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DESCRIPTION:We are in the midst of an artificial intelligence (AI) revoluti
 on. It may seem that AI development is recent\, but the term AI was coined
  (1955) not too long after the invention of computing devices. However\, t
 he idea that a machine can behave like a human being is even older. The te
 rm “automaton” was used for it. Please see:\n\n- [https://www.tableau.
 com/data-insights/ai/historyLinks to an external site.](https://www.tablea
 u.com/data-insights/ai/history)\n- [https://en.wikipedia.org/wiki/Logic_Th
 eoristLinks to an external site.](https://en.wikipedia.org/wiki/Logic_Theo
 rist)\n\nWhat has changed\, though\, in recent times\, is the amount of co
 mputation that can be performed. GPUs have made tremendous progress\, and 
 it is now possible to do heavy computation with very large amount of memor
 y in real time.\n\nThe terms AI and machine learning (ML) are normally use
 d interchangeably though there are differences. We will discuss what is th
 e difference between them. While AI tries to mimic the human behavior\, ML
  is a set of statistical tools to look at the data to find patterns withou
 t direct instructions. In some ways\, it is a subset of AI. We will dig a 
 little deeper into how AI works. This will give us a better understanding 
 of what kind of problems it can solve effectively and where it should be a
 voided\, or one needs enhanced or use better tools.\n\nNeural networks are
  normally used to perform AI calculations: Deep Neural Networks (DNN)\, Co
 nvolutional Neural Networks (CNN)\, Recurrent Neural Networks (RNN) are so
 me examples. They have their origin in the way the human brain works.\n\nW
 e will discuss the biological basis of neural networks and discuss how the
 se networks are implemented. We will focus on concepts and will develop in
 tuition about how they are trained. We will also see how and when a certai
 n type of network can be used.\n\nWe will go over some caution around data
  analysis and what to watch out for when using AI.\n\nIn this lecture\, we
  will take this discussion further. AI/ML is finding more frequent use in 
 medical devices especially wearables. We will briefly discuss how exactly 
 same data can result in very different outcomes and conclusions. We will t
 hen discuss differences between making and shipping a generic consumer dev
 ice and a medical device - why medical devices cost more both in terms of 
 time and money. Finally\, we will consider a (imaginary) medical device an
 d take it through the process of designing\, validating\, and getting it t
 hrough the FDA for pre-market approval. In this respect\, we will review d
 ifferent types of FDA approvals\, and strategies for it.\n\nSpeaker(s): Dr
  Md Usman\n\nAgenda: \n- 14:00 to 14:10 PM : Welcome to IEEE OC EMBS and g
 eneral introduction\n- 14:10 to 14:20 PM : Introduction by Gora Datta\, FH
 L7\n- 14:20 to 15:50 PM: Expert Lecture by Dr Md Usman\n- The difference b
 etween AI and ML\,\n- Biological basis of some AI networks\,\n- Basics of 
 how an AI model is trained and what does it mean\,\n- Advantages and disad
 vantages of using AI – why should we use it\, and what does it measure a
 nyways?\n- What are large language models (LLM)\, and their usefulness in 
 medicine and medical devices\n- Will understand some examples of using AI\
 , and\n- Know when to use AI for health measurements.\n\n- 15:55 to 16:00 
 PM : Wrap Up\n\nRoom: Emerald Cove\, Bldg: Bealle Applied Innovation\, 527
 0 California Ave\, Gora Datta\, Irvine\, California\, United States\, 9261
 7\, Virtual: https://events.vtools.ieee.org/m/507676
LOCATION:Room: Emerald Cove\, Bldg: Bealle Applied Innovation\, 5270 Califo
 rnia Ave\, Gora Datta\, Irvine\, California\, United States\, 92617\, Virt
 ual: https://events.vtools.ieee.org/m/507676
ORGANIZER:goradatta@ieee.org
SEQUENCE:23
SUMMARY:Lecture#2: DEVICE INFORMATICS - applications in Public Health
URL;VALUE=URI:https://events.vtools.ieee.org/m/507676
X-ALT-DESC:Description: &lt;br /&gt;&lt;p&gt;We are in the midst of an artificial intel
 ligence (AI) revolution. It may seem that AI development is recent\, but t
 he term AI was coined (1955) not too long after the invention of computing
  devices. However\, the idea that a machine can behave like a human being 
 is even older. The term &amp;ldquo\;automaton&amp;rdquo\; was used for it. Please 
 see:&lt;/p&gt;\n&lt;ul&gt;\n&lt;li&gt;&lt;a class=&quot;external&quot; href=&quot;https://www.tableau.com/data
 -insights/ai/history&quot; target=&quot;_blank&quot; rel=&quot;noreferrer noopener&quot;&gt;https://ww
 w.tableau.com/data-insights/ai/history&lt;span class=&quot;external_link_icon&quot; rol
 e=&quot;presentation&quot;&gt;&lt;span class=&quot;screenreader-only&quot;&gt;Links to an external site
 .&lt;/span&gt;&lt;/span&gt;&lt;/a&gt;&lt;/li&gt;\n&lt;li&gt;&lt;a class=&quot;external&quot; href=&quot;https://en.wikiped
 ia.org/wiki/Logic_Theorist&quot; target=&quot;_blank&quot; rel=&quot;noreferrer noopener&quot;&gt;http
 s://en.wikipedia.org/wiki/Logic_Theorist&lt;span class=&quot;external_link_icon&quot; r
 ole=&quot;presentation&quot;&gt;&lt;span class=&quot;screenreader-only&quot;&gt;Links to an external si
 te.&lt;/span&gt;&lt;/span&gt;&lt;/a&gt;&lt;/li&gt;\n&lt;/ul&gt;\n&lt;p&gt;What has changed\, though\, in recen
 t times\, is the amount of computation that can be performed. GPUs have ma
 de tremendous progress\, and it is now possible to do heavy computation wi
 th very large amount of memory in real time.&lt;/p&gt;\n&lt;p&gt;The terms AI and mach
 ine learning (ML) are normally used interchangeably though there are diffe
 rences. We will discuss what is the difference between them.&amp;nbsp\; While 
 AI tries to mimic the human behavior\, ML is a set of statistical tools to
  look at the data to find patterns without direct instructions. In some wa
 ys\, it is a subset of AI. We will dig a little deeper into how AI works. 
 This will give us a better understanding of what kind of problems it can s
 olve effectively and where it should be avoided\, or one needs enhanced or
  use better tools.&lt;/p&gt;\n&lt;p&gt;Neural networks are normally used to perform AI
  calculations: Deep Neural Networks (DNN)\, Convolutional Neural Networks 
 (CNN)\, Recurrent Neural Networks (RNN) are some examples. They have their
  origin in the way the human brain works.&lt;/p&gt;\n&lt;p&gt;We will discuss the biol
 ogical basis of neural networks and discuss how these networks are impleme
 nted. We will focus on concepts and will develop intuition about how they 
 are trained. We will also see how and when a certain type of network can b
 e used.&lt;/p&gt;\n&lt;p&gt;We will go over some caution around data analysis and what
  to watch out for when using AI.&lt;/p&gt;\n&lt;p&gt;&amp;nbsp\;&lt;/p&gt;\n&lt;p class=&quot;MsoNormal&quot;
 &gt;&lt;strong&gt;&lt;span style=&quot;background-color: rgb(255\, 255\, 255)\; font-size: 
 14pt\;&quot;&gt;In this lecture\, we will take this discussion further. AI/ML is f
 inding more frequent use in medical devices especially wearables. We will 
 briefly discuss how exactly same data can result in very different outcome
 s and conclusions. We will then discuss differences between making and shi
 pping a generic consumer device and a medical device - why medical devices
  cost more both in terms of time and money. Finally\, we will consider a (
 imaginary) medical device and take it through the process of designing\, v
 alidating\, and getting it through the FDA for pre-market approval. In thi
 s respect\, we will review different types of FDA approvals\, and strategi
 es for it.&lt;/span&gt;&lt;/strong&gt;&lt;/p&gt;\n&lt;h1&gt;&amp;nbsp\;&lt;/h1&gt;\n&lt;p class=&quot;MsoNormal&quot; sty
 le=&quot;margin: 6.0pt 0in 0in 0in\;&quot;&gt;&amp;nbsp\;&lt;/p&gt;&lt;br /&gt;&lt;br /&gt;Agenda: &lt;br /&gt;&lt;ul&gt;
 \n&lt;li&gt;14:00 to 14:10 PM : Welcome to IEEE OC EMBS and general introduction
 &lt;/li&gt;\n&lt;li&gt;14:10 to 14:20 PM : Introduction by Gora Datta\, FHL7&lt;/li&gt;\n&lt;li
  aria-level=&quot;1&quot;&gt;14:20 to 15:50 PM: Expert Lecture by Dr Md Usman\n&lt;ul&gt;\n&lt;l
 i&gt;&lt;s&gt;The difference between AI and ML\,&lt;/s&gt;&lt;/li&gt;\n&lt;li&gt;&lt;s&gt;Biological basis 
 of some AI networks\,&lt;/s&gt;&lt;/li&gt;\n&lt;li&gt;&lt;s&gt;Basics of how an AI model is traine
 d and what does it mean\,&lt;/s&gt;&lt;/li&gt;\n&lt;li&gt;&lt;s&gt;Advantages and disadvantages of
  using AI &amp;ndash\; why should we use it\, and what does it measure anyways
 ?&lt;/s&gt;&lt;/li&gt;\n&lt;li&gt;What are large language models (LLM)\, and their usefulnes
 s in medicine and medical devices&lt;/li&gt;\n&lt;li&gt;Will understand some examples 
 of using AI\, and&lt;/li&gt;\n&lt;li&gt;Know when to use AI for health measurements.&lt;/
 li&gt;\n&lt;/ul&gt;\n&lt;/li&gt;\n&lt;li aria-level=&quot;1&quot;&gt;15:55 to 16:00 PM : Wrap Up&lt;/li&gt;\n&lt;/
 ul&gt;
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