ヘルスケアにおけるAI。3大メリットと応用例

The applications of AI in ヘルスケア are numerous and diverse. 人工知能 そして 機械学習 are two of the biggest technology trends that the world is witnessing at this moment. Microsoft’s announcement of the ヘルスケア NExT initiative is a harbinger of that, and marks the tech giant’s entry into cancer research.

そのプレスイベントでは、Microsoft Healthcare NExt.のCorporate Vice Presidentが登場しました。 Peter Lee氏は次のように述べています。

"（ヘルスケアの問題に取り組むことは）より大きな挑戦です。しかし、テクノロジー、具体的にはクラウド、AIやコラボレーション、ビジネス最適化ツールなどが、ヘルスケアの変革の中心になると考えています。"

Another patron at the tech biggie, Chris Bishop, further explained how healthcare is different compared to other industries, and that defeating cancer is this era’s biggest dilemma. Hence, the head honchos at Microsoft feel that applying technologies like Machine Learning and AI in healthcare is imperative for a smarter healthcare transformation.

Reviews of the current healthcare systems in various countries point misdiagnoses and delayed treatments to be the most immediate concerns. Microsoft plans to utilize machine learning systems, cloud storage and business optimization tools to fix these fundamental problems at healthcare facilities. It also looks forward to advancing its role in cancer treatment research, using out-of-the-box approaches. Microsoft’s experts believe that cancer can be treated in a similar fashion as computer viruses or software glitches are removed.

これらの動きから、ヘルスケアにおけるAIが最大のイノベーションの場になると考えられます。最近、アメリカや中国で開催されたAIやMLの国際会議でも、デジタルヘルスケアの巨大な改革が迫っていることが強調されていました。

なぜヘルスケアにおけるAIが重要なのか

It’s no secret that medical research is the most critical area where the data generated is enormous and of the highest value. So, the need for highest efficient data handling systems isn’t surprising, considering not just patient safety and compliance norms but also for the efficient management of 臨床試験 and emergency cases. Hospitals, research organizations and healthcare aid societies are aware of the various ways in which AI can change the face of healthcare, inside organizations as well as outside. However, it is surprising to note is that only few healthcare agencies are openly integrating Machine Learning and AI into their systems.

The massive overhaul of healthcare systems that AI can bring in such a short span of time is commonly spoken about but not yet witnessed in reality. The computational power of AI is important for healthcare organizations to notice, for it’s the only field that is lagging behind. There is a need for healthcare professionals to openly discuss all the dimensions in which AI and ML can help reduce mishaps, such as increased accuracy in data entries without human intervention, monitor in-patient stats for critically ill patients, etc.

1.データが増えればパワーが増す

機械学習が顕著な変化をもたらすヘルスケア分野には、より迅速で正確な診断のための膨大な臨床検査記録の可視化や、病気の予後をよりよく理解するための患者データのパターンの研究などがあります。これにより、臨床試験の効果が向上し、医療従事者の時間を大幅に節約することができます。 株式会社マッキンゼー が最近報告されました。これは、より多くの臨床研究が活用され、より多くのデータ可視化ツールが開発され、より多くのデータクラウド管理ツールが必要になることを意味します。

This also indicates the need for better automation of clinical data handling systems, which will save a lot of expenses for pharma companies, hospitals, care centres and clinical research organizations. The reason for the improved accuracy and speed is that machine learning incrementally works better. The more clinical data that’s fed into the system, more accurate is the diagnosis. Once patient data handling systems are automated, machine learning systems can be incorporated and healthcare organizations will definitely have smoother processes.

2.突発的な健康被害の予測可能性の向上

人工知能 aids in understanding diseases better, analyzing patient-specific disease characteristics and gauging the course of treatment and its effectiveness. it is a powerful tool to monitor disease progression through set parameters. One of the surprising areas of machine learning research is studying heart diseases. Scientists are currently exploring the risk factors for degenerative heart diseases, including predictions of heart attacks using available machine learning tactics.

A group of scientists at the University of Nottingham in the United Kingdom are collaborating with cardiologists at Carnegie Mellon University to study AI algorithms for predicting the occurrence of heart attacks. Their sample data consist of patients with and without cardiologic medicine prescriptions. They are proposing new theories to indicate the risk factors for cardiac arrests outside the usual list of parameters, such as age and previous heart disease diagnosis. Such a groundbreaking study would be detrimental in the issuing of drugs to varying patient populations and also decide how drug dosages are monitored.

Data modelling methods with AI integration can also be applied to studying course of disease in case of infectious diseases, HIV-AIDS and cancer, among others. In fact, 精神科医も頼りにしている 精神疾患の診断と予後のためのAIシステムについて。AIは行動パターンを研究し、その結果を脳機能の報告、位置的なMRIプレート、細胞の老化パターンと相関させ、患者にどのような神経変性疾患が控えているかを判断するのに役立ちます。

3.AIがヘルスコミュニケーションの格差をなくす

The fact that Artificial Intelligence itself developed out of the vastness of Big Data is overwhelming and the way data of humans is expanding, AI and ML seem to be the obvious choice to fully use these data. AI engineers are more involved in creating better tools to visualize medical data now than ever before and the results are of most use in behavioural science. In fact, at a recent conference, という結論に達しました。 2018年には、30%以上の医師が、患者のデータにコグニティブ分析ツールを実行してから、患者ごとの医療記録と検査データを相関させるようになります。

AIは、医療システム内のデータの流れ方、医療従事者によるデータの活用方法を変え、がんの診断における重要なステップをスピードアップするツールであることは間違いありません。科学者のグループは、機械学習が最も強力なツールであると結論づけています。 癌の発生を予測する in humans whose CT and MRI data already show sizable lesions. The fatal disease monitoring protocols contain that early diagnosis is key; if the least amount of effort can be used to predict tumours early, machine learning can easily become the order of the day for aiding cancer diagnosis. Machine learning functions in concert with conventional diagnostic instruments can be utilized for better visualization of cancer progression and functioning of nuclear machinery. But the point where ML creates the effect is on the applicability of data without any time-lags. Healthcare systems need this efficiency, they need this exponential rise in user-friendliness and ease of communication and 人工知能 is by far, the most effective way to achieve that.

ヘルスケアの新しい始まり

Not too long ago, Artificial Intelligence was touted as the new horizon of technology and the zenith of information processing efficiency, but now AI is definitely much more than that. Since the emergence of a full-blown AI system in 2010 — IBM Watson to this year’s Healthcare NExT, AI’s significance has had a meteoric rise. The intelligence and effectiveness of this technology essentially mark evidence of the fact that AI in healthcare has a bright future ahead. Today, IBM Watson integrates genomics そして オンコロジー solutions in its interface that are applied to accelerate access to better healthcare by being the most powerful and efficient communication bridge. It helps patient access clinical knowledge and information more interactively. It has increased sensitivity to patient concerns, improved on understanding relevance and has reduced information processing speeds to a tenth of a millionth second.

Prevalent market research firms, like Frost and Sullivan, have predicted the high-speed expansion of AI systems in healthcare even for small and medium enterprises. Even Microsoft’s Lee recent statement seconds that, as Microsoft aims to “help each human and company experience the most groundbreaking AI solutions” to have a healthier future. Bernard Marr, the Forbes Contributor of Health, wrote “From 肝疾患 にしています。 がん そしてさらに 精神病と統合失調症, AI algorithms are changing the game in terms of disease diagnosis. ” Hence, it is not too far a time when we interact with bots to know the status of physician appointments at a clinic nearby and even medical students operate machine learning systems to complete small tasks inside the OT. So medical students will learn more about data sciences and engineers will code more for evolved machine learning systems, most importantly!

Well, we’re just getting started!

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に相談する必要があります。 AI それともヘルスケアの専門家ですか？博士号を取得した科学者と連絡を取ることができます。 コラブツリー.