AI - Super Hearing for Healthcare Marketers - Making Artificial Intelligence and Machine Learning Practical
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AI - Super Hearing for Healthcare Marketers - Making Artificial Intelligence and Machine Learning Practical
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- Numbers are US alone
- 112,120 frontal-view chest X-ray images individually labeled with up to 14 different thoracic diseases, including pneumonia. (1,2) To estimate radiologist performance, we collect annotations from four practicing academic radiologists on a subset of 420 images from ChestX-ray14. On these 420 images, we measure performance of individual radiologists using the majority vote of other radiologists as ground truth, and similarly measure model performance. (1,2) CheXNet agreed with a majority vote of radiologists more often than those of the individual radiologists. (2) The algorithm now has the highest performance of any work that has come out so far related to the NIH chest X-ray dataset. (2) References: 1. Stanford. ArXiv 2017. https://arxiv.org/pdf/1711.05225.pdf 2. Stanford News Service. https://news.stanford.edu/press-releases/2017/11/15/algorithm-outpernosing-pneumonia/
- Gartner: by 2020, 30% of all Web browsing sessions will be done without a screen or a keyboad.1 Advances in voice recognition technology is driving this paradigm shift. IDC Research Agency: The ubiquitous presence of smartphones supports this trend. By 2020, worldwide shipments of smartphones is predicted to rise to 1.84 billion units.2 References 1. https://www.gartner.com/smarterwithgartner/gartner-predicts-a-virtual-world-of-exponential-change/ 2. https://www.idc.com/getdoc.jsp?containerId=US41515416
- Voice search is a fact of daily life for healthcare professionals and for consumers DRG Digital — 23% of physicians use a voice assistant for professional reasons. 78% of respondents in this segment used the voice assistant to search for information. Physicians search for essential information — drug lookups and dosing; diagnostic, disease, and clinical information. Reference Paging Dr. Siri, DRG Digital. http://www.drgdigital.com/ebooks/paging-dr-siri-physicians-and-the-rise-of-voice-assistants
- Voice search is also a consumer phenomenon The 2016 Meeker Report cites data from Google reporting that 20% of all mobile queries in the United States were made by voice1 References http://www.kpcb.com/internet-trends, slide 46
- The switch to voice-driven search creates an even more daunting challenge for healthcare marketers Voice-driven searches produce 1 search winner, not 10
- November ‘17 WSJ article cites 2.6% error rate — research by Stone Temple. [https://www.stonetemple.com/digital-personal-assistants-test] Collected a set of 5,000 different questions about everyday factual knowledge Google Search answered 74.3% of questions; 97.4% of answers were complete and correct Reference https://www.wsj.com/articles/googles-featured-answers-aim-to-distill-truthbut-often-get-it-wrong-1510847867
- Natural language, AKA, conversational language, is how you ask a question to another person. Challenge 1: Search analysts have to extract context and intent, not just keywords, from marketing data Challenge 2: Different people have different ways of asking the same question Challenges 1 & 2 increase the volume and complexity of data processing required from search analysts Reference: Your Google Assistant is getting better across devices, from Google Home to your phone. https://blog.google/products/assistant/your-assistant-getting-better-on-google-home-and-your-phone/
- For the past couple of years, Google has repeatedly stated that content is ranked on 2 things: Does it help people answer a question? Does it help people complete a task?
- How can marketers prepare for the challenge of voice searches? To appreciate AI’s role in answering this question, we have to review research we executed via human data analysis to define the questions that a brand’s audience needed answered
- This analysis uncovered a valuable insight — a huge answer gap existed Of 110 brand questions for which Google served content, only 4 questions were answered correctly This answer gap represents a major hearing failure Human data analysis uncovered valuable insights, but we asked ourselves: Can we accelerate the discovery of marketing insights?
- This analysis uncovered a valuable insight — a huge answer gap existed Of 110 brand questions for which Google served content, only 4 questions were answered correctly This answer gap represents a major hearing failure Human data analysis uncovered valuable insights, but we asked ourselves: Can we accelerate the discovery of marketing insights?
- Human data analysis limited by Volume of data – approximately 400 hours required to analyze 250,000 searches Number of variable simultaneously evaluated These issues limit humans’ ability to detect trends, especially when signals are low and scattered We sought a technology to overcome these limitations. This brought us to AI.
- Before we discuss our AI solution, let’s first discuss what marketers need to know about AI An AI solution has to be custom built based on the business problem that the solution has to solve There are 3 functional components to an AI solution
- Data is any feedback from any source that can be digitized Data is your intellectual property The brand, NOT the brand’s vendors, must own, store, and control the data Capture ALL your data. Start TODAY if not already doing so
- There are 2 technology components The “engine” provides the functionality that evaluates the data Capabilities vary AI is a composite of many functionalities. NLP and machine learning are 2 common functionalities NLP enables AI to understand the meaning and context of human language Machine learning enables the AI solution to self-improve The interface delivers the data to the engine Must be custom built
- Strategic decision #1: Select the optimal AI engine based on the business problem Strategic decision #2: Instruct the AI engine how to analyze the data Feed the engine training data If the analysis involves common knowledge, feed it Wikipedia If the analysis involves human disease, feed it medical journals Strategic decision #3: Create a training strategy for machine learning
- Human data analysis exceed sales 1 month early How can we do better Apply learnings from previous discussion to Zicam – on a limited budget
- “Challenge: How do we help the brand communicate even more authentically” “Solution: Analyze 250,000 rows of data”
- Analyzed 250,000 to define how audiences were asking about Zicam cost, use, and active ingredient AI detected small number of Spanish inquiries Unexpected given English-only campaign based in U.S. Just for fun, analyzed data for OPPOSITE of how people ask about Zicam cost using algorithms that translated non-English languages
- The multilingual algorithm identified 147 Spanish inquiries, nearly all relevant to the brand Data corroborated client data generated independently Client developed new demographic-specific campaign strategy
- The magic of AI and machine learning does not arise from the technology or the data It arises from our human curiosity and desire to look beyond the obvious AI requires a high level of technical and analytical expertise; also demands a playfulness, almost a child’s mind, to reimagine the world when we can look past the obvious and identify patterns and insights that historically have remained hidden because they had a very low signal-to-noise ratio. These anomalies were beyond human identification, but are clearly obvious to a machine
- Essential take-aways for marketers Data is the queen – treat it accordingly Every component of an AI solution must reflect the business problem that the solution must solve It takes a team the right team to implement an effective AI solution