Even Your Best Reviewer Misses Things at a Conference. That's a Scale Problem, Not a Skill Problem.

Even Your Best Reviewer Misses Things at a Conference. That's a Scale Problem, Not a Skill Problem.

Every Medical Affairs team has that one person. The reviewer who has been to the meeting fifteen times, knows the field cold, and can read a session title and tell you which three abstracts matter and which twenty are noise. When conference season comes, the coverage plan runs through them.

But they will still miss things.

Not because they are not good enough. A major conference is bigger than any one expert's time, and the math does not work in their favor.

The math of a modern conference

Consider the scope of what you’re asking one person to do. A large scientific conference publishes thousands of abstracts. Pivotal data is embedded in parallel sessions, tucked away in poster halls with hundreds of boards. Satellite symposia, late-breaker slots, and guided poster tours overlap with the platform presentations you already planned to attend. The full program is released two weeks in advance, and the window to triage it is short, because the same people covering the meeting are usually the ones keeping the lights on across the company: running trials, handling medical monitoring, answering the field.

Many lean teams have no dedicated conference analyst. There’s limited field team to divide and conquer. There is a handful of people, a spreadsheet, and a program that goes on and on.

So your expert does what any good expert does under time pressure. They go where they expect the important science to be. They cover their lane thoroughly. And they make a reasonable bet that anything worth knowing will be flagged by keywords or obvious based on the titles.

That bet is where things get missed.

What actually slips through

The abstracts an expert misses are rarely the obvious ones. The headline data in core sessions gets caught every time. What slips through sits at the edges:

A competitor's molecule showing up in an adjacent indication you were not watching. An early-stage program with an unfamiliar code name, buried in a poster. The same mechanism of action appearing in three different talks across three different days, a pattern that only becomes visible if someone is looking across the whole program at once rather than session by session. Something impossible to do at this scale and under these time constraints.

These are exactly the signals Medical Affairs is meant to catch. They are the off-agenda insights, the competitive early warnings, the connections that shape strategy. And they are the first casualties of a program that is too big to read and process in a few days.

None of this is a failure of expertise. The MA team member’s judgment is the most valuable thing you have. The problem is that judgment can only be applied to what is actually reviewed, and at congress scale, a lot can be inadvertently overlooked.

Where AI actually fits

Here is the part that tends to get miscast. The value of AI in conference coverage is not that it replaces the expert's judgment. It is that it makes sure the expert's judgment gets applied to a complete set instead of a partial one.

A systematic pass across the entire program does the thing a human under time pressure cannot: it reads everything. Every abstract, every session, every poster, against the assets, competitors, and mechanisms the team actually cares about. It does not get tired at abstract four hundred. It does not miss the poster tour because the platform sessions ran long. And because good tools match on concepts rather than exact words, it catches the priority abstract that never used your keyword, the one a manual search would have walked right past.

What comes back is not a decision. It is a complete, triaged set: here is everything in the program that touches your priorities, sorted by how much it matters, with the off-lane items flagged rather than buried. The expert still decides what is worth their time on the ground. They just get to start from everything, instead of from whatever they had time to curate, often using keywords that can lack sensitivity and specificity.

Reviewing is not redoing

Teams sometimes worry that checking an AI-generated coverage list is just duplicate work. In practice it is the opposite. When your expert reviews a comprehensive set, they are not re-reading the program. They are doing the one thing only they can do, which is apply judgment to a complete picture. The items they already knew about confirm the pass is calibrated to their priorities. The items or synergies they did not know about are the entire point. That is not verification as a chore. That is verification as the moment coverage becomes better than it was last year and that the expert gets to learn the data without first sifting through haystacks that may or may not contain a needle.

And the patterns that surface from a full-program sweep, the mechanism showing up in three places, a competitor quietly building a case across a poster series, are the kind of connections a person would need weeks to assemble by hand, if they ever assembled them at all.

The takeaway

If your congress coverage depends entirely on how much of the program one talented person can get through before the meeting starts, your coverage has a ceiling, and it is not set by their ability. It is set by the clock.

The fix is not a better reviewer. It is giving the reviewer you already have a tool and empowering them with a complete set to start with.

That is what conference intelligence for Medical Affairs is built to do. Datym is an AI-powered conference intelligence platform for pharmaceutical and biotech Medical Affairs teams. It reads the full program against your priorities and turns it into the deliverables your team already uses, from pre-conference coverage grids to post-conference synthesis, so nothing relevant reaches the meeting without first reaching your desk.

Your experts are the reason coverage is good. Give them the one thing the time pressure of conference coverage keeps taking away, which is the whole picture.