Abhishek Chatterjee

With mid-year reviews around the corner, felt apt to share this interesting illustration I came across - the relation between learning, implementing and feedback. This continuous cycle ensures that you're always improving and adapting, an essential for personal and professional growth. Learning should never stop. 

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😐 Doctors #survey results were eye-opening on #AI, revealing both #enthusiasm and #uncertainty regarding AI's role in medicine. While #AI holds immense promise in transforming #healthcare, it's essential for #doctors to understand its full potential and embrace it thoughtfully to ensure the best outcomes for patients. Which was not the case in my survey. “ 😚 How can HCPs use AI for patients?” At a recent panel discussion @ DigiPharmaX event and awards, I was one of panelist. Since a doctor on panel would not be there, a week before the event, I conducted survey of 45 tech-savvy doctors to know what they think. The results were eye-opening, #revealing both #enthusiasm and #uncertainty regarding AI's role in medicine. 1.      Majority of the surveyed #doctors expressed a positive sentiment towards AI, recognizing its potential usefulness despite lacking a clear understanding of its applications. 2.      Many identified #pathology and #radiology as early adopters of AI technology, highlighting its perceived value in diagnostic fields. 3.      However, when probed further on how they envision using AI in their practice, most doctors cited mundane tasks like appointment and data management, indicating a limited grasp of AI's broader capabilities. I feel as of now, many doctors are not aware of AI's potential to revolutionize medicine beyond administrative tasks. The sentiments among doctors regarding AI varied: ·        Positive: Enthusiasm on AI for diagnostics, personalize treatment plans, and automate administrative tasks. ·        Neutral: Caution about AI's nature, need for careful integration into existing healthcare systems. ·        Negative: Concerns about AI undermining the human touch, & maintaining a balance between technology and human expertise. AI is being used in personalized treatment plans, virtual health assistants, remote monitoring, health education, behavioral coaching, predictive analytics, decision making, and language translation services. 😃 😄 😃 To facilitate AI adoption among doctors, it's crucial (To train doctors). One need to provide training on AI tools' functionalities and benefits to doctors, Offer real-case demonstrations of successful AI implementations, Emphasize patient-centered care and how AI enhances it, Encourage ongoing learning in digital health technologies, etc. Real-world examples, such as #cancer detection #algorithms being used, #FDA-approved AI systems for #diabetic #retinopathy detection, and many more have evolved. Thanks DigiPharmaX, maX, Girdhar Ba, ani, Sujeet Ka, yar, Debashis, Roy, AMIT, AIN,, tai, Vikas Dan, kar, Suresh Subram...

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Are we in the middle of a death march of AI pilots- setup to fail? Failed AI nudges, disease prediction of oncology on non-genetic data?  Are you working for a company that is “Data Transformed” or a “Brute Force Results” company? First fix the data. In 1997 there was a book called “Death March” released the gist of it was that a lot of IT projects were death marches and doomed to failure because of the way they were set up. 👉 AI projects will fail if the data “enabling infrastructure” is not there- companies that do the following will likely fail in AI projects.  1. No designation whose top KPI is related to data. It starts with the right people. Whether it is payers wanting better patient experience or digital transformation, it starts with people hiring (or promotion) to a clear and distinct data role. The designation can be SFE, head of transformation but without an ownership of data the organization leadership does not understand competitive advantage of data and projects will fail. 2. No budget for data improvement, augmentation for competitive advantage. In with head heart and wallet. Will your team build your target customer by burning ad money across all target profiles? How will software for data be selected and invested? Budgets preceed training, software, goals and roadmap. 3. No case study of data success. It starts with a small successes to transform. Companies that are either building data lake for a perfect AI nudge without having tested "the AI nudge without a data lake" are setup for AI failure.   4. Senior leadership not talking or tweeting AI. Nvidia CEO had a lonely journey of years talking about AI before manifestation. Yes there is over dose of false claims but enterprise manifestation starts with CEO visioning in public. Expect none of the above if the senior leadership is not willing to publicly commit AI as core. McKinsey reports that companies with a data-centric culture are 23 times more likely to outperform their competitors in both customer acquisition and retention. What we feel is that non-data-transformed companies are brute-force companies working twice as hard to push the envelope. What stage of data denial is your team or company in? First fix the data. Credit for cartoon: Here is a cartoon from the awesome site Marketoonist modified from digital to data. #data #aitransformation #healthcareonlinkedin #pharmaceo #genaicontent #genaipilot

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Thoughts on this? >> RBI Governor: Incoming Economic Growth Data Mixed, But Positive Factors Prevail >> Comment below! >>> lqventures.com #industry40 #AI #IoT #mhealth #healthtech

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Thoughts on new Models from OpenAI and similar models Lot of interesting announcements and publications on OpenAI o1 - Thinking focused . Tested few scenarios , got good initial results !!!!! . If we take this and see where it will fit for business use cases - One option is use this model to think, reason and come up with actions which can be taken by Agentic AI , when business decision changes AND/OR decision needs human ideas on fly due to data variability . This concept works Open for suggestions and other thoughts from experts !!!!!

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🚀 **Is GenAI Overhyped or the Next Big Leap in Innovation?** In recent months, the buzz around Generative AI (GenAI) has reached a fever pitch. From creating art and drafting content to coding and problem-solving, GenAI is everywhere. But as with any technological advancement, the question arises: **Is GenAI overhyped?** Let’s unpack this. 🔍 **The Promise of GenAI:**   GenAI is undeniably powerful, offering capabilities that were science fiction just a few years ago. It can augment human creativity, streamline workflows, and provide solutions at a speed and scale previously unimaginable. For industries like marketing, design, and even software development, the potential applications are vast and transformative. ⚖️ **The Reality Check:**   However, like any emerging technology, there are limits and challenges. The outputs of GenAI are only as good as the data it's trained on, which can introduce biases or inaccuracies. Additionally, while GenAI can generate content, it often lacks the nuanced understanding and contextual awareness that human professionals bring to the table. 🔧 **Navigating the Hype:**   Businesses and professionals need to approach GenAI with a balanced perspective. Rather than viewing it as a magic bullet, we should see it as a tool that, when used wisely, can amplify our capabilities. The key lies in integrating GenAI thoughtfully into our workflows, understanding its strengths, and being aware of its limitations. ✨ **The Takeaway:**   GenAI is not just a passing trend—it's a glimpse into the future of how we work and create. But to unlock its full potential, we must combine it with human insight, ethical considerations, and strategic implementation. 💬 What are your thoughts? Is GenAI the game-changer it's made out to be, or is there more hype than substance? In case you want to contribute, it is most welcome. #GenerativeAI #Innovation #TechTrends #FutureOfWork #AI

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Are scientists inventing their AI replacement? https://lnkd.in/gZ6fkpd7 interesting ready - attempting to automate research itself. An end-to-end framework designed to automate the generation of research ideas, implementation, and the production of research papers.

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