「ＡＩドクター」、患者の予後予測能力を実証　死亡リスク含め

【ワシントンＡＦＰ＝時事】米ニューヨーク大学グロスマン医学部のチームが、新たに開発した人工知能（ＡＩ）ツールを使って医師のメモを読み取り、死亡リスクや再入院など患者の予後について正確に予測できることを実証した。研究結果が７日、英科学誌ネイチャーに掲載された。（写真は資料写真）
　チームが開発したのは、大規模言語モデルの「ＮＹＵＴｒｏｎ」。ニューヨーク市内の同大系列の病院ですでに使用されている。
　ＡＩの学習は、２０１１年１月～２０年５月に系列病院で治療を受けた患者３８万７０００人分の記録を用いて行った。患者の経過記録から放射線検査報告書、退院指示書まで医師が書いたあらゆる記録に基づき、４１億語から成るデータベースが作成された。
　■人間の代替ではない
　これまでＮＹＵＴｒｏｎは、入院中に死亡した患者の９５％、３０日以内に再入院した患者の８０％について、現実と同じ結果を予測できた。
　また、患者の入院期間に関しては７９％、保険適用を拒否されたケースの８７％、原疾患に加え続発性疾患を併発した症例の８９％を正確に予想した。
　ＮＹＵＴｒｏｎの予測能力は多くの医師や、現在使用されているＡＩ以外のコンピューターモデルをしのいでいた。
　ただし、「ある著名なベテラン医師はＡＩを上回る超人的なパフォーマンスを発揮した」と、同大の神経外科医でコンピューター科学者でもある論文の主著者、エリック・オールマン氏はＡＦＰに語った。この点はチームにとって意外だったという。
　同氏は、ＡＩは決して治療に当たる医師に置き換わるものではなく、「より多くの情報に基づいた決定を医師が行うための診療現場におけるシームレスな情報提供」を支援するものだと述べた。【翻訳編集ＡＦＰＢＢＮｅｗｓ】
〔ＡＦＰ＝時事〕（2023/06/08-17:05）

Artificial intelligence has proven itself useful in reading medical imaging and even shown it can pass doctors' licensing exams.
Now, a new AI tool has demonstrated the ability to read physicians' notes and accurately anticipate patients' risk of death, readmission to hospital, and other outcomes important to their care.
Designed by a team at NYU Grossman School of Medicine, the software is currently in use at the university's affiliated hospitals throughout New York, with the hope that it will become a standard part of health care.
A study on its predictive value was published Wednesday in the journal Nature.
Lead author Eric Oermann, an NYU neurosurgeon and computer scientist, told AFP that while non-AI predictive models have been around in medicine for a long time, they were hardly used in practice because the data they needed requires cumbersome reorganization and formatting.
But one thing that's common in medicine everywhere, is physicians write notes about what they've seen in clinic, what they've discussed with patients, he said.
So our basic insight was, can we start with medical notes as our source of data, and then build predictive models on top of it?
The large language model, called NYUTron, was trained on millions of clinical notes from the health records of 387,000 people who received care within NYU Langone hospitals between January 2011 and May 2020.
These included any records written by doctors, such as patient progress notes, radiology reports and discharge instructions, resulting in a 4.1-billion-word corpus.
One of the key challenges for the software was interpreting the natural language that physicians write in, which varies greatly among individuals, including in the abbreviations they choose.
By looking back at records of what happened, researchers were able to calculate how often the software's predictions turned out to be accurate.
They also tested the tool in live environments, training it on the records from, for example, a hospital in Manhattan then seeing how it fared in a Brooklyn hospital, with different patient demographics.
- Not a substitute for humans -
Overall, NYUTron identified an unnerving 95 percent of people who died in hospital before they were discharged, and 80 percent of patients who would be readmitted within 30 days.
It outperformed most doctors on its predictions, as well as the non-AI computer models used today.
But, to the team's surprise, the most senior physician who's actually a very famous physician, he had superhuman performance, better than the model, said Oermann.
The sweet spot for technology and medicine isn't that it's going to always deliver necessarily superhuman results, but it's going to really bring up that baseline.
NYUTron also correctly estimated 79 percent of patients' actual length of stay, 87 percent of cases where patients were denied coverage by insurance, and 89 percent of cases where a patient's primary disease was accompanied by additional conditions.
AI will never be a substitute for the physician-patient relationship, said Oermann. Rather, they will help provide more information for physicians seamlessly at the point-of-care so they can make more informed decisions.