結論(CONCLUSION)
本章試圖藉由探討術語、形態學、型態辨識,以及若干將有助於強化臨床病理相關性 (clinicopathologic correlation) 實務的技術,為皮膚科學的學習提供一份導論與基本的結構性架構。所期望的最終結果是更準確的診斷與更好的病人照護。
其他圖與表,「其餘健康者的急性皮膚疹 (Acute cutaneous eruptions in otherwise healthy individuals)」,可於我們的電子書中取得(存取碼請見封面內頁)。
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總結而言,機器學習與 AI 在未來幾乎必定會在臨床實務中引導與協助醫師。或許將不再需要以人工計算 PASI 這類分數,或是在黑色素細胞性病灶的診斷灰色地帶中將更容易縮小範圍、執行全人口的皮膚癌篩檢,以及進行半自動化的遠距醫療/分流。自動化的預測性分期系統或許會取代 TNM 分期系統。然而,何者將成為現實,取決於醫師與研究者、技術進展、病人、政治、公司、倫理考量與監管機構。此外,鑑於篇幅限制,還有許多其他應用與技術在此並未涵蓋或僅簡略提及。最後,機器學習領域步調快速,而對相關文獻的批判性回顧,在理解 AI 的新概念與新可能性上至關重要。