Pre-scan every clinical study report, regulatory submission, manuscript, abstract, patient-education sheet, and hospital communication before it reaches a journal editor, a regulator, or a patient. Sentence-level highlights show exactly which lines react AI on methods sections, ICH E3 modules, scientific abstracts, and patient-facing copy. Calibrated for the formal register and CONSORT or STROBE structure of medical prose. PHI must never be submitted to TextSight; de-identify drafts first. Adjunct to clinical and regulatory judgment, never a substitute for it. No training on submitted drafts. Free to try. No card.
Practising clinicians publishing case reports and manuscripts, medical writers in pharma and CROs drafting clinical study reports and regulatory submissions, medical-affairs teams producing MSL communications and slide decks, and hospital communications writers shipping patient education and service-line marketing all face the same question by 2026: did this document pass through an AI tool, and can the named author and the sponsor defend what was submitted under their signature. The pre-submission scan is the cheapest answer.
The healthcare writing stack runs from the patient education leaflet to the integrated summary in an NDA. Pre-scanning fits every stage because journal editors, regulators, peer reviewers, and sophisticated patients now read with the AI question in mind, and FDA and EMA have signalled that AI-drafted regulatory content sits under sharper review than a pure-human draft of the same module.
Hospitalists, attendings, residents, and academic faculty publishing case reports, retrospective studies, and review articles carry the broadest publication mix. AI assistance has crept into the abstract pass, the discussion section, and the response-to-reviewer letter during 2024 and 2025. Pro at $19.99 a month, or $14.99 a month on yearly, unlocks 10,000 character pastes and unlimited scans for the iteration weeks before a journal deadline or a society-meeting abstract cutoff.
Medical writers drafting clinical study reports, integrated summaries, IND and CTA modules, NDA and BLA sections, briefing documents, and investigator brochures operate under ICH E3 and agency expectations. AI as a first-draft tool on long-form regulatory content is now common across the industry, and CROs increasingly run shared AI-assisted templates across sponsors. The pre-submission scan catches AI-shaped phrasing in methods sections, integrated summaries, and discussion modules before the regulatory lead reviews the draft, and before the sponsor signs the cover letter.
Medical-affairs functions produce HCP-facing slide decks, MSL communication, advisory-board summaries, congress posters, and scientific publications. Promotional Review Committee and Medical Legal Regulatory review (PRC and MLR) sit downstream and they ask AI-assistance questions on the record. Pre-scanning every draft before MLR submission shortens the review cycle and reduces revision rounds. Business tier with 5 seats fits a typical medical-affairs pod across associate director, MSLs, publications lead, and external freelance writers.
Hospital systems, academic medical centres, telehealth companies, and outpatient clinics ship patient education sheets, condition pages, service-line marketing, and provider bios at high volume. None of that copy carries PHI when written generically, but it carries the brand trust signal that patients read before choosing care. Pre-scanning outgoing patient-facing copy is the lowest-cost trust-insurance available for a healthcare brand.
TextSight is a general-purpose writing analytics tool and is not offered as a HIPAA business associate by default. The compliance design assumes you de-identify any text that originated from a covered entity workflow before submission, so the data leaving your environment does not contain protected health information. This page is operational guidance, not a HIPAA compliance opinion; confirm with your privacy office before any production use.
Protected health information includes the eighteen Safe Harbor identifiers under 45 CFR 164.514: names, geographic subdivisions smaller than a state, dates more specific than year, telephone and fax numbers, email addresses, social security numbers, medical record numbers, health-plan beneficiary numbers, account numbers, certificate or licence numbers, vehicle identifiers, device identifiers, URLs, IP addresses, biometric identifiers, full-face photographic images, and any other unique identifying characteristic. None of that belongs in a paste to TextSight or to any other external scanning tool, regardless of vendor claims.
De-identify drafts before pasting: replace patient names with placeholders, scrub dates to year-only where the year is even needed for the genre, remove medical record numbers and account numbers, generalise locations to country or region, and rewrite any narrative detail that could re-identify a patient even after the obvious identifiers are gone. The detector reads prose cadence, not patient identity, so de-identification does not affect the score. The same de-identified draft scores within a point or two of the original on the genres we tested.
If you operate inside a covered entity, the obligation under the HIPAA Privacy Rule to handle PHI according to your notice of privacy practices and your minimum-necessary policy travels with you regardless of which tool you reach for. TextSight is not a business associate by default, we do not sign a BAA as part of standard onboarding, and we do not make HIPAA compliance claims about the service itself. Hospitals, payers, and life-sciences organisations should treat that boundary as load-bearing and align tool selection with their privacy officer and vendor-risk function before any production use.
Patient-education content written generically, condition pages, service-line copy, and provider bios drafted before a specific patient is identified usually do not contain PHI and are well within scope for pre-publication scanning. The pre-submission scan on patient-facing marketing copy is one of the cleanest use cases for the detector in healthcare, because the brand and trust signal matters and the privacy footprint is small.
Pro at $19.99 a month standard, $14.99 a month on yearly billing, fits solo clinicians and freelance medical writers. Business at $39.99 a month, or $29.99 a month on yearly, fits medical-affairs functions, biotech and pharma writing teams, CROs, and hospital communications with 5 seats. Group discounts available on Business. Full details on the pricing page.
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Most medical-affairs and biotech teams see Pro or Business pay for itself the first time a flagged section gets caught internally rather than in MLR or in a journal revision. Group discounts on Business. View full pricing →
Every healthcare document genre carries a distinct AI risk profile and a distinct calibration target. The headline score on a patient-education leaflet is not the headline score on an NDA module or a Nature submission, and the editing posture differs accordingly. Read the sentence map by section type, not the single percentage across the document.
Clinical study reports follow ICH E3 structure across synopsis, methods, statistical analysis, results, and discussion. Integrated summaries of safety and efficacy in an NDA or BLA module aggregate across studies. Both genres carry heavy boilerplate around protocol descriptions, statistical methodology, and adverse-event narratives that scores low on detectors regardless of who wrote it. The signal worth chasing concentrates in study-specific results interpretation, discussion paragraphs, and any bespoke narrative around protocol deviations or post-hoc analyses.
Investigational New Drug applications, Clinical Trial Applications, New Drug Applications, and Biologics License Applications are the highest-stakes regulatory genres. FDA and EMA have both flagged AI-drafted content as a topic for reviewer attention. Pre-scan every module before the regulatory lead reads it, aim above 75 on study-specific narrative prose, and remember the sentence map is the diagnostic, not the headline score across a 200-page module.
Manuscripts for Nature, NEJM, The Lancet, JAMA, BMJ, and society-journal publications now travel through AI screening at the submission stage. Abstracts for ASCO, AHA, ESC, ASH, and similar congress meetings sit under the same review. The discussion section is the highest-risk passage because it is the genre where AI-flavored hedging cadence and tidy parallel structure show up most readily. Pre-scan every section before submission.
Patient-education leaflets, condition pages on hospital websites, and pre-procedure instructions need to be accurate, accessible, and warm. The voice gap between an HCP-facing methods section and a patient-facing leaflet is wide by design. A patient-education sheet that reads as AI-generated undermines the warmth the genre demands. Pre-scanning catches that drift before the document reaches a patient.
Slide-deck speaker notes, MSL talking points, advisory-board pre-reads, and congress poster narratives are common AI tasks because the input volume is large and the output format is consistent. Risk concentrates in the safety paragraphs, the data-on-file references, and any off-label discussion bracket. Pre-scan before PRC and MLR review; the cycle shortens by one or two rounds when the AI residue is removed upstream.
Personal statements for residency applications through ERAS, fellowship applications, and clinical training programmes share a recognisable arc: clinical anecdote, motivation, fit with the specialty, future plans. The conventional structure can pull a fully human essay into the 50 to 70 band. The fix is sentence-level review, not abandoning the structure. Read the highlighted sentences and rewrite the ones that drift into AI-flavored phrasing while keeping the anecdote concrete and the voice specific.
Most healthcare documents clear three checks before they reach a regulator, a journal, or a patient: the drafter's own pass, the medical or regulatory reviewer pass, and the formal MLR or peer-review step. The pre-submission scan fits before the first internal review, so the version your medical reviewer reads is already AI-clean.
Write in your usual editor: Word, Docs, or your sponsor's authoring system such as Veeva Vault, MasterControl, or a CRO authoring tool. Using AI for an outline pass, a methods-section first draft, or a discussion paragraph is the realistic 2026 default under most sponsor policies. Before scanning, de-identify any patient narrative: remove the eighteen HIPAA Safe Harbor identifiers, replace names with placeholders, and scrub dates beyond year-level granularity.
Before the AI scan, verify every drug dose, contraindication, guideline citation, statistical claim, and trial result against current primary literature, approved labelling, and the relevant ICH, NCCN, or society guidelines. Detection does not verify medical accuracy; that check sits on its own track and is the non-negotiable step before any draft reaches a journal, a regulator, or a patient.
Open app.textsight.ai, paste each section, and scan. Free tier handles 5,000 characters in one paste. Pro handles 10,000, which fits a typical methods paragraph, an abstract, a discussion section, or a patient-education sheet. For a long CSR or NDA module, split by ICH E3 section: synopsis, methods, statistical analysis, results, discussion. The scan returns in well under a minute with an Authenticity Score and a sentence-by-sentence colour map.
Rewrite flagged sentences in the voice the genre demands while preserving clinical precision, defined terms, and citation accuracy. Methods sections stay precise and protocol-anchored. Patient education stays warm and accessible. A manuscript discussion typically needs three to eight sentence rewrites to move from a 65 score into the 85 range. The sentence map tells you which lines need attention; the headline score confirms the rewrite landed.
Send the consolidated draft to the medical reviewer, the regulatory lead, the publications lead, or the MLR committee for the substantive review. Pro history keeps every section scan for 90 days. PDF export gives you a contemporaneous record for the regulatory file or the journal submission. A typical methods section round-trips in about fifteen minutes; a full discussion section in about ninety. Disclose AI assistance in the methods or acknowledgements section per the target journal's author guidelines; the scan itself is internal hygiene, not a disclosure document.
ICMJE-aligned journals now require authors to disclose generative AI use in manuscript preparation and confirm that AI is not listed as an author. FDA draft guidance on AI in regulated submissions and the EMA reflection paper on large language models push sponsors toward transparency on AI-drafted content. Pre-scanning every submission and disclosing honestly is the defensible 2026 posture for any medical writer.
The International Committee of Medical Journal Editors and aligned journals including Nature, NEJM, The Lancet, JAMA, BMJ, Annals of Internal Medicine, and most society publications now require authors to disclose generative AI use in manuscript preparation, typically in the methods or acknowledgements section, and to confirm that AI is not credited as an author. Policies vary in detail: some prohibit AI for specific tasks such as image generation or peer review, some restrict AI to narrow workflow steps, some require a tool-by-tool listing. Read the target journal's current author guidelines before submission.
FDA has issued draft guidance on the use of AI in regulated submissions, including the broad expectation that sponsors document how AI was used during drafting and review and that reviewer attention may concentrate on AI-drafted modules. The substantive review focus remains the underlying science: protocol design, statistical analysis, safety signals, and benefit-risk. Pre-scanning gives the regulatory writer a contemporaneous signal of which sections carry AI residue before the submission package locks.
The European Medicines Agency reflection paper on large language models in regulatory science signals the same direction of travel: transparency on AI use, expectation of human authorship and verification, and reviewer attention on AI-drafted content. Sponsors operating across FDA, EMA, and other national regulators face a converging expectation that AI assistance is disclosed and that the human writer owns the final text.
Promotional Review Committee and Medical Legal Regulatory review are the internal gates inside pharma and biotech that catch off-label claims, fair-balance issues, and reference quality. AI-drafted promotional content adds a new question to the MLR docket: did the AI introduce phrasing that hedges away from the approved indication, or inflate a claim beyond the data on file. Pre-scanning before MLR shortens the cycle by giving reviewers a draft that reads in a single human voice.
Medical writing carries a wider audience range than most professional writing. The same drug story is told one way to a regulator, another to an HCP at a congress, a third to a patient at discharge, and a fourth to an investor on an earnings call. The voice shifts; the accuracy does not. AI-shaped prose flattens the voice range and that flattening is what the detector catches.
Slide-deck speaker notes, MSL talking points, scientific abstracts, and methods sections live in a register that prizes precision, appropriate clinical hedging, and citation anchoring. A discussion section that reads AI-flat tells the reviewer the writer did not engage with the data. The sentence-level highlights surface the lines that drift toward generic hedging cadence so you can rewrite them with the specific clinical detail the genre demands.
Patient-education leaflets, condition pages, and pre-procedure instructions need readability around grade six to grade eight on standard readability indices, warmth that signals the institution cares, and concrete examples that ground abstract clinical concepts. AI-shaped prose strips the warmth and produces tidy parallel structures that read as polite but distant. Pre-scanning catches that drift before patients read it.
Regulatory submissions live in a register that prizes structure, defensible language, and an auditable trail back to source documents. ICH E3 boilerplate dominates the structural skeleton; the writer's voice surfaces in the discussion paragraphs, the protocol-deviation narratives, and the safety integrated summaries. The pre-submission scan helps the regulatory writer ensure the bespoke discussion content does not slip into AI-flavored phrasing while the boilerplate carries its expected low-novelty score.
Biotech and pharma investor communications around clinical trial results, label updates, and pipeline milestones carry a distinct voice: confident on the data the company stands behind, properly qualified on forward-looking statements, and aware of the regulator and journal context. AI assistance is now common at the first-draft stage for these communications. The pre-submission scan catches AI-shaped phrasing in the qualified-statement paragraphs where wording precision matters most.
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