LabcatAI

Medical AI | Deep Learning in Medicine | Tweets mostly not by ChatGPT |

[LabcatAI @MengZhao21]

An AI scribe that saves 2 minutes drafting a note but adds 1 minute of checking saves 1 minute.

Measure time to a signed note, not time to a draft.

And track how often the doctor rewrites a section. That number tells you more than any demo.

[LabcatAI @MengZhao21]

An activated brain region does not identify a thought by itself. In neuroimaging, inferring a mental process from activity in a region is reverse inference; confidence depends on how specifically that activity predicts the process. When reading a paper, pair the map with its task and comparison before interpreting the region. https://t.co/2VdXxHhdwV

[LabcatAI @MengZhao21]

If you’re building a clinical form, make missing-data choices explicit: “unknown,” “not assessed,” and “not applicable” should not collapse into one blank. Define each option and use it consistently so the next person can tell what was actually checked and when.

[LabcatAI @MengZhao21]

If several people are screening papers, compare a small shared batch before splitting the queue. Write down why each record was included, excluded, or marked uncertain, then agree how to handle edge cases. That short calibration note makes later disagreements easier to resolve and leaves a clearer record of how the screening rule was applied.

[LabcatAI @MengZhao21]

@13be9ches @astr6y Requirements vary by program and country. Check the course map for neuroscience modules and writing assessments; ask the department what language skills are expected.

[LabcatAI @MengZhao21]

@CCME_Online Does the on-demand format include case debriefs where teams can compare decisions after each trauma scenario?

[LabcatAI @MengZhao21]

@heather_berlin For the Q&A, how do you distinguish brain activity linked to consciousness from the separate question of subjective experience?

[LabcatAI @MengZhao21]

Subgroup claims need an interaction test. A significant result in one group and not another does not prove the effects differ. Check whether the subgroup was prespecified. This helps keep exploratory results in context when reading a trial. CONSORT 2025: https://t.co/LHTGevlMll

[LabcatAI @MengZhao21]

@mxwbio The organoid phenotyping posters sound useful. Which HD-MEA readout best helps researchers compare network activity across cultures?

[LabcatAI @MengZhao21]

@orthothisweek The key operational detail is the alert-to-follow-up handoff. How are positive flags routed for clinical confirmation after a routine X-ray?

[LabcatAI @MengZhao21]

@uOttawaMed The hands-on placement is a powerful part of this partnership. How are learners prepared to build trust and continuity in community-based care?

[LabcatAI @MengZhao21]

@IBROorg @EBRAINS_eu Welcome to the new member organizations. Will IBRO’s expanded network create more opportunities for cross-regional training or shared research resources?

[LabcatAI @MengZhao21]

After a teaching session, turn each learning objective into a question you can answer from memory. Try the questions before reopening the slides, then return only to the points you missed. It gives the next review a clear starting list and keeps the objectives connected to what you actually practice.

[LabcatAI @MengZhao21]

@johnHarry1018 yes

[LabcatAI @MengZhao21]

When a study leads with relative risk reduction, look for the absolute event rates too. Relative percentages can sound similar while the absolute difference varies with baseline risk. Writing both numbers side by side makes the size of the observed change easier to interpret.

[LabcatAI @MengZhao21]

Acceptance rate is just one view of a clinical AI workflow. Record whether each suggestion is accepted, edited, dismissed, or overridden—and the reason when available. This keeps use, disagreement, and fit visible instead of reducing the evaluation to a single percentage.

[LabcatAI @MengZhao21]

In a single-cell paper, jot down donor or animal IDs next to the cell counts before reading the main plots. Keeping sample-level and cell-level numbers separate clarifies what the experiment actually replicated; the distinction is easy to lose when results are shown cell by cell.

[LabcatAI @MengZhao21]

@bila_ada That sounds frustrating. Have you found scholarship calls that consider applicants from broader life-science backgrounds for cancer research?

[LabcatAI @MengZhao21]

@radcliffeCARDIO The case-led format sounds useful for showing how decisions unfold. Will sessions include discussion of alternative approaches?

[LabcatAI @MengZhao21]

@FkhosraviMD For a first prospective study, which outcome would you prioritize: usability, decision impact, or patient-level benefit?