28 measurements spanning 34 days, net +579. Dots are measurements; the straight line between them is drawn to join them, not to claim we know the path taken in between — snapshots are recorded only when a count changes, so gaps mean “no change observed”, never “interpolated”. The vertical axis spans 12,415–13,168 and does not start at zero.
Measurement log — every subscribers count we have recorded, most recent 20 of 28
Measured (UTC)
Subscribers
Change
10 Sept 2026, 01:19
13,081
+47
6 Sept 2026, 16:01
13,034
+33
4 Sept 2026, 08:40
13,001
+67
2 Sept 2026, 20:45
12,934
-6
1 Sept 2026, 21:45
12,940
+6
1 Sept 2026, 00:47
12,934
+5
31 Aug 2026, 03:13
12,929
+14
30 Aug 2026, 01:02
12,915
+6
29 Aug 2026, 02:48
12,909
+16
28 Aug 2026, 00:18
12,893
+8
27 Aug 2026, 00:38
12,885
+25
25 Aug 2026, 23:02
12,860
+20
25 Aug 2026, 01:22
12,840
+51
23 Aug 2026, 14:26
12,789
+36
22 Aug 2026, 00:16
12,753
+13
20 Aug 2026, 17:55
12,740
+16
18 Aug 2026, 18:12
12,724
+13
17 Aug 2026, 14:37
12,711
+41
16 Aug 2026, 08:55
12,670
+10
14 Aug 2026, 22:29
12,660
first reading
Engagement
44 posts held, back to 30 July 2026 — the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 48 pages of Telegram’s post history, 20 posts per page.
ERR · 30 days
4.71%
avg views ÷ 13,081 subscribers
Avg views / post
616
30 posts measured
Reaction rate
1.27%
reactions ÷ views · ER floor
Posts in window
30
of 44 held
ERR is average views per post over the last 30 days divided by subscribers, the definition TGStat uses, so this figure is comparable with the one you will see elsewhere. It falls structurally as a channel grows: a high ERR on a small channel and a low one on a large channel describe reach mathematics, not quality. We publish the figure and the sample it came from and pass no verdict on it.
ER is defined industry-wide as (forwards + reactions + comments) ÷ views — note the denominator is views, not subscribers. Telegram’s public web preview carries views and reactions but not forward or comment counts, so the reaction rate above is the reactions term only and is therefore a floor: the true ER for this channel is higher by an amount we have not measured and will not estimate. It is computed over the 28 of 30 measured posts that carry a reaction reading, and over those same posts' views.
What these figures were computed from
Window
Rolling 30 days · latest post in window 2 September 2026
Posts held
44 (30 July 2026 – 2 September 2026)
Views total
18,493
Reactions total
219
Forwards / comments
not exposed by the public surface — not measured, not estimated
Readings taken
2 Sept 2026, 20:50 UTC
Views are a single reading per post, taken at the time above. A post published in the last day or two is still accumulating views, which pulls the 30-day average down slightly. That is a property of the standard definition rather than a fault in it, so we keep the definition rather than “correcting” the number into something nobody can reproduce.
Precision. Telegram publishes view counts on its public widget in short form — 8.12K, 3.7M — so any reading at or above 1,000 reaches us rounded to three significant figures, and only counts below 1,000 are exact. Averages and rates derived from them are shown to the same precision rather than to the unit: a figure like 3,701,250 would assert digits nobody measured.
Reaction counts are published per emoji and rounded the same way, so a total below 1,000 is exact and a larger one is a sum that may carry a rounded component from each emoji above 1,000. Because it is a sum, it does not look rounded — read a large reaction total as three significant figures per contributing emoji rather than as the figure it prints.
What this channel posts
Video runtime
2m 48s
Average length
34s
Measured directly from 5 videos with a duration reading, out of the posts we hold for this channel — not this channel’s whole posting history, only the sample this register has actually read. An exact reading to the second, taken from the post itself rather than from Telegram’s own rounded chrome, so it carries no ≈ mark.
Reaction mix
327 reactions across 42 posts, in 8 distinct kinds. The most used accounts for 51.1% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
❤
167
51.1%
🔥
96
29.4%
👍
30
9.17%
👌
10
3.06%
🥰
9
2.75%
🙏
8
2.45%
👀
6
1.83%
❤🔥
1
0.306%
No sentiment is inferred, and none should be read in. This table is ordered by count and by nothing else. Emoji do not carry stable meaning across languages or communities — 🙏 is thanks in one channel and mourning in another — so we publish which ones were pressed and how often, and pass no judgement on what an audience meant by them.
Precision. Telegram publishes reaction counts per emoji and short-forms each one — 4.34K, 1.2M — so any single kind at or above 1,000 reaches us at three significant figures, and only counts below 1,000 are exact. The shares above are ratios of those figures and carry the same error. This is also why the total here can differ slightly from a reaction total printed elsewhere on the page: both are sums of the same rounded parts, taken over samples with different edges.
Coverage. Reactions were read on 42 of the 44 sampled posts in this sample. Summed by Telegram’s own count on each post — not by adding up the per-emoji breakdown above — those same posts carry 327 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 44 most recent posts we hold, published 30 July 2026 to 2 September 2026, using the newest reading held for each. Telegram Stars are excluded: they are a payment, not a reaction, and they have their own section.
Telegram Stars
Stars received
1
across the posts below
Posts paid on
1
of 44 we hold a reading for · 2%
Most on one post
1
single highest reading
A paid reaction is a reader spending Telegram Stars — bought with money — on a post by @mdai_dima. Telegram publishes the count on the public post preview alongside ordinary reactions, and this register reads it there. It is the only figure on this site that measures money moving rather than attention.
Stars are not reactions, and the two are never added. They are rendered in the same strip on Telegram and counted in the same shape, but one is a tap and the other is a purchase. The reaction totals and the engagement rate elsewhere on this page exclude every figure in this section, and no rate here is computed against a reaction count.
This is not revenue, and we publish no currency figure. What a Star costs a reader and what it pays a channel are different numbers, Telegram takes a share we cannot observe, and the terms have changed. Converting a Star count into money would be an estimate dressed as a measurement, so the count is where we stop.
Counted over the 44 most recent posts we hold for this entry, published 30 July 2026 to 2 September 2026. Star counts above 1,000 reach us in Telegram’s short form and carry the same three-significant-figure rounding as everything else on this page.
И если всё лето вы собирались «с сентября наконец разобраться с ИИ» — тот самый сентябрь уже наступил.
Вы врач, но огромная часть рабочего дня уходит совсем не на общение с пациентом. Вот что говорили наши студенты до прохождения курса:
💬
«Я трачу больше времени на заполнение документов, чем на сам приём»
💬
«Смена закончилась, но я всё ещё в кабинете, доделываю бумажную работу»
💬
«Пытаюсь автоматизировать процесс…
🎁 Подготовили для вас подарок — гайд «От данных до медицинской иллюстрации»
Построить график для статьи, презентации, отчёта или учебного материала кажется простой задачей — пока не приходится вручную переносить данные, настраивать оси, подписи и оформление.
Нейросеть может сделать это за несколько минут.
На карточке показываем один из пяти сценариев из гайда: как превратить таблицу с лабораторными показателями в …
С 1 по 6 сентября — распродажа MD.school: флагманские программы от 11 900 ₽.
И если всё лето вы собирались «с сентября наконец разобраться с ИИ» — тот самый сентябрь уже наступил.
🔥 «Нейросети в работе врача» — 22 900 ₽ вместо 39 900 ₽
Курс для тех, кто хочет использовать ИИ в реальной работе:
— быстрее готовить черновики выписок и других документов;
— разбирать клинические рекомендации и статьи;
— искать и структ…
ТОП‑3 нейросети для интерпретации анализов и диагностики + пример запроса.
Нейросети всё активнее входят в медицинскую практику. Они помогают от расшифровки лабораторных данных до поиска лечения сложных клинических случаев.
На карточках разобрали 3 инструмента для интерпретации анализов и диагностики:
1. AIDA (AI Diagnostic Assistant)
2. Цельс (Celsus.ai)
3. DIMA от MD.School
А вы уже пробовали такие инструменты в…
Как врачу быстро превратить статью, заметки или клинический разбор в понятный пост для коллег
Если вы хоть раз просили нейросеть: «Напиши пост про гипотиреоз / вакцинацию / головную боль», то наверняка видели результат: формально всё правильно, но читать невозможно — канцелярит, общие фразы и ощущение, что текст писал не врач.
Поэтому держите шаблон, который помогает получить нормальный черновик медицинского поста,…
Как быстрее оформлять выписки после приёма — без текста с нуля каждый раз
Жалобы, анамнез, обследования, лечение, рекомендации…
Следующий пациент — всё начинаем с начала. Потом ещё раз. И ещё.
При этом у каждого врача уже есть свой привычный формат выписки: порядок блоков, формулировки, объём, оформление рекомендаций.
Его можно один раз показать ИИ и дальше использовать как рабочий шаблон.
Например 👇
Ты — медицин…
«Не время тратить деньги на обучение…,сейчас кризис.»
Когда вокруг много неопределённости, хочется осторожнее относиться к расходам и вкладываться только в то, что действительно пригодится.
Поэтому здесь важный вопрос: может ли навык работы с ИИ вернуть врачу вложенные деньги — хотя бы частично, через время, новые задачи и дополнительные возможности?
Опыт наших учеников показывает: Вполне может. Например 👇
1️⃣ Ос…
Пациент скорее мёртв, чем жив 😱
Примерно так многие думают о нейросетях после пары неудачных попыток.
Попробовали → получили странный ответ → закрыли вкладку.
Диагноз: «нерабочий инструмент».
⚡️ Но проблема часто не в самой нейросети.
Когда вы понимаете:
— какую модель выбрать под задачу;
— как сформулировать медицинский промпт;
— как замечать галлюцинации;
— как проверять источники и результат —
ситуация меняется…
Встреча закончилась. Кто теперь переслушает два часа записи?
Консилиум, рабочее совещание, занятие или разговор с иностранными коллегами. Во время встречи нужно слушать, отвечать, делать заметки и запоминать договорённости. В итоге важная мысль теряется, а к длинной записи никто не хочет возвращаться.
Мы подобрали три инструмента, которые берут эту работу на себя.
1️⃣ Tactiq — для встреч на иностранном языке
Расши…
«Я не могу полностью доверять ChatGPT. Вдруг ИИ снова выдумал статью?»
Такой риск действительно есть. Нейросеть может ошибиться.
Алгоритм действий 👇
⭐️ Работайте с конкретными источниками
Надёжнее загрузить в чат клинические рекомендации, статью или заранее проверенные ссылки и попросить нейросеть анализировать только их.
В запросе напишите:
«Используй только информацию из прикреплённых документов. Не добавляй све…
❤5👍3🔥3
Showing the 12 most recent of 44 posts we hold for @mdai_dima. View and reaction counts are the latest single reading for each post, not a live figure, and a recent post is still accumulating both. A view count marked ≈ was rounded by Telegram before we ever saw it — t.me prints views in full below 1,000 and to three significant figures above, so ≈1,200,000 means somewhere between 1,150,000 and 1,249,999. Unmarked counts are exact. Text is reproduced from the public post preview and truncated for length.
Stars beside a post are paid reactions — Telegram Stars, bought with money and spent on that post. They are a different unit from reactions and are never added to them, here or anywhere else on this page.
Posts edited after publishing
@mdai_dima edited 1 post after it first published — the same permalink now carries different wording than the one this register originally read, caught because our own crawl held a copy of the earlier text.
An edit is not deception. Typo fixes, price updates and corrections look exactly like this too — this register can tell you the wording changed and when, not why. How this is measured.
First edit seen
24 August 2026
Most recent edit
24 August 2026
Mentions
Named by 4 registered channels — every channel on the register whose own posts have named this one, by its current username or any other username it currently holds, merged from two separately captured readings of the same fact so a namer caught by only one of them is not missed and a namer both caught is not counted twice. A username this channel has since dropped is not matched — that handle may belong to someone else now, and crediting today’s namer to yesterday’s owner would misattribute it.
Named by
Channels on the register whose posts name this channel's handle.
A mention is a weaker signal than a forward and is counted separately for that reason — naming a channel is not republishing it, and a handle in a post body is easy to place deliberately. The post counts beside each row below are distinct posts in which the handle appeared, from posts we have read on both sides — the “Named by N registered channels” figure above is a different count, of distinct NAMING CHANNELS rather than posts, and is not the sum of the rows under it.
Cite this entry
A live page changes as we take new readings, so a citation should name the measurement it is based on, not just the URL. The line below cites the subscriber count as measured 10 September 2026 — this
entry's latest reading, not the date you are reading this.
“Нейросети для врачей | Нейросети в медицине | MD.school” (@mdai_dima), 13,081 subscribers as measured 10 September 2026. Telegram Register, tgregister.com/channel/mdai_dima.
Full measurement history, CC BY 4.0. Every reading this register holds for this entry, not just the latest one, as a dated, downloadable record: CSV · JSON. Free to use with attribution to tgregister.com. Each file carries its own generation timestamp, which is the figure to cite for exactly when the data was retrieved.