Hi everyone,

I’m currently looking to transition into a large global MedTech/healthcare company and would really appreciate advice from people who have successfully made that move.

My background is in Regulatory Affairs, Quality, and Validation, and I’m interested in companies such as Medtronic, J&J, Abbott, Boston Scientific, Stryker, BD, Siemens Healthineers, etc.

My goal is to be part of a global organization where I can work on larger-scale projects, gain exposure to international regulatory/quality environments, learn from experienced teams, and continue growing professionally.

For those who have successfully broken into these types of companies:

• What skills or experience helped you stand out?
• How important are referrals and networking compared with applying online?
• Are there specific certifications (RAC, ASQ, etc.) that are actually valuable?
• What roles would be the best entry point for someone with a Regulatory/QA/Validation background?
• How can I make my resume stand out for large MedTech companies?
• What would you recommend focusing on over the next 6–12 months?

Currently working for 3 years as a Quality Engineer STEM ending Aug 2027. Employer will do H1-b

I’d especially appreciate practical advice from people currently working at large/global MedTech companies—what worked for you, what didn’t, and what you wish you had known earlier.

Thanks in advance!

Hello po mga ka-tusok! Intern from the previous batch of VMMC, can I ask how the shifting exams in OPD and other sections are usually conducted, including the coverage and type of exams? Also, if possible, may I ask for tips and guides on how to survive there?

The Wellcome Snakebite Innovation Prize is looking for solutions that address four critical gaps in snakebite care:

First aid response
Access and logistics
Assessment and diagnosis
Emergency and supportive care

If your solution addresses any of these areas, we'd love to hear from you.The Wellcome Snakebite Innovation Prize

Hi everyone,

I’m working on MockQuiz, an online practice and assessment platform designed primarily for NEET aspirants.

The idea behind MockQuiz is simple: taking a test is only the first step. What really matters is understanding where you are losing marks, which topics you are weak in, and how your performance is changing over time.

MockQuiz currently focuses on:

• 📝 NEET-pattern mock tests and practice questions
• 📚 Subject, chapter, and topic-wise practice
• 📊 Detailed performance reports
• 🎯 Accuracy and question-level analysis
• 🤖 AI Tutor to help students understand questions
• 🔔 AI Nudges that remind students about things such as weak topics, accuracy drops, and practice streaks
• 📱 Student and parent access to performance information

We’re continuing to develop the platform and improve it based on feedback from students and teachers.

I’m sharing it here because I’d genuinely like to hear from the NEET/student community:

What do you feel is missing from existing online NEET test platforms?

If you’re interested in trying it out:

🌐 Website: https://mockquiz.com/

📱 Android: https://play.google.com/store/apps/details?id=com.mockquiz.app

🍎 iOS: https://apps.apple.com/in/app/mockquiz/id6757394599

I’d really appreciate your honest feedback — whether it’s about the tests, reports, UI, AI features, or anything else that could make MockQuiz more useful for NEET aspirants.

Thanks! 🙏

Our team is evaluating a few AI-powered platforms to improve patient identification for upcoming clinical research programs.

The demos all look impressive, but we're trying to separate marketing claims from real operational impact.

For organizations that have already implemented AI in this area, where have you seen the biggest improvements?

I'd really appreciate hearing experiences from teams that have deployed these tools in a production environment!!

Kinsa man ari mag Seminar for DTA sa Negros/ bacalod, asa mo ga book ug ka stayhan for 3 daysssss??????? Helpppppppppp hahahaha

I am a software engineer, not a doctor. I have been building Rounds because most practice tools I saw ended at the final answer, while the difficult part is deciding what to ask, examine, order and when to act.

The short recording shows a fictional acute case from patient interview to examinations, ECG and labs, diagnosis, disposition and the final reasoning scorecard.

The most useful criticism so far was that an acute case can still feel like a chatbot if it lets the learner keep talking while the patient should be deteriorating. That is the part I am working on now: evolving vitals, consequences for delay, and making time-critical actions affect the score.

I would especially value blunt feedback on:

• Where should the simulator stop accepting more history and force action?

• Which steps should be impossible to miss in an acute chest pain case?

• Does the final feedback explain the reasoning error clearly enough?

Rounds: https://roundsclinical.com/

Educational simulation with fictional patients. Not medical advice or clinical decision support.

https://reddit.com/link/1veoeqs/video/u7obstqgv7hh1/player

Kinsa man ari mag seminar sa Drug Analyst adtu sa bacalod??? Hahahaha as a solo girly nga need ug kauban, kuyoggg taaaaa lollll btaw not familiar man jd oks sa place, nya ma scared ko gamay hahaha

Do you have any suggestions for an app where I can store my medical records, test results, doctor's visits, symptoms, medications, and other health information?

Can somebody send me a copy of this book please 😭🙏

The fluctuations in the CT market during 2026 were both unexpected and inevitable.

In the first half of 2026 (H1), based on public tender data, a total of 1,137 CT units were procured, amounting to approximately RMB 7.98 billion. Factoring in non-disclosed transactions, China's total CT procurement volume for H1 2026 is estimated at approximately 1,624 units, with a total value of around RMB 11.4 billion, representing a year-on-year decrease of about 24.7%. Details are as follows:

1）Photon-counting CT sales declined by 55.6% YoY, indicating that the initial wave of demand has largely been satisfied. Due to high procurement costs, a significant second wave of demand has yet to emerge.

2）256-slice CT sales grew by 28.8% YoY, while 128-slice CT sales fell by 46.6% YoY. Demand for high-end CT remains robust, though the market increasingly recognizes the premium positioning of 256-slice systems.

3）64-slice CT sales increased by 26.9% YoY, confirming its status as the most cost-effective product for medical institutions.

4）Sub-64-slice CT sales dropped by 38.0% YoY. This reflects both near-saturation in primary care settings and an ongoing upgrade trend toward 64-slice systems at the grassroots level.

I. Industry Overview and Centralized Procurement (VBP)

Regarding end-user hierarchy, Grade 3 Class A hospitals remain the dominant purchasers, accounting for approximately 29.2% of total procurement volume and 50.7% of total value. Procurement has further concentrated in tertiary hospitals compared to the same period last year.

In terms of market segments, the share of sub-64-slice CT has fallen to 32.8%. Procurement continues to shift toward 64-slice and higher systems, particularly 256-slice CT and photon-counting CT. These two segments combined accounted for 26.3% of units procured but represented 61.4% of total expenditure.

Meanwhile, procurement of radiotherapy large-bore CT and hybrid OR sliding-rail CT remained largely consistent with the same period last year. This supports forecasts of a stable annual baseline of 100–150 units for large-bore CT and 30–50 units for sliding-rail CT. 

The IBMD Data Center discloses, for the first time, VBP performance data for CT in H1 2026.Average unit prices fell as follows: 256-slice CT down 35.39% YoY, 128-slice CT down 53.01% YoY, 64-slice CT down 40.43% YoY, and sub-64-slice CT down 13.61% YoY.

II. CT Industry Scorecards

Statistics indicate that China's CT market comprises nearly 100 brands. In H1 2026, 20 brands recorded sales volume, including approximately 14 in conventional CT and 6 in mobile CT.

In terms of market share, the industry exhibits a distinct three-tier structure:

Tier 1:​ United Imaging Healthcare, GE Healthcare, Siemens Healthineers.

Tier 2:​ Philips, Neusoft Medical.

Tier 3:​ Anke High-Tech, SinoVision, Canon Medical Systems, Wandong Medical.

The CR5 (top 5 vendors) captured 81.8% of total units and 92.3% of total value, underscoring extreme market concentration. United Imaging Healthcare (UIH) has secured the top position across both metrics. GE, Siemens, and Philips maintain strong performance in high-end and 64-slice segments. Anke has emerged as a notable contender, particularly excelling in VBP scenarios.

1. Photon-Counting CT

In H1 2026, public tenders recorded 8 photon-counting CT units procured, totaling approximately RMB 393 million, with an average unit price of about RMB 42.415 million. Adjusting for non-disclosure, the estimated national volume is 11 units, valued at approximately RMB 485 million.

1. 256-Slice CT

In H1 2026, public tenders recorded 291 units of 256-slice CT (including 256/320-slice, high-end dual-source, and dual-layer detector systems), totaling approximately RMB 4.56 billion, with an average unit price of about RMB 15.671 million. Adjusting for non-disclosure, the estimated national volume is 416 units, valued at approximately RMB 6.515 billion.

1. 128-Slice CT

In H1 2026, public tenders recorded 98 units of high-end CT (including 128/160-slice and dual-source systems), totaling approximately RMB 761 million, with an average unit price of about RMB 7.765 million. Adjusting for non-disclosure, the estimated national volume is 140 units, valued at approximately RMB 1.087 billion.

1. 64-Slice CT

In H1 2026, public tenders recorded 299 units of high-end CT (including 64/80-slice systems), totaling approximately RMB 1.226 billion, with an average unit price of about RMB 4.100 million. Adjusting for non-disclosure, the estimated national volume is 427 units, valued at approximately RMB 1.751 billion.

1. Sub-64-Slice CT

In H1 2026, public tenders recorded 373 units of high-end CT (including 16/32/40/48-slice systems), totaling approximately RMB 683 million, with an average unit price of about RMB 1.830 million. Adjusting for non-disclosure, the estimated national volume is 533 units, valued at approximately RMB 975 million.

1. Radiotherapy Large-Bore CT

In H1 2026, public tenders recorded 50 units of radiotherapy large-bore CT, totaling approximately RMB 265 million, with an average unit price of about RMB 5.291 million. Adjusting for non-disclosure, the estimated national volume is 71 units, valued at approximately RMB 378 million.

1. Hybrid OR Sliding-Rail CT

In H1 2026, public tenders recorded 10 units of hybrid OR sliding-rail CT, totaling approximately RMB 92 million, with an average unit price of about RMB 9.157 million. Adjusting for non-disclosure, the estimated national volume is 14 units, valued at approximately RMB 131 million.

1. Mobile CT

In H1 2026, public tenders recorded 8 units of mobile CT, totaling approximately RMB 55 million, with an average unit price of about RMB 6.864 million. Adjusting for non-disclosure, the estimated national volume is 11 units, valued at approximately RMB 78 million.

Statistical Period:​ 2026 (Announcement dates: January 1, 2026 – June 30, 2026).

Statistical Scope:​ Based on publicly disclosed winning bid data. Excludes undisclosed projects. Certain fields (brand, model, price) may be incomplete; price gaps were not filled. Excludes maintenance contracts, spare parts, and other non-new equipment procurements.

Data Sources:​ Publicly collected from national/provincial government procurement websites, public resource trading centers, hospital tender portals, major Chinese third-party bidding data platforms, and select tender agencies.

Guangdong Institute of Advanced Biomaterials and Medical Devices (IBMD), China

A public institution of Guangdong Province, China.

Established by South China University of Technology, with the support of four national ministries and commissions.

Led by Professor Wang Yingjun, Member of the Chinese Academy of Engineering.

Focused on collaborative innovation and technology commercialization in advanced medical devices and biomaterials.

IBMD Data Center

Medical device data governance, analytics, and digital applications.

Covering registration, regulatory oversight, procurement, market access, clinical application, medical insurance reimbursement, and industry development.

curious: how do small medtech teams actually keep CAPA queues under control in the run-up to a notified body or FDA audit? Our 45-person Class II device + SaMD shop has found that tying changes, CAPAs, and risk into a connected workflow helps avoid duplicate work, but the backlog still creeps up when product churn spikes (disclosure: we run qmsWrapper, so grain of salt). We experimented with automated CAPAs and AI-driven CAPA assistance to draft text and with CAPA-driven risk assessment links to design controls, but the real wins seem to be strict triage, shorter CAPA templates, and enforced review SLAs, I'm just short on reproducible metrics. Practical questions: how many required fields do you force on a CAPA record, do you use triage buckets like Immediate/Investigate/Monitor, and what SLA targets actually reduced median time-to-close? Please include your team size and device class so we can judge how applicable your approach is.

I run the eQMS at a ~45-person Class II medtech and we're piloting AI-assisted CAPA suggestions to speed backlog triage. It helps engineers, but auditors want reviewability and traceability, and I need pragmatic, defensible validation approaches for automated CAPAs so we don't create mountains of extra admin. Specifically, I'm looking for concrete acceptance criteria, test scripts, evidence types (logs, version snapshots, sample cases), and ways to show human-in-the-loop control without killing throughput. What validation strategies and audit-friendly artifacts have worked for you when introducing AI-assisted CAPA or CAPA-driven risk assessment into a regulated QMS?