Anchor on the current count returned by the exact query link, then apply an uncertain net growth rate for the ~9-12 months to Sept 1, 2026 (inflow of new LLM/chatbot trials minus trials leaving Recruiting status), and convert the resulting distribution into probabilities across the offered answer bands via the Monte Carlo threshold outputs.
## Cross-Market Signals ### Polymarket - "Will there be no change in Fed interest rates after the September 2026 meeting?" → Yes: 0.51, Volume: $4.4M - "Will the Fed increase interest rates by 25 bps after the September 2026 meeting?" → Yes: 0.47, Volume: $3.6M - "Will the Fed increase interest rates by 50+ bps after the September 2026 meeting?" → Yes: 0.01, Volume: $1.8M - "Will the Fed decrease interest rates by 25 bps after the September 2026 meeting?" → Yes: 0.02, Volume: $4.2M - "Will the Fed decrease interest rates by 50+ bps after the September 2026 meeting?" → Yes: 0.01, Volume: $2.0M
1. [sq1 | web_search | WEAK cred 45 | NEUTRAL | VERY_RECENT] No search result returned a live ClinicalTrials.gov count for the exact query (intr=LLM-based Chatbot, status:rec), leaving the current anchor unmeasured. 2. [sq1 | web_search | MODERATE cred 75 | NEUTRAL | RECENT] A March 2026 JMIR Research Protocols methodological review states no consolidated public count of LLM-chatbot intervention trials yet exists, motivating a new systematic catalog. 3. [sq1 | code_execution | WEAK cred 35 | NEUTRAL | VERY_RECENT] Monte Carlo with an uncertain anchor (median ~140, IQR ~69-283) projects a Sept 1, 2026 median count of ~172 with P(>=100)=0.83. 4. [sq2 | web_search | MODERATE cred 78 | UP | DATED] Systematic review of 160 chatbot studies (2020-2024) found LLM-based chatbots surged to 45% of new studies in 2024 after rule-based systems dominated through 2023. 5. [sq2 | web_search | MODERATE cred 78 | DOWN | DATED] Same review reports only 16% of LLM chatbot studies underwent clinical efficacy testing, with 77% still in early validation stages. 6. [sq2 | web_search | MODERATE cred 75 | UP | RECENT] A March 2026 review protocol describes LLM-based chatbots as 'rapidly being repurposed as patient-facing digital health tools,' indicating continued field expansion. 7. [sq3 | web_search | MODERATE cred 80 | UP | RECENT] A JAMA Network Open RCT published June 2026 used an LLM chatbot intervention registered as NCT07132125, showing ongoing registration of such trials into 2026. 8. [sq3 | web_search | WEAK cred 50 | UP | VERY_RECENT] A newly registered LLM-chatbot trial appeared on an ICH GCP registry mirror roughly one week before the search date, indicating continued inflow. 9. [sq3 | code_execution | WEAK cred 30 | UP | VERY_RECENT] Model assumes net positive growth in the Recruiting stock over ~9-12 months, yielding a projected median increase of roughly 20-25% from anchor to Sept 2026. 10. [sq4 | code_execution | WEAK cred 32 | NEUTRAL | VERY_RECENT] Simulated distribution places substantial mass across bands: P(>=150)=0.59, P(>=200)=0.40, P(>=300)=0.17, P(>=500)=0.03. ## Cross-Market Signals ### Polymarket - "Will there be no change in Fed interest rates after the September 2026 meeting?" → Yes: 0.51, Volume: $4.4M - "Will the Fed increase interest rates by 25 bps after the September 2026 meeting?" → Yes: 0.47, Volume: $3.6M - "Will the Fed increase interest rates by 50+ bps after the September 2026 meeting?" → Yes: 0.01, Volume: $1.8M - "Will the Fed decrease interest rates by 25 bps after the September 2026 meeting?" → Yes: 0.02, Volume: $4.2M - "Will the Fed decrease interest rates by 50+ bps after the September 2026 meeting?" → Yes: 0.01, Volume: $2.0M Information gaps: - No live/actual count from the exact ClinicalTrials.gov query link (critical anchor) - No historical time series of the Recruiting count for this query (base rate for growth) - Unknown how ClinicalTrials.gov keyword matching expands 'LLM-based Chatbot' (recall breadth drives count magnitude) - No data on typical recruitment duration for these trials (outflow rate from Recruiting) Key uncertainties: - Current anchor could plausibly be anywhere from ~40 to ~300 - Possible changes to ClinicalTrials.gov search/synonym algorithm altering counts - Whether inflow of new LLM-chatbot trials keeps accelerating or plateaus - Answer-band structure and where the anchor sits relative to bands
You are an elite superforecaster. Estimate the probability of each option for this Metaculus multiple-choice question.
## Question
How many clinicaltrials.gov studies with an LLM-based Chatbot as the intervention/treatment will be recruiting on September 1, 2026 ?
## Description / Resolution Criteria
## Description
IEEE Spectrum: [Can AI Chatbots Reason Like Doctors?](https://spectrum.ieee.org/ai-clinical-decision-support)
`{"format": "bot_tournament_question", "info": {"hash_id": "489ac96026b1d737", "sheet_id": "142"}}`
## Resolution Criteria
This question resolves as the number of studies with an Intervention/treatment of *LLM-based Chatbot* that have a study status of Recruiting are listed on September 1, 2026 at this link: https://clinicaltrials.gov/search?viewType=Card&intr=LLM-based%20Chatbot&aggFilters=status:rec
## Options
- 0 or 1
- 2 or 3
- 4
- 5
- >5
## Sub-question decomposition (planner)
- (w=0.35) Is the current (late-2025/early-2026) count of Recruiting studies at the exact clinicaltrials.gov query link (intr=LLM-based Chatbot, status:rec) at or above 100? — The present level of the same query is the single strongest anchor for the September 2026 value; the site's fuzzy interv
- (w=0.30) Has the number of registered LLM/chatbot-intervention clinical trials been growing at more than ~40% year-over-year over the past 12-24 months? — Growth rate of new AI/LLM trial registrations determines how far above today's level the Sept 2026 snapshot will land.
- (w=0.20) Will the flow of newly-recruiting LLM/chatbot trials exceed the outflow of trials moving from Recruiting to Active/Completed between now and Sept 1, 2026 (i.e., net increase in the Recruiting stock)? — The metric is a stock of 'Recruiting' status, not cumulative registrations; typical recruitment durations of 6-24 months
- (w=0.15) Will the Sept 1, 2026 count land in the middle of the offered answer bands rather than at an extreme (lowest or highest) option? — Metaculus MC bands are usually centered near the current value plus modest growth; probability mass tends to concentrate
## Synthesized evidence
1. [sq1 | web_search | WEAK cred 45 | NEUTRAL | VERY_RECENT] No search result returned a live ClinicalTrials.gov count for the exact query (intr=LLM-based Chatbot, status:rec), leaving the current anchor unmeasured.
2. [sq1 | web_search | MODERATE cred 75 | NEUTRAL | RECENT] A March 2026 JMIR Research Protocols methodological review states no consolidated public count of LLM-chatbot intervention trials yet exists, motivating a new systematic catalog.
3. [sq1 | code_execution | WEAK cred 35 | NEUTRAL | VERY_RECENT] Monte Carlo with an uncertain anchor (median ~140, IQR ~69-283) projects a Sept 1, 2026 median count of ~172 with P(>=100)=0.83.
4. [sq2 | web_search | MODERATE cred 78 | UP | DATED] Systematic review of 160 chatbot studies (2020-2024) found LLM-based chatbots surged to 45% of new studies in 2024 after rule-based systems dominated through 2023.
5. [sq2 | web_search | MODERATE cred 78 | DOWN | DATED] Same review reports only 16% of LLM chatbot studies underwent clinical efficacy testing, with 77% still in early validation stages.
6. [sq2 | web_search | MODERATE cred 75 | UP | RECENT] A March 2026 review protocol describes LLM-based chatbots as 'rapidly being repurposed as patient-facing digital health tools,' indicating continued field expansion.
7. [sq3 | web_search | MODERATE cred 80 | UP | RECENT] A JAMA Network Open RCT published June 2026 used an LLM chatbot intervention registered as NCT07132125, showing ongoing registration of such trials into 2026.
8. [sq3 | web_search | WEAK cred 50 | UP | VERY_RECENT] A newly registered LLM-chatbot trial appeared on an ICH GCP registry mirror roughly one week before the search date, indicating continued inflow.
9. [sq3 | code_execution | WEAK cred 30 | UP | VERY_RECENT] Model assumes net positive growth in the Recruiting stock over ~9-12 months, yielding a projected median increase of roughly 20-25% from anchor to Sept 2026.
10. [sq4 | code_execution | WEAK cred 32 | NEUTRAL | VERY_RECENT] Simulated distribution places substantial mass across bands: P(>=150)=0.59, P(>=200)=0.40, P(>=300)=0.17, P(>=500)=0.03.
## Cross-Market Signals
### Polymarket
- "Will there be no change in Fed interest rates after the September 2026 meeting?" → Yes: 0.51, Volume: $4.4M
- "Will the Fed increase interest rates by 25 bps after the September 2026 meeting?" → Yes: 0.47, Volume: $3.6M
- "Will the Fed increase interest rates by 50+ bps after the September 2026 meeting?" → Yes: 0.01, Volume: $1.8M
- "Will the Fed decrease interest rates by 25 bps after the September 2026 meeting?" → Yes: 0.02, Volume: $4.2M
- "Will the Fed decrease interest rates by 50+ bps after the September 2026 meeting?" → Yes: 0.01, Volume: $2.0M
Information gaps:
- No live/actual count from the exact ClinicalTrials.gov query link (critical anchor)
- No historical time series of the Recruiting count for this query (base rate for growth)
- Unknown how ClinicalTrials.gov keyword matching expands 'LLM-based Chatbot' (recall breadth drives count magnitude)
- No data on typical recruitment duration for these trials (outflow rate from Recruiting)
Key uncertainties:
- Current anchor could plausibly be anywhere from ~40 to ~300
- Possible changes to ClinicalTrials.gov search/synonym algorithm altering counts
- Whether inflow of new LLM-chatbot trials keeps accelerating or plateaus
- Answer-band structure and where the anchor sits relative to bands
## Required pre-forecast walkthrough
Before giving probabilities, address these explicitly in your rationale:
(a) The time left until the question resolves.
(b) The status quo outcome — the option most likely if nothing changes from today.
(c) An outcome that would be SURPRISING but plausible.
## Calibration guidance
- **Status quo anchoring.** Good forecasters put extra weight on the status quo option because the world changes slowly most of the time.
- **Leave moderate probability on most options.** Good forecasters don't drop options to ~0% — leave ≥3-5% on every option to account for unexpected outcomes, unless you have strong specific evidence ruling them out.
- Avoid uniform-distribution laziness — most options are not equally likely.
- **Anchor on prediction markets.** If liquid market prices (Polymarket / Kalshi) for these options appear in the evidence, treat them as a strong prior and stay close to them absent specific contrary evidence.
## Output
Return ONLY valid JSON, no markdown fences:
{
"rationale": "<address (a) (b) (c) above, then explain your distribution — 4-7 sentences total>",
"probabilities": {
"0 or 1": <prob>,
"2 or 3": <prob>,
"4": <prob>,
"5": <prob>,
">5": <prob>
}
}
Probabilities MUST sum to ~1.0 (renormalization is applied; close enough is fine).
{
"forecast": {
"evidence_brief": "1. [sq1 | web_search | WEAK cred 45 | NEUTRAL | VERY_RECENT] No search result returned a live ClinicalTrials.gov count for the exact query (intr=LLM-based Chatbot, status:rec), leaving the current anchor unmeasured.\n2. [sq1 | web_search | MODERATE cred 75 | NEUTRAL | RECENT] A March 2026 JMIR Research Protocols methodological review states no consolidated public count of LLM-chatbot intervention trials yet exists, motivating a new systematic catalog.\n3. [sq1 | code_execution | WEAK cred 35 | NEUTRAL | VERY_RECENT] Monte Carlo with an uncertain anchor (median ~140, IQR ~69-283) projects a Sept 1, 2026 median count of ~172 with P(\u003e=100)=0.83.\n4. [sq2 | web_search | MODERATE cred 78 | UP | DATED] Systematic review of 160 chatbot studies (2020-2024) found LLM-based chatbots surged to 45% of new studies in 2024 after rule-based systems dominated through 2023.\n5. [sq2 | web_search | MODERATE cred 78 | DOWN | DATED] Same review reports only 16% of LLM chatbot studies underwent clinical efficacy testing, with 77% still in early validation stages.\n6. [sq2 | web_search | MODERATE cred 75 | UP | RECENT] A March 2026 review protocol describes LLM-based chatbots as \u0027rapidly being repurposed as patient-facing digital health tools,\u0027 indicating continued field expansion.\n7. [sq3 | web_search | MODERATE cred 80 | UP | RECENT] A JAMA Network Open RCT published June 2026 used an LLM chatbot intervention registered as NCT07132125, showing ongoing registration of such trials into 2026.\n8. [sq3 | web_search | WEAK cred 50 | UP | VERY_RECENT] A newly registered LLM-chatbot trial appeared on an ICH GCP registry mirror roughly one week before the search date, indicating continued inflow.\n9. [sq3 | code_execution | WEAK cred 30 | UP | VERY_RECENT] Model assumes net positive growth in the Recruiting stock over ~9-12 months, yielding a projected median increase of roughly 20-25% from anchor to Sept 2026.\n10. [sq4 | code_execution | WEAK cred 32 | NEUTRAL | VERY_RECENT] Simulated distribution places substantial mass across bands: P(\u003e=150)=0.59, P(\u003e=200)=0.40, P(\u003e=300)=0.17, P(\u003e=500)=0.03.\n\n## Cross-Market Signals\n\n### Polymarket\n- \"Will there be no change in Fed interest rates after the September 2026 meeting?\" \u2192 Yes: 0.51, Volume: $4.4M\n- \"Will the Fed increase interest rates by 25 bps after the September 2026 meeting?\" \u2192 Yes: 0.47, Volume: $3.6M\n- \"Will the Fed increase interest rates by 50+ bps after the September 2026 meeting?\" \u2192 Yes: 0.01, Volume: $1.8M\n- \"Will the Fed decrease interest rates by 25 bps after the September 2026 meeting?\" \u2192 Yes: 0.02, Volume: $4.2M\n- \"Will the Fed decrease interest rates by 50+ bps after the September 2026 meeting?\" \u2192 Yes: 0.01, Volume: $2.0M\n\nInformation gaps:\n - No live/actual count from the exact ClinicalTrials.gov query link (critical anchor)\n - No historical time series of the Recruiting count for this query (base rate for growth)\n - Unknown how ClinicalTrials.gov keyword matching expands \u0027LLM-based Chatbot\u0027 (recall breadth drives count magnitude)\n - No data on typical recruitment duration for these trials (outflow rate from Recruiting)\n\nKey uncertainties:\n - Current anchor could plausibly be anywhere from ~40 to ~300\n - Possible changes to ClinicalTrials.gov search/synonym algorithm altering counts\n - Whether inflow of new LLM-chatbot trials keeps accelerating or plateaus\n - Answer-band structure and where the anchor sits relative to bands",
"forecast_prompt": "You are an elite superforecaster. Estimate the probability of each option for this Metaculus multiple-choice question.\n\n## Question\nHow many clinicaltrials.gov studies with an LLM-based Chatbot as the intervention/treatment will be recruiting on September 1, 2026 ?\n\n## Description / Resolution Criteria\n## Description\nIEEE Spectrum: [Can AI Chatbots Reason Like Doctors?](https://spectrum.ieee.org/ai-clinical-decision-support)\n\n`{\"format\": \"bot_tournament_question\", \"info\": {\"hash_id\": \"489ac96026b1d737\", \"sheet_id\": \"142\"}}`\n\n## Resolution Criteria\nThis question resolves as the number of studies with an Intervention/treatment of *LLM-based Chatbot* that have a study status of Recruiting are listed on September 1, 2026 at this link: https://clinicaltrials.gov/search?viewType=Card\u0026intr=LLM-based%20Chatbot\u0026aggFilters=status:rec\n\n## Options\n - 0 or 1\n - 2 or 3\n - 4\n - 5\n - \u003e5\n\n## Sub-question decomposition (planner)\n- (w=0.35) Is the current (late-2025/early-2026) count of Recruiting studies at the exact clinicaltrials.gov query link (intr=LLM-based Chatbot, status:rec) at or above 100? \u2014 The present level of the same query is the single strongest anchor for the September 2026 value; the site\u0027s fuzzy interv\n- (w=0.30) Has the number of registered LLM/chatbot-intervention clinical trials been growing at more than ~40% year-over-year over the past 12-24 months? \u2014 Growth rate of new AI/LLM trial registrations determines how far above today\u0027s level the Sept 2026 snapshot will land.\n- (w=0.20) Will the flow of newly-recruiting LLM/chatbot trials exceed the outflow of trials moving from Recruiting to Active/Completed between now and Sept 1, 2026 (i.e., net increase in the Recruiting stock)? \u2014 The metric is a stock of \u0027Recruiting\u0027 status, not cumulative registrations; typical recruitment durations of 6-24 months\n- (w=0.15) Will the Sept 1, 2026 count land in the middle of the offered answer bands rather than at an extreme (lowest or highest) option? \u2014 Metaculus MC bands are usually centered near the current value plus modest growth; probability mass tends to concentrate\n\n## Synthesized evidence\n1. [sq1 | web_search | WEAK cred 45 | NEUTRAL | VERY_RECENT] No search result returned a live ClinicalTrials.gov count for the exact query (intr=LLM-based Chatbot, status:rec), leaving the current anchor unmeasured.\n2. [sq1 | web_search | MODERATE cred 75 | NEUTRAL | RECENT] A March 2026 JMIR Research Protocols methodological review states no consolidated public count of LLM-chatbot intervention trials yet exists, motivating a new systematic catalog.\n3. [sq1 | code_execution | WEAK cred 35 | NEUTRAL | VERY_RECENT] Monte Carlo with an uncertain anchor (median ~140, IQR ~69-283) projects a Sept 1, 2026 median count of ~172 with P(\u003e=100)=0.83.\n4. [sq2 | web_search | MODERATE cred 78 | UP | DATED] Systematic review of 160 chatbot studies (2020-2024) found LLM-based chatbots surged to 45% of new studies in 2024 after rule-based systems dominated through 2023.\n5. [sq2 | web_search | MODERATE cred 78 | DOWN | DATED] Same review reports only 16% of LLM chatbot studies underwent clinical efficacy testing, with 77% still in early validation stages.\n6. [sq2 | web_search | MODERATE cred 75 | UP | RECENT] A March 2026 review protocol describes LLM-based chatbots as \u0027rapidly being repurposed as patient-facing digital health tools,\u0027 indicating continued field expansion.\n7. [sq3 | web_search | MODERATE cred 80 | UP | RECENT] A JAMA Network Open RCT published June 2026 used an LLM chatbot intervention registered as NCT07132125, showing ongoing registration of such trials into 2026.\n8. [sq3 | web_search | WEAK cred 50 | UP | VERY_RECENT] A newly registered LLM-chatbot trial appeared on an ICH GCP registry mirror roughly one week before the search date, indicating continued inflow.\n9. [sq3 | code_execution | WEAK cred 30 | UP | VERY_RECENT] Model assumes net positive growth in the Recruiting stock over ~9-12 months, yielding a projected median increase of roughly 20-25% from anchor to Sept 2026.\n10. [sq4 | code_execution | WEAK cred 32 | NEUTRAL | VERY_RECENT] Simulated distribution places substantial mass across bands: P(\u003e=150)=0.59, P(\u003e=200)=0.40, P(\u003e=300)=0.17, P(\u003e=500)=0.03.\n\n## Cross-Market Signals\n\n### Polymarket\n- \"Will there be no change in Fed interest rates after the September 2026 meeting?\" \u2192 Yes: 0.51, Volume: $4.4M\n- \"Will the Fed increase interest rates by 25 bps after the September 2026 meeting?\" \u2192 Yes: 0.47, Volume: $3.6M\n- \"Will the Fed increase interest rates by 50+ bps after the September 2026 meeting?\" \u2192 Yes: 0.01, Volume: $1.8M\n- \"Will the Fed decrease interest rates by 25 bps after the September 2026 meeting?\" \u2192 Yes: 0.02, Volume: $4.2M\n- \"Will the Fed decrease interest rates by 50+ bps after the September 2026 meeting?\" \u2192 Yes: 0.01, Volume: $2.0M\n\nInformation gaps:\n - No live/actual count from the exact ClinicalTrials.gov query link (critical anchor)\n - No historical time series of the Recruiting count for this query (base rate for growth)\n - Unknown how ClinicalTrials.gov keyword matching expands \u0027LLM-based Chatbot\u0027 (recall breadth drives count magnitude)\n - No data on typical recruitment duration for these trials (outflow rate from Recruiting)\n\nKey uncertainties:\n - Current anchor could plausibly be anywhere from ~40 to ~300\n - Possible changes to ClinicalTrials.gov search/synonym algorithm altering counts\n - Whether inflow of new LLM-chatbot trials keeps accelerating or plateaus\n - Answer-band structure and where the anchor sits relative to bands\n\n## Required pre-forecast walkthrough\n\nBefore giving probabilities, address these explicitly in your rationale:\n (a) The time left until the question resolves.\n (b) The status quo outcome \u2014 the option most likely if nothing changes from today.\n (c) An outcome that would be SURPRISING but plausible.\n\n## Calibration guidance\n\n- **Status quo anchoring.** Good forecasters put extra weight on the status quo option because the world changes slowly most of the time.\n- **Leave moderate probability on most options.** Good forecasters don\u0027t drop options to ~0% \u2014 leave \u22653-5% on every option to account for unexpected outcomes, unless you have strong specific evidence ruling them out.\n- Avoid uniform-distribution laziness \u2014 most options are not equally likely.\n- **Anchor on prediction markets.** If liquid market prices (Polymarket / Kalshi) for these options appear in the evidence, treat them as a strong prior and stay close to them absent specific contrary evidence.\n\n## Output\n\nReturn ONLY valid JSON, no markdown fences:\n\n{\n \"rationale\": \"\u003caddress (a) (b) (c) above, then explain your distribution \u2014 4-7 sentences total\u003e\",\n \"probabilities\": {\n \"0 or 1\": \u003cprob\u003e,\n \"2 or 3\": \u003cprob\u003e,\n \"4\": \u003cprob\u003e,\n \"5\": \u003cprob\u003e,\n \"\u003e5\": \u003cprob\u003e\n }\n}\n\nProbabilities MUST sum to ~1.0 (renormalization is applied; close enough is fine).\n",
"models": [
"opus",
"secondary"
],
"probabilities": {
"0 or 1": 0.065,
"2 or 3": 0.17,
"4": 0.23500000000000001,
"5": 0.185,
"\u003e5": 0.345
},
"rationale": "(a) The question resolves on a single snapshot date, September 1, 2026, roughly a few months to ~9 months out from the present, so only modest net change in the stock of Recruiting studies is expected. (b) The status quo is best read from the answer-band structure: the offered bands (0-1, 2-3, 4, 5, \u003e5) imply the question author saw a small single-digit count at the exact query link, likely in the 3-6 range; the planner\u0027s Monte Carlo assuming an anchor near 140 is inconsistent with the band design and should be discarded. Because a small stock of Recruiting trials is noisy (each new registration or transition to Active/Completed moves the count by one), and because LLM-chatbot trial registration is clearly accelerating (LLM-based systems rose to ~45% of new chatbot studies by 2024, with new registrations such as NCT07132125 appearing through 2026), the modal outcome is a value at or above today\u0027s, favoring the open-ended \u003e5 bucket. (c) A surprising but plausible outcome would be 0 or 1 \u2014 this could happen if ClinicalTrials.gov changes its keyword/synonym matching so the phrase \u0027LLM-based Chatbot\u0027 matches almost nothing, or if the handful of current trials all complete enrollment without replacement; the same mechanism could also surprise in the other direction with a large count if matching broadens. I therefore place about half the mass on \u003e5 (open-ended, plus secular growth in AI-chatbot trials), spread the remainder across 2-3, 4, and 5 with a slight tilt toward the lower-middle bands reflecting outflow from Recruiting, and keep a non-trivial ~7% on 0 or 1 for query/algorithm breakage or a stock collapse.",
"top_option": "\u003e5"
},
"plan": {
"combination_logic": "weighted_average",
"domain": "health",
"n_sub_qs": 4,
"n_tools": 3,
"reasoning_approach": "Anchor on the current count returned by the exact query link, then apply an uncertain net growth rate for the ~9-12 months to Sept 1, 2026 (inflow of new LLM/chatbot trials minus trials leaving Recruiting status), and convert the resulting distribution into probabilities across the offered answer bands via the Monte Carlo threshold outputs.",
"sub_questions": [
{
"id": "sq1",
"question": "Is the current (late-2025/early-2026) count of Recruiting studies at the exact clinicaltrials.gov query link (intr=LLM-based Chatbot, status:rec) at or above 100?",
"rationale": "The present level of the same query is the single strongest anchor for the September 2026 value; the site\u0027s fuzzy intervention matching means the level could be far higher or lower than a naive \u0027LLM chatbot trials\u0027 count.",
"weight": 0.35
},
{
"id": "sq2",
"question": "Has the number of registered LLM/chatbot-intervention clinical trials been growing at more than ~40% year-over-year over the past 12-24 months?",
"rationale": "Growth rate of new AI/LLM trial registrations determines how far above today\u0027s level the Sept 2026 snapshot will land.",
"weight": 0.3
},
{
"id": "sq3",
"question": "Will the flow of newly-recruiting LLM/chatbot trials exceed the outflow of trials moving from Recruiting to Active/Completed between now and Sept 1, 2026 (i.e., net increase in the Recruiting stock)?",
"rationale": "The metric is a stock of \u0027Recruiting\u0027 status, not cumulative registrations; typical recruitment durations of 6-24 months mean churn partly offsets inflow.",
"weight": 0.2
},
{
"id": "sq4",
"question": "Will the Sept 1, 2026 count land in the middle of the offered answer bands rather than at an extreme (lowest or highest) option?",
"rationale": "Metaculus MC bands are usually centered near the current value plus modest growth; probability mass tends to concentrate in interior bands unless a structural change occurs.",
"weight": 0.15
}
],
"tool_requests": [
{
"parameters": {
"queries": [
"clinicaltrials.gov \"LLM-based Chatbot\" intervention recruiting number of studies",
"clinicaltrials.gov search results count large language model chatbot trials 2026",
"number of clinical trials registered large language model chatbot intervention"
]
},
"target_sub_questions": [
"sq1",
"sq2"
],
"tool_name": "web_search"
},
{
"parameters": {
"brief": "Find the current number of studies listed at https://clinicaltrials.gov/search?viewType=Card\u0026intr=LLM-based%20Chatbot\u0026aggFilters=status:rec (Recruiting studies with intervention \u0027LLM-based Chatbot\u0027), and any data on the growth trend of registered clinical trials involving large language models or AI chatbots on ClinicalTrials.gov over 2023-2026 (counts per year, bibliometric analyses, review papers). Also note typical recruiting-status durations for such digital health trials.",
"max_searches": 5,
"question_title": "How many clinicaltrials.gov studies with an LLM-based Chatbot as the intervention will be recruiting on September 1, 2026?"
},
"target_sub_questions": [
"sq1",
"sq2",
"sq3"
],
"tool_name": "claude_news"
},
{
"parameters": {
"code": "import numpy as np\n\n# Monte Carlo: project Recruiting-stock count on 2026-09-01 from an anchor level today.\n# Anchor uncertainty (current count of the exact query) - to be updated with search findings.\nrng = np.random.default_rng(0)\nN = 200000\nanchor = np.exp(rng.normal(np.log(140), 0.55, N)) # current recruiting count, wide uncertainty\nmonths = 9.0 # approx months to 2026-09-01 from Dec 2025\n# annual growth in the recruiting stock (net of churn): centered ~35%, wide\ng_ann = rng.normal(0.35, 0.30, N)\nfuture = anchor * (1 + np.clip(g_ann, -0.5, 2.0))**(months/12)\nqs = [1,5,10,25,50,75,90,95,99]\nprint(\u0027anchor quantiles\u0027, np.percentile(anchor, qs).round(0))\nprint(\u0027future quantiles\u0027, np.percentile(future, qs).round(0))\nfor t in [50,75,100,125,150,200,250,300,400,500]:\n print(f\u0027P(count \u003e= {t}) = {(future\u003e=t).mean():.3f}\u0027)\n",
"description": "Monte Carlo projection of the Recruiting-status study count to 2026-09-01 from an uncertain current anchor and uncertain growth rate, producing threshold exceedance probabilities for the multiple-choice bands."
},
"target_sub_questions": [
"sq1",
"sq3",
"sq4"
],
"tool_name": "code_execution"
}
]
},
"question": {
"close_time": "2026-08-06T18:00:00Z",
"description": "## Description\nIEEE Spectrum: [Can AI Chatbots Reason Like Doctors?](https://spectrum.ieee.org/ai-clinical-decision-support)\n\n`{\"format\": \"bot_tournament_question\", \"info\": {\"hash_id\": \"489ac96026b1d737\", \"sheet_id\": \"142\"}}`\n\n## Resolution Criteria\nThis question resolves as the number of studies with an Intervention/treatment of *LLM-based Chatbot* that have a study status of Recruiting are listed on September 1, 2026 at this link: https://clinicaltrials.gov/search?viewType=Card\u0026intr=LLM-based%20Chatbot\u0026aggFilters=status:rec",
"options": [
"0 or 1",
"2 or 3",
"4",
"5",
"\u003e5"
],
"question_type": "multiple_choice",
"title": "How many clinicaltrials.gov studies with an LLM-based Chatbot as the intervention/treatment will be recruiting on September 1, 2026 ?"
},
"research": {
"cross_market_brief": "## Cross-Market Signals\n\n### Polymarket\n- \"Will there be no change in Fed interest rates after the September 2026 meeting?\" \u2192 Yes: 0.51, Volume: $4.4M\n- \"Will the Fed increase interest rates by 25 bps after the September 2026 meeting?\" \u2192 Yes: 0.47, Volume: $3.6M\n- \"Will the Fed increase interest rates by 50+ bps after the September 2026 meeting?\" \u2192 Yes: 0.01, Volume: $1.8M\n- \"Will the Fed decrease interest rates by 25 bps after the September 2026 meeting?\" \u2192 Yes: 0.02, Volume: $4.2M\n- \"Will the Fed decrease interest rates by 50+ bps after the September 2026 meeting?\" \u2192 Yes: 0.01, Volume: $2.0M",
"errors": [],
"has_cross_market": true,
"n_errors": 0,
"n_tools": 3,
"tools": [
{
"elapsed_s": 40.42,
"error": null,
"success": true,
"summary": "0 results",
"tool_name": "web_search"
},
{
"elapsed_s": 34.3,
"error": null,
"success": true,
"summary": "8 citations",
"tool_name": "claude_news"
},
{
"elapsed_s": 0.26,
"error": null,
"success": true,
"summary": "Monte Carlo projection of the Recruiting-status study count ",
"tool_name": "code_execution"
}
]
},
"synthesis": {
"error": null,
"evidence": [
{
"claim": "No search result returned a live ClinicalTrials.gov count for the exact query (intr=LLM-based Chatbot, status:rec), leaving the current anchor unmeasured.",
"credibility": 45,
"direction": "NEUTRAL",
"priced_in": false,
"recency": "VERY_RECENT",
"source": "web_search",
"strength": "WEAK",
"sub_question_id": "sq1"
},
{
"claim": "A March 2026 JMIR Research Protocols methodological review states no consolidated public count of LLM-chatbot intervention trials yet exists, motivating a new systematic catalog.",
"credibility": 75,
"direction": "NEUTRAL",
"priced_in": false,
"recency": "RECENT",
"source": "web_search",
"strength": "MODERATE",
"sub_question_id": "sq1"
},
{
"claim": "Monte Carlo with an uncertain anchor (median ~140, IQR ~69-283) projects a Sept 1, 2026 median count of ~172 with P(\u003e=100)=0.83.",
"credibility": 35,
"direction": "NEUTRAL",
"priced_in": false,
"recency": "VERY_RECENT",
"source": "code_execution",
"strength": "WEAK",
"sub_question_id": "sq1"
},
{
"claim": "Systematic review of 160 chatbot studies (2020-2024) found LLM-based chatbots surged to 45% of new studies in 2024 after rule-based systems dominated through 2023.",
"credibility": 78,
"direction": "UP",
"priced_in": true,
"recency": "DATED",
"source": "web_search",
"strength": "MODERATE",
"sub_question_id": "sq2"
},
{
"claim": "Same review reports only 16% of LLM chatbot studies underwent clinical efficacy testing, with 77% still in early validation stages.",
"credibility": 78,
"direction": "DOWN",
"priced_in": true,
"recency": "DATED",
"source": "web_search",
"strength": "MODERATE",
"sub_question_id": "sq2"
},
{
"claim": "A March 2026 review protocol describes LLM-based chatbots as \u0027rapidly being repurposed as patient-facing digital health tools,\u0027 indicating continued field expansion.",
"credibility": 75,
"direction": "UP",
"priced_in": false,
"recency": "RECENT",
"source": "web_search",
"strength": "MODERATE",
"sub_question_id": "sq2"
},
{
"claim": "A JAMA Network Open RCT published June 2026 used an LLM chatbot intervention registered as NCT07132125, showing ongoing registration of such trials into 2026.",
"credibility": 80,
"direction": "UP",
"priced_in": false,
"recency": "RECENT",
"source": "web_search",
"strength": "MODERATE",
"sub_question_id": "sq3"
},
{
"claim": "A newly registered LLM-chatbot trial appeared on an ICH GCP registry mirror roughly one week before the search date, indicating continued inflow.",
"credibility": 50,
"direction": "UP",
"priced_in": false,
"recency": "VERY_RECENT",
"source": "web_search",
"strength": "WEAK",
"sub_question_id": "sq3"
},
{
"claim": "Model assumes net positive growth in the Recruiting stock over ~9-12 months, yielding a projected median increase of roughly 20-25% from anchor to Sept 2026.",
"credibility": 30,
"direction": "UP",
"priced_in": false,
"recency": "VERY_RECENT",
"source": "code_execution",
"strength": "WEAK",
"sub_question_id": "sq3"
},
{
"claim": "Simulated distribution places substantial mass across bands: P(\u003e=150)=0.59, P(\u003e=200)=0.40, P(\u003e=300)=0.17, P(\u003e=500)=0.03.",
"credibility": 32,
"direction": "NEUTRAL",
"priced_in": false,
"recency": "VERY_RECENT",
"source": "code_execution",
"strength": "WEAK",
"sub_question_id": "sq4"
}
],
"information_gaps": [
"No live/actual count from the exact ClinicalTrials.gov query link (critical anchor)",
"No historical time series of the Recruiting count for this query (base rate for growth)",
"Unknown how ClinicalTrials.gov keyword matching expands \u0027LLM-based Chatbot\u0027 (recall breadth drives count magnitude)",
"No data on typical recruitment duration for these trials (outflow rate from Recruiting)"
],
"key_uncertainties": [
"Current anchor could plausibly be anywhere from ~40 to ~300",
"Possible changes to ClinicalTrials.gov search/synonym algorithm altering counts",
"Whether inflow of new LLM-chatbot trials keeps accelerating or plateaus",
"Answer-band structure and where the anchor sits relative to bands"
],
"n_evidence": 10
},
"timings": {
"forecast": 75.55,
"plan": 26.92,
"research": 40.42,
"synthesis": 18.29
}
}