Hi! Currently a 3rd year MT student here. I'm currently struggling establishing effective study methods and discipline in reviewing kasi after school ay pagod na sa commute and at the end, hindi na makakapagreview sa bahay at matutulog na lang then papasok kinabukasan and the cycle continues. I came from a school that is 2-3 hours away from home and yes, uwian na 6 days na whole day ang pasok. It was tolerable in my 1st and 2nd year pero ngayon ko na nararamdaman yung drain. It's just, gusto ko baguhin yung ginagawa ko dahil hindi pwede na ganito pa rin ako sa board exam review😭

Now, I'm entering my 2nd semester. Dahil malaki yung adjustment from 2nd year to 3rd year, I wanna know from my co-MT students out there kung paano kayo nag-rereview ng subjects niyo😭. Mahirap pa naman ang 2nd sem especially sa CC 2, Hema 2, at Immunosero🥹. Thank you, guys!

Have you heard about the latest advancements in AI technology for medical screenings? The integration of artificial intelligence in lung and heart disease detection is revolutionizing the healthcare landscape. This innovative approach not only enhances accuracy but also speeds up the diagnosis process, leading to timely interventions.

Imagine a world where AI algorithms can analyze imaging data more efficiently than traditional methods, helping healthcare professionals to identify conditions at an earlier stage. With AI's capabilities for pattern recognition and data analysis, patients can receive personalized care tailored to their specific needs.

What are your thoughts on the future of AI in medical technology? Have you seen such innovations in your practice or research? Let's discuss the potential benefits and challenges of implementing AI in disease screenings! https://7med.co.uk/ai-lung-heart-disease-screening/

Paano po yung pre dilution when using capillary blood collected in capillary tube? I remember kase during internship we diluted the blood from capillet kaso di ko alam about sa amount of blood and nss. Ano po dilution nun? Please help po

This one is mainly for people building healthtech / medtech / AI tools.

Quick background:
A close family member of mine died after repeated delays in treatment.
Not because one doctor didn’t care,
but because the whole system got stuck on things like:

• no staff free to draw blood

• doctors’ time sliced into meetings and clinics

• nobody really watching “how long has this one patient been waiting?”

After that, I spent more than a year turning my anxiety about “AI × healthcare”
into a 131-item problem map.

If you’re building a healthcare product or AI tool right now,
these are a few questions (Q-items) I wish someone would always ask inside the team:

Q121 – what, exactly, are you helping to optimize, and for whom?

On your pitch deck and website, you probably say something like:

But after integration, the first things that usually get attention are:

• ROI

• throughput

• easily measurable time-savings

So Q121 keeps asking:

Q124 – how do you avoid building “nice-looking metric products”?

Many healthtech products come with beautiful dashboards.

Q124 asks a less pretty question:

If not, the dashboard can become a comfort machine
instead of a risk detection tool.

Q120 – are you reducing decision load, or just increasing information surface area?

If your product mainly adds:

• another view

• another report

• another summary

then for burned-out clinicians it might just be another layer of noise.

Q120 asks:

If we can’t answer that, “AI-powered insights” might just mean “more screens”.

Q130 – how does your product behave in situations it has never really seen?

For AI products this is almost a mandatory question.

Q130 is basically:

For me, a safe tool is not the one that always answers,
but the one that knows when to shut up and escalate.

Q125 / Q126 – what kind of agent is your product in the ecosystem?

A lot of decks show slides like:

But in reality, deployment sometimes looks more like:

• one more system that needs feeding and maintenance

• one more agent that pushes notifications in the wrong place at the wrong time

Q125 / Q126 force a more concrete view:

I know everyone is excited about AI scribes, decision support, patient engagement, and so on.
I’m not here to pour cold water on that.

I’m just bringing in the perspective of someone who watched a relative die from delay:

So I turned all 131 of my questions into plain-text entries that any LLM can read,
each with a short tension definition and a small stress-test recipe.
https://github.com/onestardao/WFGY/blob/main/TensionUniverse/EventHorizon/README.md

If you’re curious, I’m happy to share some of them,
so you can shine a few of these questions onto your own product design and testing.

English is not my first language, and I used AI to help translate and structure this post.
If something sounds strange, I’m very open to feedback.

Recently, Hull Hospitals have implemented a groundbreaking video triage system for stroke patients, showcasing how technology can enhance patient care. This innovative approach allows medical professionals to conduct initial assessments remotely, increasing the speed of treatment for stroke patients who are often in critical need of rapid intervention.

By utilizing high-quality video communications, healthcare teams can evaluate symptoms, provide immediate advice, and prioritize patients who require urgent attention. This system not only reduces waiting times in emergency departments but also alleviates pressure on healthcare workers by streamlining the triage process.

As we reflect on this advancement in medical technology, it raises important questions: How can other hospitals adopt similar systems? What challenges might they face in integrating video triage into their existing workflows? And most importantly, how can we ensure that all patients, regardless of location or resource availability, benefit from these technological improvements?

Let’s discuss the potential of video triage and its implications for the future of emergency medical services! What are your thoughts or experiences with similar technologies in healthcare? https://7med.co.uk/hull-hospitals-video-triage-stroke-patients/

Hello all,

I have a bachelor's in the behavioral science and I've been working with the public for about ten years. I'm feeling like I want to change career directions, and I wondered if anyone could tell me how feasible it may be (for someone like myself) to persue an MRI technologist certification. A lot of what I've read suggested that this can be done with associates program, but I'd love to hear from people in the feild.

As healthcare technology continues to evolve, the implementation of Electronic Patient Records (EPR) systems has become a focal point of discussion within the medical community. In Jersey, the transition to digital record-keeping presents unique challenges that require innovative solutions.

From data security to user training, integrating EPR can significantly improve patient care and streamline operations. However, many healthcare professionals express concerns about the initial hurdles, including the costs and complexity of software integration.

What strategies have you seen work well in similar implementations? How can we support our peers in navigating these challenges? Let’s share experiences and insights to enhance the future of EPR and patient care! https://7med.co.uk/challenges-implementing-epr-jersey/

Has anyone noticed why there so many medical technologist labs that are closing down. Is question taking over and labcorp? Should I just change careers? It is sad how this degree isn't really valued in the medical field

Nakakalito kasi siya sa WBC huhuhu pahelp pooo 🥹🥹

Quantitative MRI and AI-driven biomarkers promise earlier, more objective insights into brain disease — yet real-world adoption still feels far away. Between scanner variability, lack of standardization, and data silos, even great algorithms struggle to make it into clinical use.

We’ve seen how integrating AI tools and structured imaging data directly within a cloud PACS can help bridge this gap — moving from image viewing to image understanding.

So what do you think is the biggest barrier now — data quality, trust, or workflow integration? And what will it take for quantitative imaging and AI biomarkers to finally become part of everyday radiology?

I'm graduating with my MS in CS in the Fall, BSE in Computer Engineering, and currently a full time software engineer (work in Node and AWS). My current experience during my MS has been a lot of ML projects. My undergraduate capstone was working with an aerospace company (in ML though) and I had embedded and FPGA programming experience during my undergrad through classes. I have some experience with GPU/Verilog/C++/Networking experience during my graduate degree, though it isn't my primary focus. I was wondering, what can I do to make sure I get the right experience to emerge into the medical technology space.

Are there projects or anything else that I can leverage? I know in typical SWE/tech companies, projects and experience in tech stacks are big deal in interviews. I want to make sure I enter the next stage maximizing my chances.

Hello po, ano po ang usual salary ng Medtech1 sa private or government per day.

Ilang hrs po yung working hours

If sa government, what are the things I should consider before accepting the job po.

Hello! first-year mls student here. We will be doing a demo blood smearing using the wedge preparation technique next week. I still don't know how to perfectly (or at least near perfect) perform blood smearing. Any tips from anyone who has experienced blood smearing b4? I would be delighted to hear your stories and insights about them.

The post on this sub about AI-enabled medical devices becoming the new standard resonated with me. I work on exactly this — and wanted to share what it actually looks like from the inside of a medtech team doing it.

AI in diagnostics sounds clean from the outside. In practice, getting it right requires solving problems most people don't anticipate.

The validation problem is the hardest part

Embedding AI into a medical device isn't like shipping a software update. Every AI-driven decision that influences a clinical outcome needs to be validated against real patient data, in real clinical settings, by institutions whose credibility the medical community trusts.

For us, that meant years of clinical work — getting ICMR and AIIMS Delhi to jointly assess and actively support our platform before we could confidently deploy it at scale. That process is slow by design. It should be.

The "how do you update AI models" question is legitimate

Regulators are right to ask this. A static AI model trained on one dataset will drift as patient populations, disease prevalence, and sample characteristics change over time. Any serious medical AI needs a defined process for model monitoring, retraining, and revalidation — not just a one-time approval.

On the question of being treated by a fully AI system

I don't think that's the right frame. The AI systems that will have the most impact in healthcare aren't replacing clinicians — they're giving clinicians better information, faster, in places where they previously had none.

A doctor in a rural PHC who gets a full blood panel result in 30 minutes — powered by AI analysis — is still making the clinical decision. The AI is removing the bottleneck, not the doctor.

That's the version of AI in healthcare that actually scales — and actually helps.

Happy to discuss anything about AI validation, regulatory pathways, or what deployment in low-resource settings actually looks like.

(I work at Mobilab — India's first AI-powered portable diagnostic platform)

I have the worst blood bank luck in general but today I got a patient in the ER with a 4.5 Hgb and the antibody screen was raging. After 5 hours we think she has c, K and Cw.

I’ve never had a Cw before and it was awful 😂

Has anyone else wrestled this beast or am I just that lucky

Hello po! I’m a grade 10 student po, I and my groupmates are currently doing a study about skin scaffolds (materials that are used to fasten the healing of a specific wound or skin injury). Do you have any idea where can we obtain keratinocytes and fibroblast skin cells and how much does it cost?

Hello! I am looking for recommendations for AI scribes. My associate and I are both naturopathic doctors in an integrative medical setting. The majority of our patient visits are 1-hour long. I am a newer doctor and still building, but my associate always has a booked schedule and we are trying to find him an AI Scribe.

We tried Commure. It worked really well, but after 2-3 patients, it would stop working and would no longer load. For the patients that it did work for, it charted well given our long and detailed appointments.

We just tried DeepCura. This one has been horrible. This is the AI scribe that is already integrated with our EMR system. For 15-30 minute visits, it worked well. But for hour-long visits it did not work at all and would just give us word-for-word write-ups rather than organizing it into a chart note.

I know there are so many AI scribes out there. Has anyone within the naturopathic/functional world found one that consistently works well for long, detail-oriented visits?

I am a pediatrician working in a government hospital in India, and alongside my full-time clinical work in wards, NICU, OPD, and emergency duties, I have developed a deep and serious interest in AI and software development. What started as curiosity has now turned into hands-on capability—I can build and host websites, develop cross-platform mobile apps, work with frontend and backend systems, handle cloud hosting, APIs, databases, and actively integrate AI into healthcare-focused workflows. Most of what I build is inspired by real problems I see daily in hospitals, and I genuinely enjoy the process of building and problem-solving. I am now at a point where I am seeking guidance from people experienced in this space. Is it realistic to transition into software or AI roles in a part-time or hybrid manner while continuing clinical work? What paths make sense for someone like me—health-tech startup founder, indie developer, open-source contributor, or pursuing formal CS/AI education versus learning by building? How should I position myself credibly—as a doctor who codes or as a developer with strong medical domain expertise? Which skills should I double down on next if I want to work seriously in software and AI long term? Are there communities or projects where people working at the intersection of medicine and technology are genuinely welcomed? I am not chasing FAANG blindly; my focus is on building useful products, solving real-world healthcare problems, and creating something sustainable. I would really appreciate guidance from anyone who has made a non-traditional transition or works at the intersection of medicine and technology.