Two quick questions: Question 1: Are you developing AI for the medical sector❓ Question 2: Do you have a spare 8 minutes❓ If the answer to the above is yes, then you should definitely read our recent blog article titled: "What Do Doctors Really Think About AI in Medicine?" Here are 5 reasons why it's worth your time: 👉 Reason 1: Because it's about what doctors think about artificial intelligence. If you're implementing this kind of technology, it's worth knowing what doctors think about AI - spoiler alert: yes, they have specific and legitimate concerns. 👉 Reason 2: It contains insights, statistics, and indicators. Our article is not a marketing-type, GPT-generated fairy tale about the hypothetical application of AI in medicine. It is written by a human and contains concrete, real data, statistics and opinions – always sourced. 👉 Reason 3: You will learn what to avoid when designing your product. Some pilot implementations, despite their positive impact on healthcare, have not been well received by physicians. Why is that? Because the opinions of physicians, the end users, were ignored in the concept phase. Read our article and learn from the mistakes of others, not your own - it's cheaper that way. 👉 Reason 4: Learn how to promote your product to avoid negative positioning Many in the medical field have strong views on AI in healthcare, thanks to the growing attention it gets. These views can make it hard for AI-based products to be accepted. Our recent article looks at these biases closely, giving you the information you need to address people's concerns. 👉 Reason 5: You will learn what doctors expect from such tools. We have dedicated an entire chapter to what doctors want from AI tools and how to increase the likelihood that your product will be accepted. It’s the most important part. If you can't read the whole thing, at least read this part. ⭐ Bonus point: the article is short, you can read it in less than 10 minutes ;) Link to the article is in the first comment 🙂
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Director of Product Management & Product Marketing ● Expert in Product Lifecycle, Go-to-market strategy, and Ecosystem & Partner Development ● 15 Years Building High-Performing Teams
One of the biggest advantages of #AI is data analysis and that's especially true when applied to the healthcare industry. Over the course of several years, a medical file could have hundreds if not thousands of data points including height, weight, family history, etc. that AI can analyze to generate better assessments of a person's current health and future risk. This article from Fast Company provides helpful insight into how machine learning can be leveraged to improve both patient care and the development of potentially lifesaving treatments. https://lnkd.in/gAkWGq_R
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Passionate Marketing & Communication Enthusiast | Driving Success as Head of Marketing, T-Systems North America | Empowering Future Leaders as University Professor
Healthcare digital transformation is here, but many are still wondering how artificial intelligence (AI) will play a role in it. There are two paths for AI to take - automation or augmentation. Automation can free up doctors' time, while augmentation can enhance their capabilities. What are your thoughts? #AI #Automation #DigitalTransformation https://lnkd.in/gqVw9zqp
AI is creating a complex landscape for healthcare executives
healthcarefinancenews.com
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I just published "Pushing the Limits: Google’s Med-PaLM 2 is Transforming AI in Healthcare." The advancement of artificial intelligence (AI) technology has fundamentally altered numerous sectors, and healthcare is no exception. One pivotal stride in this direction has been the development of AI systems that can provide accurate answers to medical questions, emulating the performance of skilled physicians. A revolutionary leap in this arena is the development of Med-PaLM 2 by Google DeepMind, an enhanced AI model designed to answer complex medical queries accurately. https://lnkd.in/gpb_c2kk #biomedicalscience #healthcare #ai #artificialintelligence
Pushing the Limits: Google’s Med-PaLM 2 is Transforming AI in Healthcare.
medium.com
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Healthcare digital transformation is here, but many are still wondering how artificial intelligence (AI) will play a role in it. There are two paths for AI to take - automation or augmentation. Automation can free up doctors' time, while augmentation can enhance their capabilities. #AI #Automation #DigitalTransformation https://lnkd.in/evSpSw8U
AI is creating a complex landscape for healthcare executives
healthcarefinancenews.com
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New #AI tools continue to make headlines, but the focus should be on the data used to develop and train #LLMs. In this blog, our Senior Medical Director, Mike Mbagwu, MD, discusses the importance of human expertise when using healthcare data to power algorithms: https://hubs.la/Q02kVTk10 #AI #LLM #healthcare #data
If AI is a Body, the Data That Powers it is the Blood
https://veranahealth.com
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Unlock the potential of AI in high-risk industries with insights from expert Cal Al-Dhubaib. In his blog, he breaks down the barriers and simplifies the steps to integrate AI in sectors like healthcare, finance, and education. No jargon, just clear guidance to help you get started 👇 https://lnkd.in/dPQKz29c #AI #Innovation #HealthcareAI #FinanceAI #EducationAI #HighRiskIndustries
How To Get Started With Building AI in High-Risk Industries
https://opendatascience.com
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Sharing a great blog on AI by the wonderful Cal Al-Dhubaib 👋🏼 In his blog, he breaks down the barriers and simplifies the steps to integrate AI in sectors like healthcare, finance, and education. Enjoy 😃
Unlock the potential of AI in high-risk industries with insights from expert Cal Al-Dhubaib. In his blog, he breaks down the barriers and simplifies the steps to integrate AI in sectors like healthcare, finance, and education. No jargon, just clear guidance to help you get started 👇 https://lnkd.in/dPQKz29c #AI #Innovation #HealthcareAI #FinanceAI #EducationAI #HighRiskIndustries
How To Get Started With Building AI in High-Risk Industries
https://opendatascience.com
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An interesting contrary viewpoint from The Lancet: "It has been argued that #explainable #AI will engender trust with the #healthcare workforce, provide transparency into the AI decision making process, and potentially mitigate various kinds of #bias. In this Viewpoint, we argue that this argument represents a false hope for explainable AI and that current explainability methods are unlikely to achieve these goals for patient-level decision support. We provide an overview of current explainability techniques and highlight how various failure cases can cause problems for decision making for individual patients. In the absence of suitable explainability methods, we advocate for rigorous internal and external #validation of AI models as a more direct means of achieving the goals often associated with explainability, and we caution against having explainability be a requirement for clinically deployed models." https://lnkd.in/gZhZe-Vz
The false hope of current approaches to explainable artificial intelligence in health care
thelancet.com
https://nubisoft.io/blog/generative-ai-in-healthcare-doctors-perspectives-and-statistical-insights/