAR CONDUCTION BLOCK.pdfJim Jacob Roy
Cardiac conduction defects can occur due to various causes.
Atrioventricular conduction blocks ( AV blocks ) are classified into 3 types.
This document describes the acute management of AV block.
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
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
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
Lung Cancer: Artificial Intelligence, Synergetics, Complex System Analysis, S...Oleg Kshivets
RESULTS: Overall life span (LS) was 2252.1±1742.5 days and cumulative 5-year survival (5YS) reached 73.2%, 10 years – 64.8%, 20 years – 42.5%. 513 LCP lived more than 5 years (LS=3124.6±1525.6 days), 148 LCP – more than 10 years (LS=5054.4±1504.1 days).199 LCP died because of LC (LS=562.7±374.5 days). 5YS of LCP after bi/lobectomies was significantly superior in comparison with LCP after pneumonectomies (78.1% vs.63.7%, P=0.00001 by log-rank test). AT significantly improved 5YS (66.3% vs. 34.8%) (P=0.00000 by log-rank test) only for LCP with N1-2. Cox modeling displayed that 5YS of LCP significantly depended on: phase transition (PT) early-invasive LC in terms of synergetics, PT N0—N12, cell ratio factors (ratio between cancer cells- CC and blood cells subpopulations), G1-3, histology, glucose, AT, blood cell circuit, prothrombin index, heparin tolerance, recalcification time (P=0.000-0.038). Neural networks, genetic algorithm selection and bootstrap simulation revealed relationships between 5YS and PT early-invasive LC (rank=1), PT N0—N12 (rank=2), thrombocytes/CC (3), erythrocytes/CC (4), eosinophils/CC (5), healthy cells/CC (6), lymphocytes/CC (7), segmented neutrophils/CC (8), stick neutrophils/CC (9), monocytes/CC (10); leucocytes/CC (11). Correct prediction of 5YS was 100% by neural networks computing (area under ROC curve=1.0; error=0.0).
CONCLUSIONS: 5YS of LCP after radical procedures significantly depended on: 1) PT early-invasive cancer; 2) PT N0--N12; 3) cell ratio factors; 4) blood cell circuit; 5) biochemical factors; 6) hemostasis system; 7) AT; 8) LC characteristics; 9) LC cell dynamics; 10) surgery type: lobectomy/pneumonectomy; 11) anthropometric data. Optimal diagnosis and treatment strategies for LC are: 1) screening and early detection of LC; 2) availability of experienced thoracic surgeons because of complexity of radical procedures; 3) aggressive en block surgery and adequate lymph node dissection for completeness; 4) precise prediction; 5) adjuvant chemoimmunoradiotherapy for LCP with unfavorable prognosis.
Triangles of Neck and Clinical Correlation by Dr. RIG.pptx
Digital healthcare show - How will Artificial Intelligence in healthcare will impact patient outcomes in the future?
1. How will Artificial Intelligence in
healthcare will impact patient
outcomes in the future?
Dominic Cushnan, Ameet Bakhai, Andy Wilkins
Twitter: #AICommunity
2. The future is here: it is just not
evenly distributed
William Ford Gibson (born 17 March 1948) is an American-Canadian writer
who has been called the "noir prophet" of the cyberpunk subgenre of
science fiction. Gibson coined the term "cyberspace" in his short story
"Burning Chrome" and later popularized the concept in his debut novel,
Neuromancer (1984).
3. As healthcare professionals, what
does this future hold for us and
our patients?
Write on the paper in front of you.
6. INSTITUTE OF NAVAL MEDICINE
Getting evidence in to practice
It took 200 years before the Royal Navy routinely used
lemon juice to prevent scurvy.
First study 1601
7.
8. The internet has changed the way we do …
everything.
1998 was also the year a little company called Google was born,
although back then, it looked a little different than it does now.
9. Social media has taken over.
One of the most influential applications of the internet is social media.
15. The maned sloth, also known as the ai,
is a three-toed sloth that lives only in Brazil.
16. What is AI
AI describes a set of advanced technologies that enable machines to do
highly complex tasks effectively – which would require intelligence if a
person were to perform them.
There is, however, “no standard definition of intelligence” and no
single agreed definition of AI.
In addition, the line between AI and other techniques, such as big data
analytics, can be blurred.
17. AI Types
• Weak AI (narrow AI) – non-sentient machine intelligence, typically
focused on a narrow task (narrow AI).
• Strong AI – (hypothetical) sentient machine (with consciousness and
mind).
• Artificial general intelligence (AGI) – (hypothetical) machine with the
ability to apply intelligence to any problem, rather than just one
specific problem, typically meaning "at least as smart as a typical
human".
• Superintelligence – (hypothetical) artificial intelligence far surpassing
that of the brightest and most gifted human minds.
26. “Despite central government’s enthusiasm for AI,
current applications within the NHS are
piecemeal.”
Reform January 2018
27. Artificial intelligence / digital Health care Impact on professionals
& future of NHS Healthcare
Ameet Bakhai
MBBS, MD, FRCP, FESC
Consultant Cardiologist & Physician
Cardiovascular Research & Development Director
Amore Health Ltd / Royal Free London NHS Trust / Royal National
Orthopeadic Hospital / Barnet CCG
abakhai@gmail.com
28. Patient Access Value Based Care Personalized Digital Health Preventative Medicine Drug Trials and Discovery
data environment is rapidly changing
Healthcare organizations are facing a deluge of rich data that is enabling them to become more efficient, operate
with greater insight and effectiveness, and deliver better service
Advances analytical and computing techniques coupled with the explosion of data in healthcare organizations can help uncover leading clinical practices, shrink research discovery time,
streamline administration, and offer new personalized engagement paradigms at an industrial scale that align people’s decisions and actions in ways that improve outcomes and add value
Sources of the data deluge
Advances in computing power and techniques
Smarter Algorithms Faster Processing Speed Improved Visualization
Patient Centric
Optimal Resource
Structure
Adaptive Organization
* HP Autonomy, Transitioning to a new era of human information, 2013
** Steve Hagan, Big data, cloud computing, spatial databases, 2012
Sensors / DevicesVideosImagesSocial MediaPaper / Text
Documents
EMRsMobile
40-50%
Annual growth in digital data volume*
~9X
of unstructured data vs. structured data by 2020**
62%
Annual growth in unstructured data*
29. Technology – GAME CHANGERS / DISRUPTORS
https://hbr.org/2011/01/reinvent-your-business-before-its-too-late
http://marketingio.com/2016/11/07/martecs-law-the-greatest-management-challenge-of-the-21st-century-chief-marketing-technologist/
31. Deep Learning
Open Access
Google Inc et al.
May 2018
216K Adults
46K Million data
24 hours
AUROC: (predict)
0.93 death
0.85 re-admit
Beat clinical scores
32. 32
Example: Project Genesis
• Scope of Computer Systems
• Clinical
• Power Chart - Orders and results
• Clin Doc - Clinical documentation
• PharmNet - Pharmacy
• FirstNet: Emergency Dept.
• RadNet: Radiology Dept.
• SurgiNet: Operating Room
• Inet: ICU
• Profile - HIM application
• EMPI
• CPOE
• Electronic Record
- Clinical functions by pt. type
- Current clinical
documentation forms
Implementation Readiness
Process Requires 20-24 Months
•People
•Process
•Technology
Implementation Readiness
Major Learning: Realizing clinical benefits of
transformational change is a function of time
33. COGNITIVE LOAD THEORY: WHY USING YOUR ELECTRONIC HEALTH RECORD
IS SO PAINFUL AND HOW TO FIX IT
Michael Zimmerman, MD Temescal Creek Medicine
34.
35.
36. Amongst the Oldest Health Information Technologies
• Original digital health device
• Conceived and designed to solve
a healthcare need, not fill a
market gap
• Strengths:
• Fits into existing lifestyle
• Passive (no patient action
necessary)
• It works really, really well
• Weaknesses to overcome with
new technologies
• Security
• One size does not fit all; QoL
• Expense
• Designed to be replaced – profit at
the price of complications
Oh, and by the way, making a great device is
good business: 2014 WW sales > $6 billion
https://globenewswire.com/news-release/2016/01/07/800112/0/en/Cardiac-Pacemaker-Market-
to-Reach-US-12-85-bn-by-2023-Rising-Geriatric-Population-is-High-impact-Driver-Transparency-
Market-Research.html
40. Care Algorithms / Decision Pathways:
The Slide Towards AI in Healthcare
Useful
• Speed up decision making
• Project sense of normality
• Demonstrate predictability
• Allow forecasting
• Project an evidence base
• Reduce variation in
practice
• Reduce ‘fud’
• Avoids senior input
(GP + AI = Specialist?)
Challenge
• Speed up nature - impatience
• Assume NO human error
• Reduce checks
• Debase impact of issue (MI)
• Generalisability to individual
• Divert individualised care
• Reduce responsibility
• Reduce experience base
(GP – AI = Medical Novice?)
MI = Myocardial Infarction – now only a 72 hr issue
41. When Big Data goes wrong:
UK breast cancer screening IT error
• All women aged 50 to 70 in the UK who are registered with a
GP are automatically invited for breast cancer screening
every three years
• A “computer algorithm failure”, which dated back to 2009,
meant an estimated 450,000 women aged between 68 and
71 were not invited to their final breast screening between
2009 and the start of 2018
• Initial estimates have suggested that between 135 and 270
women may have “had their lives shortened as a result”
• If you don’t get the programming right, systems won’t work
correctly
Available from: https://www.digitalhealth.net/2018/05/14000-women-contact-breast-cancer-helpline/
42. AI: friend or foe?
“What does your organization primarily plan to do in terms of
employees that have been replaced with technology?”
• Retrain them into a new role/area of the organization 34%
• Redeploy within the same area of the organization 42%
• Make them redundant 24%
• Need to carefully consider how AI deployment could affect the
workforce and ensure that the proper ethical checks for autonomous
systems are in place.
• AI will exist to support people in their jobs. For instance, AI will
optimize clinical processes, such as recording patients’ vital signs or
analysing scans and samples, but the doctor will decide the final line
of treatment.
• The purpose of AI will be to augment natural intelligence, and its role
will always be subordinate to the human’s.
Available from: https://www.infosys.com/smart-automation/Documents/ai-healthcare.pdf
43. AI in healthcare: peer-reviewed evidence
PubMed search for clinical trials with the keywords “artificial intelligence” and “NHS” published in the last 5 years
44. Duty of candour: a level playing field?
• Aim of the regulation: to ensure that providers are open and
transparent with people who use services in relation to care and
treatment.
• Contains specific requirements that providers must follow when things
go wrong with care and treatment, including:
• informing people about the incident
• providing reasonable support
• providing truthful information and an apology.
• Providers must promote a culture that encourages candour, openness
and honesty at all levels.
• What about companies who provide healthcare IT solutions?
Available from: http://www.cqc.org.uk/sites/default/files/20150327_duty_of_candour_guidance_final.pdf
45. FDA report
In 2010, 260 HIT reports, 44 injuries, 6 deaths in 2
years – Voluntary reporting system – likely
underreported..
46. Example: Babylon Health to power NHS 111
with ‘AI triage’ bot
https://www.digitalhealth.net/2017/01/babylon-health-to-power-nhs-111-with-ai-triage-bot/
• A chatbot to answer NHS non-emergency inquiries
from more than a million Londoners as a new way
to manage the growing health burden.
• Driven by clinically based algorithms that triage
patients without human intervention based on
reported symptoms.
• Based on the symptoms and its own algorithms,
the app could refer the patient to hospital or
recommend a GP appointment the next day.
• Doctors have already expressed concerns about
the reliance on algorithms and self-reported
symptoms for determining the severity of a
person’s illness. However, ? published evidence...
47. AI has huge potential
Feasibility study of a randomised controlled trial to
investigate the effectiveness of using a humanoid
robot to improve the social skills of children with
autism spectrum disorder (Kaspar RCT): a study
protocol
Mengoni SE, Irvine K, Thakur D, et al. BMJ Open 2017;7:e017376. doi:10.1136/bmjopen-2017-017376
48. Immediate example in cardiology
management?
• Research presented at the 2017 AHA Congress on pairing machine-
learning algorithms with the Apple Watch’s heart-rate sensor and
step counter to predict hypertension or sleep apnoea
• Apple says it’s working on a study with Stanford that will test the
gadget’s ability to detect atrial fibrillation
• There are many theoretical possibilities for how AI could assist us
with managing patients with or at risk from thromboembolic disease
Available from: https://www.wired.com/story/ai-can-help-apple-watch-predict-high-blood-pressure-sleep-apnea/
49. Kintzugi – Japanese Art of Appreciating that which has Broken
– for its wisdom & sacrifice. By Repairing it with Gold.
The NHS may face disruption before it’s evolves to a new model of care.
Schwartz Rounds
- prior Barnet Clinical Lead
- Compassion for the Caregivers
50. Disclosures
Ameet Bakhai, Consultant Cardiologist & Physician / Cardiovascular R&D Director
• I am employed by the NHS, CCG and work with AHSNs – UCL Partners,
Imperial Health
• Founder Amore Health Ltd
• An ambassador for Digital Health London
• I have advised pharma, device, advisory, strategy, health technology
appraisal, government policy, commissioning groups and technology
firms on innovations in healthcare from drugs, devices, diagnostics,
decision pathways to digital technologies
• Past & Present Committees: Research & Development, Governance
and Risk, 18 week Pathway Champion, Medicine Management, D&TC,
Thrombosis, Audit, Clinical Excellence Awards, Physician Associate
Project, Work Experience, Education & Partnership for Cardiac & Stroke
Network, Surrey Heath CCG Board Member, Task Forces in Cardiology
for UCLp & Imperial Health, Horizon Scanning for NICE, End of Life Care
for NHS England – London, Cardiovascular Research North Thames
CRN, Education Standards ABPI
• Studied Decision Analysis Modelling & Health Economics @ Harvard
School of Public Health
Advisor / Lecturer / Appraiser / Committee
- Health technology appraisal groups
- Economic modelling teams
- Pharma, Device & Strategy companies
- IT and Digital Health companies
- ABPI, NICE, UCLp
51. Scope & purpose of report
51
A 10-15 year vision for AI powered
person-centred public healthcare
Andy Wilkins – Report Lead Author
On behalf of the Royal Free Charity
The Digital Healthcare Show
27th June 2018
52. 52
Royal Free North Central London
New models of care
• Integrated care
• Whole person care
• Population health
New capabilities
• New medical
breakthroughs
• New clinical & digital
technologies
Fog of Uncertainty
The challenge – how to invest for the future when so much is
changing?
53. 53
The answer – look beyond the “fog” to shine a light on a vision of the long
term future
54. A 10-15 year vision for person-centred public healthcare
Scope & purpose of report
54
New major report coming soon!!
Report sets out a future that delivers:
1. Transformational improvements in
the nation’s levels of health and
wellbeing
2. A pathway to building a sustainable,
person-centred 21st Century public
health and care system
3. An engine for economic growth and
social renewal
The report describes a vision based on the transformational new capabilities arriving in the next 10-
15 years
55. 55
We rolled forward 6 key trends…
to imagine a world where they
had all “landed” at scale
We asked ourselves:
1. What would health look like for the
individual
2. What would this mean for the future
delivery of health & care?
56. 56
The 21st Century health challenges call for a wider
framing of the healthcare landscape
57. 57
A new generation of sensors will enable revolutionary new
sources of data and possibilities to improve care
58. 58
Real time data will make it possible to dynamically simulate
health
59. 59
A Digital health coach enables always-on personalised care
support
My health
context
Holistic care support
Decision
Support
Integrated
Care Teams
60. 60
The three core elements of the Vision
1. Personalised Health and Wellbeing2. AI Mediated Health Coaching 3. Future Health and Care System
61. 61
As PoC technologies miniaturise and are
combined with AI powered Decision
support systems then:
1. Care becomes more integrated
2. LTC care becomes more health/ life
coaching based and moves into
community settings
3. Digital health coaches manage day to
day and moment to moment support
What impact will this have on the management of chronic
disease?
62. 62
Size of the prize
= better quality of
life + sustainable
healthcare system
64. System Framing
Making prevention and population health a
priority
Integrating prevention, healthcare and social
care into a unified care system
Taking a longer term investment perspective
Public engagement
Making the case for self-management of health
and wellbeing
Acceptance of sensors
Acceptance of sharing of data
Acceptance of a AI powered digital health
coach
New healthcare ecosystem
New data skills and capabilities
New medical and wellbeing skills
Transformational funding
New clinical care models
New funding and governance
models
New clinical organisational
structures
Person-centred data strategy
Radical new innovation
15 Building Blocks – challenges to overcome not reasons to hold
back
65. 65
The next stage in our journey – creating a movement for change!
2. Working with Junior Doctors at Barnet
Hospital on a Story Platform – CPD
opportunity
1. Created a cross sector steering
group to plan the provocation
3. Exploring joint initiatives with the RSM
4. Engaging with Politicians, Healthcare leaders & Industry
on the need for a new vision for 21st Century healthcare
66. 66
We’d love you to be involved…
go to www.beyondthefog.org to stay in touch
67. Snowstorm
Write down one key thing you have learnt or
will think about when you go back to your
organisation?
68. Snowstorm
• Write down one key thing you have learnt or will think
about when you go back to your organisation?
• Screw the paper up
• On the signal, throw your snowball in the air.
69. Snowstorm
• Write down one key thing you have learnt or will think
about when you go back to your organisation?
• Screw the paper up
70. Snowstorm
• Write down one key thing you have learnt or will think
about when you go back to your organisation?
• Screw the paper up
• On the signal, throw your snowball in the air.
• Pick up a snowball and read it the person next to you
71. Snowstorm
• Write down one key thing you have learnt or will think
about when you go back to your organisation?
• Screw the paper up
• On the signal, throw your snowball in the air.
• Pick up a snowball and read it the person next to you
• On your way out please hand the snowballs to us.