A wide variety of AI tools to search for Vitamin D information

Yes. For your VitaminDWiki research, I would use these tools somewhat differently rather than trying to pick a single winner.

ChatGPT comparison - Sept 2026

Tool	Best use for VitaminDWiki	Finding obscure papers	RCT identification	Extracting study details	Checking whether findings held up	Large literature reviews
Google Scholar Labs	Broad discovery	Excellent	Good	Limited	Limited	Good
Consensus	Medical evidence questions	Very good	Excellent	Very good	Moderate	Excellent
Elicit	Systematic reviews	Very good	Excellent	Excellent	Moderate	Excellent
Scite	Checking evidence/citations	Excellent	Very good	Very good	Excellent	Good
ChatGPT + web	Analysis, synthesis & writing	Good	Good	Excellent when given papers	Good	Excellent

There are some significant differences behind those ratings.

1. Google Scholar Labs — use it as the "wide net"

Google Scholar Labs

This is the tool I would use when your concern is:

"What research exists on this subject?"

Its biggest advantage is its relationship to Google Scholar's enormous scholarly index. That's particularly valuable for your work because you frequently want older papers, unusual papers, papers outside mainstream medical databases, and papers that use terminology you didn't anticipate.

For example, instead of searching:

vitamin D Parkinson's randomized controlled trial

you could ask Scholar Labs:

"Find human studies investigating whether vitamin D supplementation affects Parkinson's disease, including randomized trials, observational studies, and older studies using different terminology."

I'd use Scholar Labs to discover the universe of potentially relevant papers.

2. Consensus — probably the easiest medical-literature research tool

Consensus

Consensus currently searches 220+ million research papers, including all of PubMed, and its medical mode searches a curated subset of about 8 million papers plus 50,000 clinical guidelines. It can filter by study design, population, sample size, duration, publication date, citations, journal rank and other characteristics. (Consensus Help Center)

That's very useful for questions such as:

"What randomized controlled trials have tested vitamin D supplementation for depression?"

or:

"What human studies have measured achieved 25(OH)D levels rather than simply vitamin D dose?"

Consensus can extract things such as:

population
intervention
comparator
sample size
methodology
outcomes
study duration
agreement/disagreement between studies

Its Deep Review can synthesize roughly 50–100 papers. (Consensus Help Center)

Consensus - first stop

I'd probably make Consensus your first stop for a focused medical question.

3. Elicit — particularly powerful for VitaminDWiki

Elicit

This one deserves special attention for you.

Elicit is designed around the systematic-review workflow, rather than simply answering questions.

Its current system searches about 138 million academic papers and more than 545,000 clinical trials. It can screen large numbers of papers and extract structured information from the full text. (Elicit)

For example, imagine you wanted to investigate:

Vitamin D supplementation and cancer mortality

You could have Elicit create columns such as:

Paper	Design	N	Vitamin D dose	Baseline 25(OH)D	Achieved 25(OH)D	Follow-up	Cancer outcome	Mortality	Significant?

That is very close to the kind of evidence table you often need for VitaminDWiki.

Elicit also supports PRISMA-oriented systematic-review workflows, screening, extraction and audit trails. (Elicit)

This is probably Elicit's biggest advantage for you:

It can turn a pile of papers into a structured dataset.

That's much more valuable than merely getting an AI-generated paragraph saying "studies suggest..."

4. Scite — the "is this finding actually supported?" tool

Scite

Scite does something the others don't do nearly as well.

Its Smart Citations examine how subsequent papers cite a study and classify citation statements as:

supporting
contrasting
mentioning

Scite currently reports more than 1.6 billion Smart Citations and more than 300 million indexed articles, including full-text material. (scite.ai)

This is extremely useful for VitaminDWiki.

Suppose you find a 2014 paper saying:

Vitamin D supplementation improves outcome X.

You don't necessarily want to stop there.

Scite can help answer:

"What happened to this claim after the original paper was published?"

Were later studies:

confirming it?
contradicting it?
merely citing it?
failing to reproduce it?

That's a major distinction.

An especially interesting development

Scite now has an MCP connection to ChatGPT. It allows an AI assistant to search Scite's literature, search inside full-text papers, and retrieve Smart Citation information. (scite.ai)

So this isn't merely "another website."

It can become part of an AI research workflow.

5. ChatGPT — I would use me as the "research analyst"

This is where I think the combination becomes much more useful than any individual tool.

I wouldn't want you to use an AI simply to ask:

"Does vitamin D prevent cancer?"

and accept its answer.

Instead:

Step 1 — Discovery

Google Scholar Labs

Find the broad literature.

↓

Step 2 — Evidence identification

Consensus

Identify:

RCTs
meta-analyses
human studies
relevant populations
dose
duration

↓

_ Step 3 — Systematic extraction: Elicit_

Build a structured evidence table.

↓

Step 4 — Challenge the evidence: Scite

Ask: What subsequent research supports or contradicts these papers?

Step 5 — Analysis: ChatGPT

Give me the resulting papers/data and ask me to:

reconcile conflicting studies
distinguish association from causation
distinguish vitamin-D status from supplementation
examine dose-response
examine achieved 25(OH)D
identify methodological problems
identify missing evidence
compare RCTs with observational studies
write the VitaminDWiki page

That last distinction is important.

For VitaminDWiki specifically, I'd rank the workflows this way

Not as a general "best to worst" ranking, but by what job each is particularly suited for:

🔎 Finding papers you didn't know existed

Google Scholar Labs

🧪 Finding clinical trials/RCT evidence

Consensus + Elicit

📊 Extracting numerical study data

Elicit

🔬 Determining whether a published finding has subsequently been supported

Scite

🧠 Understanding the scientific argument

ChatGPT

✍️ Producing the final VitaminDWiki article

ChatGPT + the underlying papers

One thing I would change about your current research process

Given the way you've been doing VitaminDWiki research, I think we could make a much more rigorous workflow than simply asking me to research a topic on the web.

For a major topic, I'd suggest creating a VitaminDWiki Evidence Pipeline:

Google Scholar Labs → broad paper discovery

Consensus → clinical/RCT identification

Elicit → structured extraction

Scite → subsequent confirmation/contradiction

ChatGPT → critical analysis

VitaminDWiki → final article

And importantly, we could have the output from Elicit become a standardized table with fields such as:

Study | Year | Country | N | Design | Intervention | Dose | Baseline 25(OH)D | Achieved 25(OH)D | Control | Duration | Outcome | Effect | P-value | Adverse events | Limitations

That would make it much easier to compare studies across different VitaminDWiki subjects.

A possibility: Scite integration with ChatGPT

Scite can now connect directly to ChatGPT, so we could potentially eliminate one manual step: ChatGPT could use Scite's literature database and citation-context information during the research process. (scite.ai)

If you want, I can next look specifically at the costs and free/paid limits of Google Scholar Labs, Consensus, Elicit and Scite, and recommend a lowest-cost setup for VitaminDWiki.