Artifical Intelligence in Occupational Health
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What does Artificial Intelligence mean and what are the challenges for healthcare and occupational health in this changing work environment.
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The Application of Clinical Systems to the Study of Traumatic Brain Injury
G.G.
Walden University
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The Application of Clinical Systems to the Study of Traumatic Brain Injury
An area of research that has always sparked a keen interest in me revolves around rehabilitation medicine. I used to volunteer at TIRR Memorial Hermann, a nationally known rehab facility that deals with patients in recovery from significant injuries. Their patient population includes persons recovering from a stroke, spinal cord injury, traumatic brain injury, and a multitude of other less common illnesses. Because of that passion, I am interested in how nursing technology has evolved to help patients recovering from traumatic brain injuries (TBI). This paper will summarize a brief literature search of five articles that apply clinical systems to TBI recovery.
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In the article “The Aggressiveness of Neurotrauma Practitioners and the Influence of the IMPACT Prognostic Calculator,” researchers investigated how effective a prognosis calculator is improving outcomes for TBI patients. 154 medical professionals responded to a survey that specifically questioned them regarding the usefulness and effectiveness of the IMPACT prognosis calculator. The prognosis calculator is a clinical system that was created to assist with care planning for TBI patients. The calculator is supposed to provide an accurate estimate of the future prognosis of the patient so that doctors can know whether to use aggressive treatment strategies or not. Survey responses were collected using a research electronic data capture system and the responses were statistically analyzed using SPSS software (Letsinger, Rommel, Hirschi, Nirula, & Hawryluk, 2017). Although the IMPACT system is the most significant technological advances in modern TBI care, the results of this article reveal that physicians are not properly aware of the capabilities of this software (Lestinger et. al., 2017). Unfortunately, more medical professionals use it as a communication tool more than anything else.
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MK PRESENTATION.pptx
Artificial intelligence is being used increasingly in health care to improve outcomes. It can help detect diseases like cancer more accurately, review medical images much faster than humans, and provide personalized treatment recommendations. AI systems analyze large amounts of medical data to support clinical decision making. Chatbots and digital consultations using AI can provide medical advice by comparing symptoms to illnesses. Machine learning algorithms also help with tasks like medication management and molecular epidemiology research. AI shows promise in improving health globally by making better use of data and resources.
Frankie Rybicki slide set for Deep Learning in Radiology / Medicine
These are my #AI slides for medical deep learning using #radiology and medical imaging examples. Please use them & modify to teach your own group about medical AI.
2013 dec 26_bgu_israel_seminar_l_luciano
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Beyond Proofs of Concept for Biomedical AI
This document discusses challenges with applying machine learning and AI to healthcare and biomedicine. It summarizes that while AI promises improvements, many projects fail to deliver due to issues like focusing on the wrong problems, lack of data, and lack of collaboration between fields. It advocates for approaches like validating and reproducing results, ensuring interpretability, collaborating across expertise, and focusing on incremental improvements rather than novel methods alone.
1 1 Abstract—With the advent of the technologic
1
1
Abstract—With the advent of the technological world, the
technology is getting more and more advanced day-by-
day. Artificial Intelligence (AI) can possibly affect pretty
much every part of medical care, from identification to
forecast and anticipation. The appropriation of new
advances in medical services, nonetheless, slacks far
behind the rise of new advances. An elementary
understanding of developing Artificial Intelligence
proceedings can be basic though wellbeing couldn't care
less experts. These advancements incorporate master
frameworks, mechanical cycle robotization, regular
language preparing, Artificial Intelligence, and deepest
understanding. In the research article, different
technologies have been derived for the detection of
different health diseases. First of all, background
knowledge has been taken under consideration. After
that, diseases like Diabetes, Alzheimer’s disease and
health disease have been discussed. It has been evaluated
that technologies are providing extremely efficient results
with higher level of accuracy which shows that the
discussed technologies are contributing at their best level.
The proposed methods for the discussed diseases in
different research articles have also been evaluated and
highlighted. Every technology has its own benefits. The
proposed article illustrate that how Artificial Intelligence
is contributing in healthcare department and in the
detection of different health diseases.
Index Terms— Expert System, Decision making
Support, Artificial Intelligence, Clinical Decision Support
System, Magnetic Resonance Imaging (MRI), Alzheimer’s
Disease
I. INTRODUCTION
A. Artificial Intelligence
Artificial intelligence is how different machines exhibit
intelligence compared to natural intelligence used by different
humans and animals. In simple words, the theory related to
the growth of computer systems to perform tasks usually
needs human intelligence, for instance, visual perceptions,
decision making, translation of languages, and speed
recognition (Fei Jang, 2017). It is known as a digital
computer's capability or called a computer-controlled robot to
execute tasks usually connected with intelligence. This term
AI is applied to those projects related to developing systems
bestowed with factors of human or intellectual processes, for
example, the ability to reason, generalizing, abstracting, learn
from past experiences, or to discover meaning. In the 1940s,
digital computers evolved and came into existence, so from
1940, since now, computers are designed to perform
complicated and complex tasks, for instance, working on
advanced proofs and theorems from mathematical portions as
well as playing chess. Despite continued advances in the
speed of computer processing and memory capacity still, there
is a gap in programming that they cannot be as flexible as
human beings. This system is ...
1 1 Abstract—With the advent of the technologic
1
1
Abstract—With the advent of the technological world, the
technology is getting more and more advanced day-by-
day. Artificial Intelligence (AI) can possibly affect pretty
much every part of medical care, from identification to
forecast and anticipation. The appropriation of new
advances in medical services, nonetheless, slacks far
behind the rise of new advances. An elementary
understanding of developing Artificial Intelligence
proceedings can be basic though wellbeing couldn't care
less experts. These advancements incorporate master
frameworks, mechanical cycle robotization, regular
language preparing, Artificial Intelligence, and deepest
understanding. In the research article, different
technologies have been derived for the detection of
different health diseases. First of all, background
knowledge has been taken under consideration. After
that, diseases like Diabetes, Alzheimer’s disease and
health disease have been discussed. It has been evaluated
that technologies are providing extremely efficient results
with higher level of accuracy which shows that the
discussed technologies are contributing at their best level.
The proposed methods for the discussed diseases in
different research articles have also been evaluated and
highlighted. Every technology has its own benefits. The
proposed article illustrate that how Artificial Intelligence
is contributing in healthcare department and in the
detection of different health diseases.
Index Terms— Expert System, Decision making
Support, Artificial Intelligence, Clinical Decision Support
System, Magnetic Resonance Imaging (MRI), Alzheimer’s
Disease
I. INTRODUCTION
A. Artificial Intelligence
Artificial intelligence is how different machines exhibit
intelligence compared to natural intelligence used by different
humans and animals. In simple words, the theory related to
the growth of computer systems to perform tasks usually
needs human intelligence, for instance, visual perceptions,
decision making, translation of languages, and speed
recognition (Fei Jang, 2017). It is known as a digital
computer's capability or called a computer-controlled robot to
execute tasks usually connected with intelligence. This term
AI is applied to those projects related to developing systems
bestowed with factors of human or intellectual processes, for
example, the ability to reason, generalizing, abstracting, learn
from past experiences, or to discover meaning. In the 1940s,
digital computers evolved and came into existence, so from
1940, since now, computers are designed to perform
complicated and complex tasks, for instance, working on
advanced proofs and theorems from mathematical portions as
well as playing chess. Despite continued advances in the
speed of computer processing and memory capacity still, there
is a gap in programming that they cannot be as flexible as
human beings. This system is ...
1 1 abstract—with the advent of the technologic
The document discusses how artificial intelligence is contributing to healthcare, particularly in the diagnosis of diseases like diabetes, Alzheimer's, and heart disease. It provides background on AI and discusses how techniques like expert systems, machine learning, and medical imaging are being used to more accurately diagnose diseases. Segmentation of MRI images through techniques like clustering and edge detection have proven useful for detecting Alzheimer's disease. Electronic health records also provide useful health information for treating diseases.
Digital Health Transformation (March 16, 2019)
This document discusses digital health transformation and the concept of a "smart hospital". It begins by looking at various technologies like artificial intelligence, cloud computing, the internet of things, and how they are impacting healthcare. It then defines what a smart hospital is, differentiating it from just a digital or paperless hospital. Specifically, a smart hospital leverages health information technology to improve the quality of care by making it safer, more timely, effective, patient-centered and efficient. The document outlines how technologies like electronic health records, computerized physician order entry, and clinical decision support systems can help hospitals achieve these goals and move towards becoming truly smart.
Digital Health 101 for Hospital Executives (October 4, 2021)
Presented at the Healthcare CEO50 Certificate Program, School of Hospital Management, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand on October 4, 2021
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Artifical Intelligence in Occupational Health
- 1. Artificial Intelligence in Occupational Health Lauri Muhonen Chief Digital Officer Lääkärikeskus Aava 5.6.2018
- 2. Goals 1. Understand what is the definition of Artifical Intelligence? 2. What natural changes are happening in work environment? 3. How can AI help to achieve best possible Occupational Health and what are the challenges?
- 3. What is AI?
- 4. ”Artificial Intelligence is a device that perceives its environment and takes actions that maximize its chance of successfully achieving its goal.” Source: Computational Intelligence: A Logical Approach Oxford University Press / Poole, Mackworth, Goebel (1998)
- 5. Machine Learning Natural Language Processing Knowledge reasoning Planning Computer vision Robotics
- 6. Occam’s razor: “The simplest theory that explains the data is the likeliest” Supervised Learning • Support Vector Machines • Nearest Neighbor method Unsupervised Learning • Clustering & anomaly detection • Neural networks
- 7. Work life is changing
- 8. Communication skills and employee motivation are more crucial than ever as the importance of physical location is vanishing.
- 9. Shortage of work force will enforce the importance of employee wellbeing as a competitive factor
- 10. Self-awareness of environment and own wellbeing is increasing
- 11. Worklife is integral part of identity and need to provide meaning
- 12. What does this mean in Occupational Health?
- 13. Use of health data will significatly increase the quality of predictive healthcare in Occupational Health.
- 14. 16,7 Zetabytes / year 1 ZB =1 000 000 000 000 GB Source: Forbes.com
- 15. Artifical Intelligence: 92 % correct Pathologist: 96 % correct Combined: 99,5 % Source: Artificial Intelligence (AI) Improves Breast Cancer Diagnosis Tissuepathology / Dr. Keith J. Kaplan (2016)
- 16. Predictive Occupational Health = Eyes towards future - Weak signal detection
- 17. Weak signal detection Hit False alarm Miss Correct reject Doctor says ”YES” Doctor says ”NO” diagnose present diagnose not present Source: Signal Detection Theory New York University, Department of Psychology / Dr. David Heeger (2006) Hit False alarm Miss Correct reject Doctor says ”YES” Doctor says ”NO” diagnose present diagnose not present
- 18. Weak signal detection Source: Signal Detection Theory New York University, Department of Psychology / Dr. David Heeger (2006) Shifting criteria changes the probability of hits vs. false alarms With more data, noise gets lower and overlap of probabilities decreases
- 19. AI does not replace the need for physical contact or empathy.
- 20. Top 5 challenges of AI in healthcare
- 21. New research updates the interpretation of health data2. Health data is scattered around different solutions and are mostly non-structured3. Systematic errors are critical in healthcare4. Privacy of individual’s data is crucial5. Health data is complex and variables fluxuating Unsupervised learning1.
- 22. Looking forward of building a better health together.