評価者のためのガイド
この市場では誰もが「最良」を名乗ります。しかし、ものさしを定義する者はほとんどいません。このページが定義します：臨床システムとデモを分ける5つの質問 — 病院も、支払者も、省庁も、私たちを含むあらゆるベンダーに投げかけられる、同じ5つです。
「最良」が空虚になる理由
ランキングは静的な試験問題で稼がれます — 整った症例文、唯一の正解、結果責任ゼロ。実運用の医療は中断され、曖昧で、責任を伴います。データつきの論証：WP-002 The Flawed Yardstick（英語）。
「平均95%正確」でも、大動脈解離ひとつで自信満々に誤るモデルは95%のシステムではありません — 法的リスクです。重要なのは自信を持って主張された虚偽の最悪ケース率 — この数字を出せるベンダーはほぼいません。
デモは質問に答えます。システムは責任を担います：EHRにサインインし、医師の署名の下で書き戻し、ネットワーク障害を生き延び、法廷で使える監査証跡を残します。
ものさし
Purchasing checklist
The market is flooded with transcription tools claiming to be clinical AI. Before integrating any system into your workflow, ask the vendor these four questions.
What happens when the microphone misses a critical word? Standard LLM-based scribes are probabilistic—they predict the most likely next word, meaning they will confidently invent a clinical finding rather than admit they missed it. DeepSensi is deterministic under uncertainty. The LIMBO protocol forces the system to declare 'I don't know' and flag the missing variable. Ask the vendor for their formally derived worst-case bound on confidently asserted falsehoods. If they answer with an 'average accuracy score', they do not have a safety bound.
Do you store patient data, and is privacy guaranteed by architecture or just by policy? A signed BAA is a legal shield for the vendor, not a technical safeguard for your patients. DeepSensi operates on a Zero-PHI architecture. Personal identifiers are stripped at the edge before any reasoning occurs. We do not need your patients' names to diagnose them. Ensure your vendor enforces privacy mathematically in their code, not just in their terms of service.
Does your system just take notes, or does it actively defend my license? An AI scribe that merely transcribes audio is a missed opportunity. DeepSensi’s ACS Scribe listens to the encounter and simultaneously runs a real-time, 37-specialist Differential Diagnosis (DDx) in the background. It doesn't just write the SOAP note; it highlights the one critical diagnosis you might be missing and calculates a SIL-4-grade confidence score, acting as an active clinical safety net.
If the AI makes an error, who takes the blame? Today, the answer is always the physician. DeepSensi alters this reality by providing a court-grade, cryptographically sealed evidentiary ledger for every single assertion the system makes. You can point to the exact node, rule, or piece of evidence that led to a conclusion. Ask your scribe vendor if their system provides a tamper-evident audit trail that you could take to court.
FAQ
ドクトリン：私たちは証明と標準と監査を公開します。設計図は公開しません。科学的文書は英語です。お問い合わせは日本語でどうぞ。