Should I Build It Should I Build It
03 May 2025
Medical Artificial Intelligence Health & Fitness

Senna it’s a medical tool similar to ChatGPT, the doctors can search ...

...medical tools like scales, dosages and more. In this tool you can write the medical record of the patient and get feedback on important points about the patient and recommendations for the treatment and management

Confidence
Engagement
Net use signal
Net buy signal

Idea type: Run Away

Multiple attempts have failed with clear negative feedback. Continuing down this path would likely waste your time and resources when better opportunities exist elsewhere.

Should You Build It?

Don't build it.

Your are here

The idea of Senna, an AI-powered medical tool to assist doctors, falls into a crowded space. There are 22 similar products, which signals high competition. While this indicates a potential market need, it also suggests a higher barrier to entry. The engagement for these types of products is high, averaging 12 comments, suggesting active interest and discussion. However, the lack of positive signals regarding both user intent and purchase intent indicates that many similar products have failed to gain traction. You're in a tough spot, so tread carefully.

Recommendations

  1. Read through the negative comments from the similar product launches to understand the pain points and reasons for rejection. Common criticisms include concerns about data accuracy, AI hallucinations, and privacy/compliance (HIPAA, GDPR). Understand these shortcomings to avoid repeating mistakes.
  2. Talk to at least three doctors who have tried similar AI-based medical tools. Understand their specific needs, frustrations, and what would make such a tool truly valuable in their daily workflow. Don't rely on assumptions; gather direct user feedback.
  3. Consider how your skills and technology could be applied to a related but different problem within the medical field. Are there niche areas or unmet needs where AI could provide a more focused and impactful solution? This could involve focusing on a specific medical specialty or a particular type of patient care.
  4. If you've already started building Senna, evaluate if the underlying technology can be repurposed for a different healthcare application. Perhaps the AI engine could be used for medical coding assistance, drug interaction analysis, or personalized patient education materials.
  5. Explore if Senna can address concerns regarding privacy, data security, and compliance, which were frequently raised in the negative feedback for similar products. Implement robust security measures and transparency in data handling to build user trust.
  6. Carefully select your target audience. Will you initially focus on hospitals, clinics, or individual practitioners? Tailor your messaging and features to resonate with the specific needs of your chosen segment.
  7. Given concerns about AI hallucinations and accuracy, prioritize rigorous testing and validation of Senna's recommendations. Incorporate mechanisms for explainable AI (XAI) to increase transparency and trust in the AI's decision-making process.
  8. Address potential copyright and legal issues related to medical information and AI-generated content. Consult with legal experts to ensure compliance with regulations and prevent future lawsuits.
  9. Apply everything you've learned from analyzing the competitive landscape and gathering user feedback to iterate on Senna's design and features. Focus on creating a product that is not only innovative but also practical, reliable, and trustworthy.

Questions

  1. Given the strong concerns about data accuracy and AI hallucination in the comments of similar products, what specific measures will Senna implement to ensure the reliability and validity of its medical recommendations and prevent the dissemination of incorrect or misleading information?
  2. Considering the sensitivity of patient data and the regulatory requirements of HIPAA and GDPR, how will Senna ensure the privacy and security of patient information, and what steps will be taken to address the concerns raised about data breaches and unauthorized access?
  3. Given the criticisms of existing AI medical tools regarding their impact on the doctor-patient relationship and the potential for deskilling healthcare professionals, how will Senna be designed to complement and enhance the role of physicians, rather than replace them, and what training will be provided to ensure its responsible and effective use?

Your are here

The idea of Senna, an AI-powered medical tool to assist doctors, falls into a crowded space. There are 22 similar products, which signals high competition. While this indicates a potential market need, it also suggests a higher barrier to entry. The engagement for these types of products is high, averaging 12 comments, suggesting active interest and discussion. However, the lack of positive signals regarding both user intent and purchase intent indicates that many similar products have failed to gain traction. You're in a tough spot, so tread carefully.

Recommendations

  1. Read through the negative comments from the similar product launches to understand the pain points and reasons for rejection. Common criticisms include concerns about data accuracy, AI hallucinations, and privacy/compliance (HIPAA, GDPR). Understand these shortcomings to avoid repeating mistakes.
  2. Talk to at least three doctors who have tried similar AI-based medical tools. Understand their specific needs, frustrations, and what would make such a tool truly valuable in their daily workflow. Don't rely on assumptions; gather direct user feedback.
  3. Consider how your skills and technology could be applied to a related but different problem within the medical field. Are there niche areas or unmet needs where AI could provide a more focused and impactful solution? This could involve focusing on a specific medical specialty or a particular type of patient care.
  4. If you've already started building Senna, evaluate if the underlying technology can be repurposed for a different healthcare application. Perhaps the AI engine could be used for medical coding assistance, drug interaction analysis, or personalized patient education materials.
  5. Explore if Senna can address concerns regarding privacy, data security, and compliance, which were frequently raised in the negative feedback for similar products. Implement robust security measures and transparency in data handling to build user trust.
  6. Carefully select your target audience. Will you initially focus on hospitals, clinics, or individual practitioners? Tailor your messaging and features to resonate with the specific needs of your chosen segment.
  7. Given concerns about AI hallucinations and accuracy, prioritize rigorous testing and validation of Senna's recommendations. Incorporate mechanisms for explainable AI (XAI) to increase transparency and trust in the AI's decision-making process.
  8. Address potential copyright and legal issues related to medical information and AI-generated content. Consult with legal experts to ensure compliance with regulations and prevent future lawsuits.
  9. Apply everything you've learned from analyzing the competitive landscape and gathering user feedback to iterate on Senna's design and features. Focus on creating a product that is not only innovative but also practical, reliable, and trustworthy.

Questions

  1. Given the strong concerns about data accuracy and AI hallucination in the comments of similar products, what specific measures will Senna implement to ensure the reliability and validity of its medical recommendations and prevent the dissemination of incorrect or misleading information?
  2. Considering the sensitivity of patient data and the regulatory requirements of HIPAA and GDPR, how will Senna ensure the privacy and security of patient information, and what steps will be taken to address the concerns raised about data breaches and unauthorized access?
  3. Given the criticisms of existing AI medical tools regarding their impact on the doctor-patient relationship and the potential for deskilling healthcare professionals, how will Senna be designed to complement and enhance the role of physicians, rather than replace them, and what training will be provided to ensure its responsible and effective use?

  • Confidence: High
    • Number of similar products: 22
  • Engagement: High
    • Average number of comments: 12
  • Net use signal: -6.5%
    • Positive use signal: 5.8%
    • Negative use signal: 12.3%
  • Net buy signal: -9.9%
    • Positive buy signal: 0.3%
    • Negative buy signal: 10.2%

This chart summarizes all the similar products we found for your idea in a single plot.

The x-axis represents the overall feedback each product received. This is calculated from the net use and buy signals that were expressed in the comments. The maximum is +1, which means all comments (across all similar products) were positive, expressed a willingness to use & buy said product. The minimum is -1 and it means the exact opposite.

The y-axis captures the strength of the signal, i.e. how many people commented and how does this rank against other products in this category. The maximum is +1, which means these products were the most liked, upvoted and talked about launches recently. The minimum is 0, meaning zero engagement or feedback was received.

The sizes of the product dots are determined by the relevance to your idea, where 10 is the maximum.

Your idea is the big blueish dot, which should lie somewhere in the polygon defined by these products. It can be off-center because we use custom weighting to summarize these metrics.

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Clint is an open-sourced medical information lookup and reasoning tool.Clint enables a user to have an interactive dialogue about medical conditions, symptoms, or simply to ask medical questions. Clint helps connect regular health concerns with complex medical information. It does this by converting colloquial language into medical terms, gathering and understanding information from medical resources, and presenting this information back to the user in an easy-to-understand way.One of the key features of Clint is that its processing is local. It's served using GitHub pages and utilizes the user's OpenAI API key to make requests to directly to GPT. All processing, except for that done by the LLM, happens in the user's browser.I recently had a need to lookup detailed medical information and found myself spending a lot of time translating my understanding into the medical domain, then again trying to comprehend the medical terms. That gave me the idea that this could be a task for an LLM.The result is Clint. It's a proof-of-concept. I currently have no further plans for the tool. If it is useful to you as-is, great! If it is useful only to help share some ideas, that's fine too.

Users are inquiring about the medical literature indexing project and comparing various language models in medical contexts. A nurse appreciates the potential for improved healthcare, while concerns about health anxiety and AI diagnostic failures like Babylon Health are noted. There's curiosity about the AI's name and a critique of the visual presentation. One user seeks reassurance on a personal health issue, and another discusses the need for OpenAI API access payment. Questions about the implications of AI hallucinations are also raised.

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https://news.ycombinator.com/user?id=TuringNYC

Dear @garrinm Firstly, thank you for sharing this! We built something like this at work, and i'd love if you could share details on your effort.I tried to follow: https://github.com/clint-llm/clint-cli/blob/main/clint/scrip... but it wasnt clear -- are you indexing the medical literature in entirety or just abstracts?We tried to do this with arXiv and others, but getting commercial rights was difficult and we got stuck on that, could you share which medical literature source you used.I tried to follow the code and it looks like you embed, so i'm assuming you're using RAG, is that it, or are you trying to fine-tune also? I didnt see any fine-tune code. (We didnt fine tune due to cost)Did you benchmark different embedding chunk sizes, etc? (Yes for us! We've tried a matrix search of chunk sizes, including sliding window and found the sweet spot for different types of media, usually a single paragraph)Did you manage to get access to a fine-tuned model like MedPALM and benchmark that? (we are still awaiting access)

https://news.ycombinator.com/user?id=linsomniac

My wife is an RN and asked it some medical questions and pronounced it "pretty neat" and also said "We're going to get so much better doctoring in the future."She did find that some of the follow-up questions seemed to lead it down the wrong path, where the follow-ups seemed to be much more important than the initial messages, and she got better results by taking the original message and follow-ups and combining them into a new message to submit to a cleared session.

https://news.ycombinator.com/user?id=jcutrell

The health anxiety in me just found a whole new lifeline.

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Hey HN,We're Alex, Martin and Laurent. We previously founded Wit.ai (W14), which we sold to Facebook in 2015. Since 2019, we've been working on Nabla (https://nabla.com), an intelligent assistant for health practitioners.When GPT-3 was released in 2020, we investigated it's usage in a medical context[0], to mixed results.Since then we’ve kept exploring opportunities at the intersection of healthcare and AI, and noticed that doctors spend am awful lot of time on medical documentation (writing clinical notes, updating their EHR, etc.).Today, we're releasing Nabla Copilot, a Chrome extension generating clinical notes from video consultations, to address this problem.You can try it out, without installation nor sign up, on our demo page: https://nabla.com/copilot-demo/Here’s how it works under the hood:- When a doctor starts a video consultation, our Chrome extension auto-starts itself and listens to the active tab as well as the doctor’s microphone.- We then transcribe the consultation using a fine-tuned version of Whisper. We've trained Whisper with tens of thousands of hours of medical consultation and medical terms recordings, and we have now reached an error rate which is 3× lower than Google's Speech-To-Text.- Once we have the transcript, we feed it to a heavily trained GPT-3, which generates a clinical note.- We finally return the clinical note to the doctor through our Chrome extension, the doctor can copy it to their EHR, and send a version to the patient.This allows doctors to be fully focused on their consultation, and saves them a lot time.Next, we want to make this work for in-person consultation.We also want to extract structured data (in the FHIR standard) from the clinical note, and feed it to the doctor’s EHR so that it is automatically added to the patient's record.Happy to further discuss technical details in comments!---[0]: https://nabla.com/blog/gpt-3/

The Show HN product, likely a healthcare-related AI service, has received mixed feedback. Privacy and compliance with regulations like HIPAA and GDPR are major concerns, with users questioning the product's adherence to these standards and the risks of sharing medical data with third parties. There's also skepticism about the reliability of AI in healthcare, particularly in summarizing medical records. However, some see the potential for reducing doctors' administrative workload and improving patient care. The product's name and privacy policy have also been criticized, and there are calls for more transparency and legal clarity. Positive comments include support for the concept and the benefits of telemedicine tools.

The Show HN product or service has received criticisms focused on privacy concerns, particularly regarding HIPAA and GDPR compliance, and the potential for AI to introduce errors into medical records. Users are skeptical about the accuracy of AI transcriptions and the involvement of third parties, which they find intrusive. There are also concerns about the lack of transparency in privacy policies and certifications. Additionally, users question the product's impact on doctor-patient relationships and the practicality of its implementation in healthcare settings. Some criticisms also touch on the inefficiency of the current medical system and the potential for AI to exacerbate existing issues.

https://news.ycombinator.com/user?id=sharemywin

I'll be happy to do that for like $200 as long as I can use ChatGPT

https://news.ycombinator.com/user?id=ar7hur

Yes absolutely.

https://news.ycombinator.com/user?id=mr_mitm

I would want my doctor to Google things. No one knows everything, but I expect an expert to know where to look. If he used Google the same way I do, he'd know which results are reliable. Doctors who finish med school and never consult a resource ever again cannot be good doctors.

https://news.ycombinator.com/user?id=trdtaylor1

You are not wrong; I did not think about this until you brought it up but it's a great plan to wrap nearly free customers, then get in bed with Health Insurers to reduce cost of care by recording and transcribing all doctor conversations, then alerting to possible fraud or excessive care based on patient input against your huge DB of patient interactions. Historically recording of the patient DR interaction has always been incredibly hard sell to do for the management types. Then insurers mandate chat capture with the visit summary billed to them. Love it!

https://news.ycombinator.com/user?id=Terretta

Ignore the nay-sayers in this thread. I doubt the ones commenting have spent many days personally observing or personally doing the problem scenario you are solving for.This is spectacular for telemedicine in the post-covid regulation environment.The new model is an onsite medical assistant taking patient vitals and setting up the Zoom, the patient then talks to an APRN (Advanced Practice Registered Nurse) in the patient's field (e.g. subacute care, gerontology, etc.).The APRNs spend 30 minutes reading the file, tele-consult with a patient for a half hour. Medicare pays for the half hour. Then the APRN has to spend ~60 minutes turning that session into the EHR notes. Many just don't, they make an ultra short note, and hope Medicare doesn't challenge it. Patient loses. If too strapped for time, they also may not pre-read, because they only get paid for the video conf time. Patient loses.Downside I see with this is the APRNs doing telemedicine are making about 1/4 of the rate they should be, and can't afford an expensive tool. The telehealth provider is not going to pay for it, they get paid for the 1/2 hour, the wasted time is not out of their pocket, so they won't spend to make it up. This is going to be on the nurse, who likely can't afford it.On the plus side: time saved with this tool would go directly to ability to pre-read, or ability to see a patient an hour instead of one per two or three hours. It would also permit a higher caliber of expertise nurse to do tele-medicine visits in off days, because the time saved could bring comp back more in line with the expertise.Go for it!

https://news.ycombinator.com/user?id=l5t

Good point, this is why we finetune Whisper with our own medical dataset to improve the performance

https://news.ycombinator.com/user?id=tzm

Love this and have had discussions w/ doctors and practitioners about this exact problem. I think extracting to FHIR is key, along with making it searchable as a longitudinal data store for a more robust personal record. Clear need. Would love to help.

https://news.ycombinator.com/user?id=ar7hur

Yes we are! https://www.nabla.com/careers/

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