<?xml version="1.0"?>
<rss version="2.0">
   <channel>
      <title>Current Innovation Project by </title>
      <link>https://padlet.com/ccorales1/nbtg8af03pfrw59i</link>
      <description></description>
      <language>en-us</language>
      <pubDate>2024-09-13 15:12:22 UTC</pubDate>
      <lastBuildDate>2024-09-16 19:17:55 UTC</lastBuildDate>
      <webMaster>hello@padlet.com</webMaster>
      <image>
         <url></url>
      </image>
      <item>
         <title>Mia, AI saving lives through early breast detection </title>
         <author>ccorales1</author>
         <link>https://padlet.com/ccorales1/nbtg8af03pfrw59i/wish/3118838678</link>
         <description><![CDATA[]]></description>
         <enclosure url="https://pixabay.com/get/g86924749af78c787527a0a192c53235d66835028a423259b439e9fa52e1604ef147ea63184e87bdcd4006eca686a511f.jpg" />
         <pubDate>2024-09-13 15:26:39 UTC</pubDate>
         <guid>https://padlet.com/ccorales1/nbtg8af03pfrw59i/wish/3118838678</guid>
      </item>
      <item>
         <title>Source Details</title>
         <author>ccorales1</author>
         <link>https://padlet.com/ccorales1/nbtg8af03pfrw59i/wish/3118838752</link>
         <description><![CDATA[<p>Title:</p><p>Prospective implementation of AI-assisted screen reading to improve early detection of breast cancer.</p><p><br></p><p>Authors &amp; Affiliations:</p><ol><li><p><strong>Kheiron Medical Technologies, London, UK</strong></p><p>Annie Y. Ng,&nbsp;Cary J. G. Oberije,&nbsp;Edit Karpati,&nbsp;Georgia Fox,&nbsp;Ben Glocker&nbsp;&amp;&nbsp;Peter D. Kecskemethy</p></li><li><p><strong>MaMMa Egészségügyi Zrt., Budapest, Hungary</strong></p><p>Éva Ambrózay,&nbsp;Endre Szabó&nbsp;&amp;&nbsp;Orsolya Serfőző</p></li><li><p><strong>Department of Computing, Imperial College London, London, UK</strong></p><p>Ben Glocker</p></li><li><p><strong>University of California, Davis, Davis, CA, USA</strong></p><p>Elizabeth A. Morris</p></li><li><p><strong>Duna Medical Center, Budapest, Hungary</strong></p><p>Gábor Forrai</p><p><br></p></li></ol><p>Credible Source:</p><p> <em>Nature Medicine</em> Journal </p><p><br></p>]]></description>
         <enclosure url="" />
         <pubDate>2024-09-13 15:26:43 UTC</pubDate>
         <guid>https://padlet.com/ccorales1/nbtg8af03pfrw59i/wish/3118838752</guid>
      </item>
      <item>
         <title>Date Published</title>
         <author>ccorales1</author>
         <link>https://padlet.com/ccorales1/nbtg8af03pfrw59i/wish/3118838910</link>
         <description><![CDATA[<p>November 16, 2023</p>]]></description>
         <enclosure url="" />
         <pubDate>2024-09-13 15:26:46 UTC</pubDate>
         <guid>https://padlet.com/ccorales1/nbtg8af03pfrw59i/wish/3118838910</guid>
      </item>
      <item>
         <title>Summary of Mia</title>
         <author>ccorales1</author>
         <link>https://padlet.com/ccorales1/nbtg8af03pfrw59i/wish/3118838992</link>
         <description><![CDATA[<p>According to the Nature Medicine Journal, the current method of cancer screening involves 2 radiologists who read the breast cancer screening images and determine if the woman should be "recalled". A 3rd radiologist called an "arbitrator" makes the decision if there is disagreement among the 2 radiologists. The research showed implementation of the AI-assisted additional-reader workflow resulted in 24 more cancer cases detected. Data suggests that using AI as an additional reader can improve the early detection of breast cancer with relevant prognostic features, with minimal to no unnecessary recalls. Although it requires additional reads, the higher positive predictive value (PPV) suggests that it can increase screening effectiveness. </p>]]></description>
         <enclosure url="https://upload.wikimedia.org/wikipedia/commons/f/f1/Blausen_0628_Mammogram.png" />
         <pubDate>2024-09-13 15:26:49 UTC</pubDate>
         <guid>https://padlet.com/ccorales1/nbtg8af03pfrw59i/wish/3118838992</guid>
      </item>
      <item>
         <title>Link/Video or Photos</title>
         <author>ccorales1</author>
         <link>https://padlet.com/ccorales1/nbtg8af03pfrw59i/wish/3118839088</link>
         <description><![CDATA[<p><a rel="noopener noreferrer nofollow" href="https://doi.org/10.1038/s41591-023-02625-9">https://doi.org/10.1038/s41591-023-02625-9</a></p>]]></description>
         <enclosure url="https://doi.org/10.1038/s41591-023-02625-9" />
         <pubDate>2024-09-13 15:26:52 UTC</pubDate>
         <guid>https://padlet.com/ccorales1/nbtg8af03pfrw59i/wish/3118839088</guid>
      </item>
      <item>
         <title>Pros </title>
         <author>ccorales1</author>
         <link>https://padlet.com/ccorales1/nbtg8af03pfrw59i/wish/3118839181</link>
         <description><![CDATA[<p>Large-scale retrospective studies of the same AI system used in this assessment have demonstrated that AI as an independent second reader can offer:</p><p>-up to 45% workload savings</p><p>-offsetting the 3–11% additional arbitration reads (1–6% additional overall reading workload) for the AI-assisted additional-reader workflow while providing the benefit of increased cancer detections</p><p>-minimal to no unnecessary recalls</p><p><br></p><p><br></p>]]></description>
         <enclosure url="" />
         <pubDate>2024-09-13 15:26:56 UTC</pubDate>
         <guid>https://padlet.com/ccorales1/nbtg8af03pfrw59i/wish/3118839181</guid>
      </item>
      <item>
         <title>Opinion and Rationale</title>
         <author>ccorales1</author>
         <link>https://padlet.com/ccorales1/nbtg8af03pfrw59i/wish/3118839224</link>
         <description><![CDATA[<p>In my opinion, AI assisted mammography reading is a helpful tool that can help identify early stages of breast cancer. Computers have the ability to identify microscope changes that the human eye cannot. There is more research needed to ensure accuracy, but the current data is promising. </p>]]></description>
         <enclosure url="" />
         <pubDate>2024-09-13 15:26:58 UTC</pubDate>
         <guid>https://padlet.com/ccorales1/nbtg8af03pfrw59i/wish/3118839224</guid>
      </item>
      <item>
         <title>Using Strengths</title>
         <author>ccorales1</author>
         <link>https://padlet.com/ccorales1/nbtg8af03pfrw59i/wish/3118839266</link>
         <description><![CDATA[<p>My 5 strengths and what I will bring to the team:</p><p>Relator- caring, trusting and honest</p><p>Learner- catches on quickly, wants to consistently improve</p><p>Achiever- work hard, strong work ethic</p><p>Analytical- thorough, thinks things through</p><p>Responsibility- committed, accountable, independent</p><p><br></p><p>Teams/ Rationale</p><p>Arranger- (Abraham) flexibility, organization, aligns and realigns tasks to find most productive configuration possible. This will offset "achiever" tendencies, being too concentrated on work. </p><p><br></p><p>Includer- (Isabelle) accepting/inclusive of others. Will help offset "relator" traits of playing favorites and having an inner circle. </p><p><br></p><p>Focus- (Fernando) prioritize then act, goal setter and goal getter, disciplined. Will help offset "learner" know it all, lacks focus on results </p><p><br></p><p>Discipline- (Daniel) highly productive and accurate because of ability to structure. Will offset "responsibility" micromanager, obsessive, takes on too much.</p><p><br></p><p>Context- (Carlos) Can leverage knowledge of the past. Help offset "analytical" never satisfied with answer and asks too many questions.</p>]]></description>
         <enclosure url="https://pixabay.com/get/g0bc234887a89e606c8a3db27673274635eabf4d12b6b2a37ab10eb381f0b3ceff7706bab4c8c77a23e38d6f177197b83.jpg" />
         <pubDate>2024-09-13 15:27:01 UTC</pubDate>
         <guid>https://padlet.com/ccorales1/nbtg8af03pfrw59i/wish/3118839266</guid>
      </item>
      <item>
         <title>References</title>
         <author>ccorales1</author>
         <link>https://padlet.com/ccorales1/nbtg8af03pfrw59i/wish/3118839308</link>
         <description><![CDATA[<p>Ng, A.Y., Oberije, C.J.G., Ambrózay, É. <em>et al.</em> (2023). Prospective implementation of AI-assisted screen reading to improve early detection of breast cancer. <em>Nat Med</em> <strong>29</strong>, 3044–3049. <a rel="noopener noreferrer nofollow" href="https://doi.org/10.1038/s41591-023-02625-9">https://doi.org/10.1038/s41591-023-02625-9</a></p>]]></description>
         <enclosure url="" />
         <pubDate>2024-09-13 15:27:04 UTC</pubDate>
         <guid>https://padlet.com/ccorales1/nbtg8af03pfrw59i/wish/3118839308</guid>
      </item>
      <item>
         <title>Cons</title>
         <author>ccorales1</author>
         <link>https://padlet.com/ccorales1/nbtg8af03pfrw59i/wish/3119308612</link>
         <description><![CDATA[<p>Evaluations of real-world performance of AI integrated into live clinical workflows have been limited to date. However, the current data available seems to be very promising.  </p>]]></description>
         <enclosure url="" />
         <pubDate>2024-09-14 01:24:04 UTC</pubDate>
         <guid>https://padlet.com/ccorales1/nbtg8af03pfrw59i/wish/3119308612</guid>
      </item>
   </channel>
</rss>
