Inside the tool
A real BuzzView analysis in this category
Climedo’s AI visibility across ChatGPT, Google AI Overviews & Perplexity — one of the brands tracked in this category, straight from the live tool.
We sent 16 real buying questions to ChatGPT and Google AI Overviews – the questions businesses in this category actually ask. Then we counted which providers get named, and how often. The result is a ranking built from real AI answers, not opinion.
Veeva (6.3%) and Medistar (5.7%) dominate the answers – the remaining 68 named providers split what's left. If you're not here, you simply don't exist to AI users. That's exactly the gap BuzzView makes visible.
Share of all brand mentions across 16 prompts (Share of Voice). The longer the bar, the more often AI names the provider – across every question tested.
Behind the ranking — what the numbers actually mean for brands in this space.
Across 16 real buying prompts, AI systems collectively named 68 distinct health-tech providers — a number that reveals just how fragmented this market looks through the lens of generative search. The six most-cited brands (Veeva, Medistar, Oracle, Medidata, tomedo, and T2med) together accumulated 49 of the 174 total mentions, which translates to roughly 28% of all AI-generated brand references. The remaining 56 providers split 92 mentions among themselves, confirming that no single platform has managed to establish clear AI dominance across the full breadth of health-tech buying scenarios.
What makes this fragmentation particularly pronounced is the absence of a clear outlier at the top. Veeva leads with 11 mentions and a 6.3% share of voice — a meaningful edge, but not the kind of commanding presence that, say, Salesforce commands in CRM or AWS commands in cloud infrastructure. Health-tech is a category where AI models draw on a very wide pool of credible sources, none of which consistently point to one dominant solution above all others. This stands in sharp contrast to categories where two or three brands capture 60–70% of AI mentions between them.
A key reason for this diffuse distribution is the structural split within health-tech itself. The category simultaneously covers pharma and life-sciences software (Veeva, Medidata, Oracle Health Sciences, Climedo) and practice management software for medical practices (tomedo, T2med, TurboMed, Medistar, Doctorly). These two sub-markets have different buying criteria, different reference communities, and different content ecosystems. When AI is asked about health-tech, it draws from both pools — which mechanically broadens the spread of mentions and prevents concentration at the top.
For brands operating in either sub-segment, the implication is significant. Because no competitor has locked up a large share of AI visibility, the window to claim a strong and defensible position is still open. Brands that invest systematically in the right kind of AI-readable content — structured, authoritative, frequently cited by third parties — can move from the long tail of the 56 unnamed providers into the visible top tier. In mature AI-search categories, that window closes quickly once a few brands pull away from the pack.
Health-tech is one of the most fragmented AI-search categories measured — 68 providers, no dominant voice above 6.3% SoV. That fragmentation is an opportunity: first-mover investment in AI visibility now can yield a structurally advantaged position before the field consolidates.
Among the four tracked firms — Climedo, Temedica, Doctorly, and samedi — Climedo achieved the highest visibility score at 75.0%, meaning AI systems mentioned it across three quarters of the prompts where it was a relevant answer. At the other end, samedi scored 37.5%, appearing in only about one in three applicable prompt responses. Temedica and Doctorly both sit at 50.0%, clustering in the middle. A 37.5-point gap between the top and bottom tracked firm is substantial and is not explained by product quality or market share alone.
The primary driver of Climedo's visibility advantage appears to be its presence in clinical data management — a sub-niche with a rich ecosystem of regulatory guidance, academic papers, and industry analyst commentary. Clinical data management software is a topic that generates structured, citable content: FDA guidance documents reference data management standards, clinical research organisations publish comparison guides, and academic journals cover electronic data capture methodologies. Each of these is a source AI systems rely on. A brand embedded in that content ecosystem earns mentions almost automatically.
Samedi's lower score reflects a different content landscape. Online appointment scheduling and practice communication tools are discussed primarily in general practitioner forums, healthcare IT news outlets, and user review platforms — sources that AI models weight less heavily than peer-reviewed or regulatory content. This does not mean samedi is absent from AI recommendations, but it means the brand is competing in a part of the AI-knowledge graph that is thinner and less authoritative by default. Building visibility here requires a deliberate effort to seed content into higher-authority channels.
The actionable insight is that visibility in health-tech is not evenly distributed across sub-categories. Brands competing in clinically adjacent spaces — where regulatory bodies, research institutions, and professional associations create content — have a structural advantage in AI systems that prioritise authoritative sources. Brands in consumer-facing or practice-administration segments must compensate by investing in third-party validation: case studies, independent reviews, integration directories, and professional association endorsements that create the kind of external signal AI models use as visibility proxies.
Sub-category positioning determines visibility ceiling. Clinically adjacent brands like Climedo benefit from a richer authoritative-content ecosystem; practice management brands like samedi must actively create the third-party signal density that AI systems use to validate relevance.
Best-of prompts — questions like "Was sind die besten Clinical Data Management Tools für Pharma & Biotech in Deutschland?" — tend to surface the internationally recognised platforms: Veeva, Medidata, and Oracle Health Sciences. These brands have the deepest content footprint in English-language and German-language analyst reports, making them the default anchors in list-style AI answers. A brand that does not appear in best-of responses is effectively invisible to buyers who begin their research with a broad category query, which is where most buying journeys start.
Comparison prompts work differently. When a buyer asks "Vergleiche Climedo, Medidata und Oracle Health Sciences im Bereich Klinische Studiendaten digital erfassen," AI systems are forced to evaluate named brands side by side. This prompt type rewards brands that have detailed, structured content covering specific features, compliance certifications, and integration capabilities — because AI needs that granularity to generate a meaningful comparison. Climedo, despite its smaller market footprint than Oracle or Medidata, performs well here precisely because its own documentation and third-party reviews provide the kind of specific, feature-level detail that comparison prompts require.
Alternative-seeking prompts ("Welche Alternativen gibt es zu Medidata?") are where smaller and more niche providers break through. Climedo and REDCap both appear in alternative-seeking contexts, pulled in by AI systems that recognise a buyer looking for a less enterprise-heavy or more cost-accessible solution. For brands like Temedica or samedi that do not yet appear in best-of lists, alternative-seeking prompts are the most realistic near-term entry point — and optimising for them requires creating content that explicitly positions the brand relative to established category leaders.
Use-case and vertical prompts ("Was sind die besten Praxissoftware Tools für Arztpraxen?") reveal the sharpest segmentation. These prompts produce an almost entirely separate provider set: tomedo, T2med, TurboMed, Medistar, and Doctorly — all practice management platforms with no meaningful overlap with the clinical data management tools that dominate best-of lists. This prompt-type segmentation means that content strategies must be engineered for the specific prompt architecture a brand's target buyers actually use, not for the category as a whole. A clinical data management brand optimising for practice management prompts is wasting resources, and vice versa.
Prompt architecture determines which brands win. Health-tech brands must map their content strategy to the specific prompt types their buyers use — best-of, comparison, alternative, or use-case — because each surfaces a substantially different provider set.
Sentiment in AI responses is not random — it reflects the tone of the underlying sources that AI systems have ingested. tomedo stands out as the most positively mentioned brand in the dataset: 4 of its 7 AI mentions carry a positive framing, a 57% positive rate that no other provider in this study comes close to matching. This tracks with tomedo's reputation in German general practice communities, where physician forums and professional association reviews tend to be enthusiastic about its usability and Apple ecosystem integration. When a significant share of the sources AI draws from are positive, the AI's own output reflects that positivity.
Medistar's sentiment profile is the starkest contrast: 0 positive mentions and 3 negative mentions across 10 total references. That means 30% of Medistar's AI appearances carry a critical framing — a signal that AI systems are picking up on recurring concerns in the sources they reference. In the context of German practice management software, Medistar's negative mentions likely reflect documented criticisms around legacy interface design, support responsiveness, and transition friction for practices migrating from older systems. Negative AI sentiment is not a death sentence, but it does mean that AI systems are actively reinforcing buyer hesitation rather than building purchase confidence.
The broader sentiment picture reveals that most health-tech providers live primarily in neutral territory. Veeva collects 9 neutral and 2 positive mentions out of 11; Oracle and Medidata each show 5 neutral and 2 positive out of 7. Neutral mentions are not a failure — they indicate that AI considers a brand relevant enough to recommend — but they represent a missed opportunity. Neutral AI mentions do not build emotional momentum for a buying decision the way positive mentions do. For enterprise health-tech brands like Veeva and Oracle, where purchase cycles are long and multi-stakeholder, the delta between neutral and positive AI sentiment can meaningfully influence which vendor gets put on a shortlist.
The drivers of positive AI sentiment in health-tech are identifiable and actionable. Clinical outcomes data — documented improvements in trial efficiency, data quality rates, or practice productivity — generates the kind of specific, measurable claims that AI models use to frame positive endorsements. Third-party validation from medical associations, regulatory bodies, or peer-reviewed publications adds authoritative weight. Customer success stories that appear in multiple independent outlets create a consistent positive signal across different source types. Brands that want to shift from neutral to positive AI mentions should audit which of these signal types are underrepresented in their current content and external presence.
tomedo's 57% positive mention rate proves that AI sentiment is earnable — through physician community engagement, documented usability outcomes, and professional association presence. Medistar's 30% negative rate is a warning: unaddressed criticism in the source ecosystem becomes baked into AI recommendations.
The concentration and fragmentation metrics from this study paint a consistent picture: the health-tech category in AI search has not yet consolidated around a recognisable set of dominant players. In mature AI-search categories — where a handful of brands have built dense, authoritative, and consistently positive content ecosystems — the top 3 providers typically account for 50% or more of all AI mentions. In health-tech, the top 6 providers together reach only 28%. The category is in what AI search researchers call the formative phase: high fragmentation, shifting positions, and high sensitivity to new content investments.
The dual-segment structure of health-tech — clinical/pharma tools versus practice management — is a structural factor that will likely prevent full consolidation even as the category matures. These two sub-markets are so different in their buyer profiles, compliance requirements, and content ecosystems that they will continue to produce separate winner sets in AI responses. The implication is that "AI visibility in health-tech" is not a single race — it is two parallel races, and a brand's competitive set and content strategy should be calibrated to whichever race it is actually running.
The formative phase also means that the current rankings are more fluid than they would be in a mature category. A brand like samedi, sitting at 37.5% visibility today, could realistically reach 60–70% visibility within 12–18 months through a sustained, well-targeted content and PR programme. Equally, a brand that is today appearing in AI responses only incidentally — one of the 56 providers in the "other" bucket — could break into the visible top tier if it builds the right kind of external signal density. In mature categories, catching up to entrenched leaders requires years of effort; in formative categories, well-executed short campaigns can produce step-change improvements.
The window for formative-phase investment in health-tech AI visibility is open now, but it will not stay open indefinitely. AI search categories follow a consolidation trajectory: as AI systems accumulate more data, the brands with the deepest and most consistent signal become progressively harder to displace. The brands that invest in AI visibility during this formative phase — building structured content, earning third-party citations, managing sentiment across physician and industry communities — will be the ones whose names appear most reliably when buyers ask AI which health-tech solution to choose in 2027 and beyond.
Health-tech is in the formative phase of AI-search development: high fragmentation, no locked-in leaders, and high responsiveness to new content investment. Brands that act now — before the category consolidates — can claim a durable top-tier position at a fraction of the cost it will require in 24 months.
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No wishful thinking: the ranking comes from exactly these prompt types – best-of questions, comparisons, alternatives and use cases.
Visibility score = share of prompts where the provider appears in the AI answer at all. 100% means: present for every relevant question.
Climedo’s AI visibility across ChatGPT, Google AI Overviews & Perplexity — one of the brands tracked in this category, straight from the live tool.
We set the real search and buying questions of your industry – exactly how your customers actually ask AI.
Every prompt runs against all major AI models. We count mentions, position, sentiment and the cited sources.
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