How AI Search Is Recommending Healthcare and Medical Devices: Monthly Trends

Executive Summary

Abbott FreeStyle remains the clear category leader in September 2026 with 59.1% valid recommendation coverage, ahead of second-place Ascensia CONTOUR at 43.0%, a gap of 16.1 points. The category was stable this month, with no brand exceeding normal month-to-month variation. Abbott FreeStyle's 2.5-point dip from August's 61.6% sits within normal movement and its lead remains substantial.

Prodigy Diabetes Care stands out as the strongest mover over the comparable period, rising from 0.9% valid recommendation coverage in July to 2.8% in September. This 1.9-point increase is the only movement in the category that exceeds normal month-to-month variation, marking a genuine shift from near-invisibility to a small but measurable presence. The brand rose from 4 valid recommendations in July to 11 in September.

LifeScan OneTouch was the sharpest decliner, moving from 10.9% valid recommendation coverage in July to 10.2% in September, down 0.7 points. This movement remains within normal month-to-month variation, as does the category's overall stability. The benchmark shows a category where the leader's advantage is concentrated and the trailing brands hold steady positions.

Each monthly run in July began with 657 prompt-surface observations (469 unique questions); August began with 645 (486 unique questions); September began with 672 (524 unique questions). Of September's total, 672 mentioned a tracked brand or competitor; 665 were relevant and 7 were irrelevant. The public metrics use the 391 September observations that survive both qualification stages, compared with 432 in July and 393 in August.

Key Findings

Benchmark Context

The report separates the raw collection universe from the qualified analysis set. Brand-level recommendation percentages are calculated within the qualified benchmark set.

The metrics below summarize the two comparable months at the aggregate level, showing how the overall shape of the benchmark shifted even as individual brand rankings held steady.

Benchmark-Level Metrics

The qualified observation count declined over the comparable period, yet the proportion of answers offering recommendations rose. In August, the recommendation-shaped answer share peaked at 36.9% before settling at 35.8% in September, a pattern suggesting the category leans increasingly toward recommendation-style responses even as the qualified set contracts.

AI Recommendation Trend

A stable two-brand lead structure with one small-base riser

The healthcare and medical devices category shows a firmly entrenched leader and a clear second-place brand, with no change to the overall competitive structure in September. Only Prodigy Diabetes Care exceeded normal month-to-month variation across the comparable period, moving up from a very small base.

The category-level change over the comparable period reflects a combination of several smaller movements rather than a single dominant shift. Only Prodigy Diabetes Care exceeded normal month-to-month variation from July to September; the other five brands moved within expected ranges.

What Changed This Month

Abbott FreeStyle

Abbott FreeStyle remains the category leader with valid recommendation coverage of 59.1% in September, down 2.5 points from August's 61.6% but up 3.5 points from July's 55.6%. The brand had 231 valid recommendations out of 391 qualified observations in September, down from 242 in August. The month-to-month decline sits within normal variation.

Top-three placement dipped from 53.2% in August to 51.7% in September, and top-ten coverage eased from 56.7% to 54.7%. Rank-one rate held nearly steady at 9.7%, down a marginal 0.3 points from August. The brand's raw mention presence remains near-universal at 95.7%, meaning it appears in almost every qualified observation.

The distinction for Abbott FreeStyle is sustained leadership with a slight monthly pullback: it is both visible and recommended at the highest rate in the category, though the September dip suggests the advantage is not expanding further this month.

Highest-priority diagnostic: Which AI surfaces or prompt patterns account for the September pullback from August's peak coverage?

Prodigy Diabetes Care

Prodigy Diabetes Care was the only brand to show movement beyond normal month-to-month variation over the comparable period, rising from 0.9% valid recommendation coverage in July to 2.8% in September. The brand's valid recommendation count went from 4 in July to 11 in September, a small but concrete increase.

Top-three placement rose from 0.2% in July to 1.5% in September, while the brand's rank-one rate remained at zero in both months. Prodigy appeared in 13 of 391 observations in September, up from 8 of 432 in July. The largest single jump occurred from July to August, when coverage moved from 0.9% to 3.0%; September held near that level at 2.8%.

The distinction here is between a genuine foothold and a fully established presence: Prodigy Diabetes Care is now recommended in more answers, but from a very low base. These counts remain small, so the movement should be read as directional rather than definitive.

Highest-priority diagnostic: Which specific prompts and surfaces drove the initial jump, and can the brand hold or extend coverage in the next measurement period?

LifeScan OneTouch

LifeScan OneTouch moved from 10.9% valid recommendation coverage in July to 10.2% in September, with its valid recommendation count dropping from 47 to 40. The brand's presence fell from 92 observations in July to 70 in September, and its raw mention presence rate declined from 21.3% to 17.9%.

Top-three rate eased from 7.2% to 6.4%, while rank-one rate ticked up marginally from 0.2% to 0.3%. These counts represent a stable but softening position: the brand is mentioned in fewer answers and recommended slightly less often, all within normal month-to-month variation.

The distinction for LifeScan OneTouch is that its presence is declining faster than its recommended coverage, suggesting the gap between being seen and being recommended is narrowing as overall visibility fades.

Highest-priority diagnostic: Which prompt types or surfaces account for the decline in raw mentions, and is the drop concentrated in specific question categories?

Buyer-Intent Interpretation

All qualified observations across the three comparable months fell into the Brand Recommendation cluster, with 432 observations in July, 393 in August, and 391 in September assigned to this class. Neither the Pricing & Value nor the Multi-Brand Comparison clusters registered any observations in any month.

The public benchmark in September can answer which brands AI systems recommend most often, but it cannot yet answer questions about how AI systems frame pricing, value, or direct brand comparisons. The category runs on recommendation-style prompts only, so the commercial picture centers on answer presence and position rather than cost signals or head-to-head analysis.

Brand Opportunity Summary

The benchmark identifies where attention is warranted across the category; a company-level analysis is needed to explain why those patterns hold.

Evidence Behind the Benchmark

The aggregate metrics are built from prompt-level observations, including the query, the AI surface, the recommendation outcome, the rank position, sentiment, and citations where exposed. Company-level analysis can go deeper into prompt, competitor, surface, and evidence patterns to understand the drivers behind the headline coverage figures. Source presence is not automatically treated as proof of causation.

Report-Specific Interpretation Notes

• Small-count movement: Prodigy Diabetes Care and Trividia Health operate on very small absolute counts, so their changes should be read as directional rather than definitive.
• Qualified denominator vs raw collection: All brand percentages are calculated against the 391 qualified observations, not the 672 prompts collected.
• Directional analysis: Month-over-month movement identifies changes worth investigating; it does not by itself establish the cause of those changes.

About This Benchmark

This report is part of the CiteWorks Studio AI Industry Market Discovery research program.

• AI Industry Market Discovery
• AI Industry Market Discovery Methodology
• AI Industry Market Discovery Metrics
• AI Industry Market Discovery Standards

Next Step

The Public Benchmark Shows Where a Brand Is Winning or Losing. A Company-Level Audit Shows Why.

Beneath the aggregate percentages sit the questions that matter commercially: which high-intent prompts are won or lost, which competitor takes the recommendation when a brand loses, what attributes AI systems associate with each option, and which external sources shape those answers. The benchmark shows the outcome; the audit shows the mechanism.

A company-specific AI visibility audit maps those prompt, surface, competitor, ranking, sentiment, and evidence-source patterns into a prioritized visibility strategy, turning the headline coverage figures into actionable direction.

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