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Технологии ИИ: нет ничего искусственного в заботе о здоровье

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Технологии проникают во все сферы нашей жизни. Здравоохранение — не исключение.

Текущая ситуация осложняется следующими факторами:

  • расходы на здравоохранение составляют до 10% [1] от мирового ВВП, с заметными различиями в расходах по странам;

  • к 2030 г. прогнозируется нехватка 10,2 млн работников [2] здравоохранения, дефицит по регионам распределяется неравномерно;

  • демографическое сжатие: замедление темпов роста населения, рост населения в возрасте 65 лет и старше опережает рост более молодых возрастных групп, население в среднем продолжает становиться старше [3].

Таким образом, решение задач снижения нагрузки на систему здравоохранения, облегчения эмоционального выгорания врачей, повышения качества жизни возрастного населения, сокращения бюджетных расходов на социальное обеспечение, повышения эффективности лечения, углубления знаний — является все более и более важным.

Эти и другие задачи в здравоохранении решаются множеством инструментов. В настоящей статье рассматривается потенциал влияния технологий ИИ на здравоохранения, что будет являться движущей силой следующего десятилетия.

Например, стартап лаборатории Retro Biosciences, в который инвестировал Сэм Альтман, ставит цель добавить минимум десятилетие качественной жизни за счет технологий, прорыв в которых должен обеспечить ИИ.

В табл. 1 представлена оценка глобального рынка ИИ в здравоохранении, CAGR (совокупный среднегодовой темп роста) составляет 27,3% на промежутке 2023–2032 гг.

Оценочно, объем мирового рынка ИИ в здравоохранении составит 28,3 млрд. долл. / 2024 г. Совокупная структура расходов состоит из программного обеспечения (40,5% мирового рынка или 11,5 млрд долл. в 2024 г.), оборудования (35,2% или 10,0 млрд долл.) и услуг / сервиса (24,3% или 6,8 млрд долл.).

Таблица 1. Прогноз глобального рынка ИИ в здравоохранении, млрд долл.

Примечание: расчеты с использованием [4 - 17]
Примечание: расчеты с использованием [4 — 17]

Мировые аналитические компании оценивают объем рынка ИИ в здравоохранении на уровне 355,8 млрд. долл. / 2032 г. [5,6,7 и др.], CAGR составляет 37,2% на промежутке 2023 — 2032 гг. То есть на протяжении 10 лет аналитики ожидают ежегодный темп роста по всем субрынкам под 40,0%, что будет происходить на фоне аналогичного роста рынков других отраслей (например, финансы и страхование, торговля оптовая и розничная, государственное управление и пр.).

И, что самое важное, темпы изменения бизнес‑процессов, квалификации персонала, соответствующей инфраструктуры также должны успевать переформатироваться.

Прогноз в 355,8 млрд. долл. / 2032 г. является излишне оптимистичным. Оценка в 180,8 млрд. долл. / 2032 г. — выглядит более реалистично.

Общий рынок технологий ИИ состоит из субрынков, наиболее перспективные области рассмотрены далее. Сдерживающие факторы применения ИИ в здравоохранении носят комплексный характер, и применимы ко всему рынку в целом, поэтому рассмотрены в конце настоящей статьи без повторения в каждом субрынке.

1.1. Роботизированная хирургия

9,9 млрд долл. / 2024 г. — объем субрынка.

Роботы с поддержкой ИИ могут использоваться для работы с чувствительными органами и тканями, что поможет снизить кровопотерю, риск инфицирования, сократить период послеоперационного восстановления, облегчить работу медицинского персонала в операционной.

Применение ИИ позволило добиться следующих эффектов:

  • на 10% сокращение затрат и повышение скорости операций.

Примеры:

Наименование потребителя / заказчика / поставщика

Инфраструктура, программное обеспечение

Moon Surgical (коммерческие продажи с 2025 г.)

Собственное ПО Maestro; NVIDIA IGX

Центральная клиническая больница с поликлиникой Управления делами Президента РФ (Россия)

da Vinci Surgical System от Intuitive Surgical, Inc.; AMD FPGA

Hunjan Hospital (Индия)

Робот Stryker, хирургическая система Mako

1.2. Виртуальные помощники

1,7 млрд долл. / 2024 г. — объем субрынка.

Консультации пациентов и ответы на часто задаваемые вопросы с возможностью ответить на дополнительные вопросы или предложить дополнительную информацию, онлайн‑диалог по проверке симптомов с предварительной диагностикой на основе анамнеза плюс возможность запланировать визит к соответствующему врачу или медицинскому специалисту — такого рода рутинные задачи могут помочь разгрузить работу клинического персонала, который затем сможет уделять больше времени непосредственно уходу за пациентами.

Применение ИИ позволило добиться следующих эффектов:

  • 370% составила окупаемость инвестиций, ROI [18];

  • на 30% сокращение времени разговора [18];

  • свыше 23% запросов отвечены в нерабочее время чат‑ботами [19].

Примеры:

Наименование потребителя / заказчика / поставщика

Инфраструктура, программное обеспечение

Cardinal Health, Inc. (США)

IBM® watsonx Assistant™

United Lincolnshire Hospitals NHS Trust (США)

Платформа Envoy

Virtua Health (Марлтон, Нью-Джерси; Сан-Франциско, Калифорния)

Woebot Health

1.3. Административный документооборот, стационарный уход и управление больницами

3,1 млрд долл. / 2024 г. — объем субрынка.

Около 30% [20,21,22] рабочего дня врача тратится на административные задачи (анализ информации для записи или иного обмена релевантными сведениями, составление графиков и пр.), что сокращает эффективность и качество ухода за пациентами.

Применение ИИ позволило добиться следующих эффектов:

  • на 83% сокращение времени, необходимого для размещения пациентов [23];

  • на 10% (по сравнению с предыдущим годом) повышение показателей удержания медсестер [24];

  • на 30 мин. / пациент высвобождение времени;

  • на 25% сокращение нагрузки на отделения неотложной помощи;

  • на 20% снижение конфликтов при составлении расписания [25].

Примеры:

Наименование потребителя / заказчика / поставщика

Инфраструктура, программное обеспечение

UZ Brussel (Брюссель)

Серверы Dell EMC PowerEdge R7515 и R7525 с процессорами AMD EPYC

Northwell Health (Нью-Йорк)

ОС ИИ Aidoc (AiOS ™), которая включает 13 одобренных FDA алгоритмов ИИ

Tampa General Hospital (Флорида)

CareComm Command Center от GE HealthCare Technologies Inc.

Emory Healthcare (Атланта)

Abridge Al + Epic

ApolloMD (США)

Fathom

1.4. Выявление мошенничества

1,6 млрд долл. / 2024 г. — объем субрынка.

По экспертным оценкам, мошеннические действия (например, страховые возмещения за неоказанные медицинские услуги, сознательное завышение счета) наносят урон отрасли здравоохранения свыше 500 млрд долл. ежегодно [26], что приводит к росту расходов на здравоохранение и увеличению страховых взносов для пациентов.

Применение ИИ позволило добиться следующих эффектов:

— В 2–3 раза увеличение количества случаев выявления мошенничества [27].

Примеры:

Наименование потребителя / заказчика / поставщика

Инфраструктура, программное обеспечение

Mastercard® Healthcare Solutions

Brighterion AI

Medicare & Medicaid Services (CMS) (Федеральное агентство Министерства здравоохранения и социальных служб США)

nH Predict

1.5. Точная (или прогностическая) медицина, уменьшение ошибок и погрешности

2,7 млрд долл. / 2024 г. — объем субрынка.

Несоблюдение правил приема лекарств приводит к 25% всех госпитализаций и 50% неудач в лечении [28]. Стоимость медицинских ошибок — около 50 млрд долл. ежегодно [29].

Точная медицина сосредоточена на интеграции различных наборов данных (геномных, транскриптомных, поведенческих и т. д.) для каждого человека, чтобы лучше оценить его здоровье, и для популяции людей, чтобы максимально точно стратифицировать болезни по отдельным подгруппам, что позволяет сделать лечение болезней существенно более экономически выгодным [30].

Применение ИИ позволило добиться следующих эффектов:

  • на 50% сокращение количества ошибок при приеме лекарств [31];

  • 70–80% точности в части предсказания реакции пациента на иммунотерапию [32].

Примеры:

Наименование потребителя / заказчика / поставщика

Инфраструктура, программное обеспечение

Amgen Inc.

NVIDIA AI Enterprise, NVIDIA BioNeMo, NVIDIA DGX SuperPod (31 узел NVIDIA DGX H100 и 248 граф. процессор. с тензорным ядром H100)

Siemens Healthineers AG

Суперкомпьютер Sherlock AI (NVIDIA HGX-1 с граф. процессорами NVIDIA Tesla и NVLink ™, NVIDIA HGX-2 с граф. процессорами NVIDIA Tesla и NVLink, переключатель Mellanox EDR Infiniband)

Johnson & Johnson MedTech

NVIDIA Holoscan на базе NVIDIA IGX (модули NVIDIA Jetson Orin, графические процессоры NVIDIA RTX A6000, сетевая технология NVIDIA ConnectX)

Более 45 тыс. поставщиков мед. услуг

Платформа Viz.ai ™ One

1.6. Медицинская визуализация и диагностика

2,4 млрд долл. / 2024 г. — объем субрынка.

ИИ при анализе маммограмм смог обнаружить рак молочной железы с точностью 94,5% по сравнению с 88,4% у радиологов [33]; компьютерных томограмм смог обнаружить рак легких с точностью до 96% по сравнению с 75% у врачей [34].

В то же время ИИ также несовершенен и не может ставить 100% правильные диагнозы для всех случаев. Следовательно, требуется объединить человеческий опыт медицинских работников с современными техническими возможностями, применять ИИ как дополнительный инструмент к традиционным методам [35]. 

Применение ИИ позволило добиться следующих эффектов:

  • на 10% повышение диагностической точности;

  • на 45% сокращение времени до потенциально спасающего жизнь вмешательства по сравнению с обычной практикой за счет ускорения времени считывания и раннего предупреждения рентгенологов о возможном инсульте крупных сосудов [36].

Примеры:

Наименование потребителя / заказчика / поставщика

Инфраструктура, программное обеспечение

GE HealthCare Technologies Inc.

Centricity, CardioVisio

Университет Хиросимы

AMD Alveo ™ U250

10 субъектов РФ (в т.ч. Москва, Калининградская область, Новгородская область, Республика Татарстан и др.)

ТОП-3 / ООО "СбермедИИ"

1.7. Исследования, открытия и разработка лекарственных средств

2,1 млрд долл. / 2024 г. — объем субрынка.

Традиционная разработка лекарственного средства, от появления его идеи до вывода на рынок, занимает около 12–15 лет при средних затратах на разработку в 1,4–2,6 млрд долл. [37,38]. При этом общий показатель успеха клинических разработок лекарств находится на уровне 10–15% [39].

Пространство химических соединений функционально бесконечно, в нем более 1060 возможных молекул, похожих на лекарства [40]. Для создания лекарственного средства требуется исследовать это обширное химическое пространство в поисках небольшой части молекул, удовлетворяющих задаче многопараметрической оптимизации. Ускоренный процесс поиска лекарств, увеличит скорость лечения заболеваний, тем самым высвобождая ресурсы, которые могут быть использованы в областях, где наблюдается потребность.

Применение ИИ позволило добиться следующих эффектов:

  • на 10% увеличение вероятности успеха испытаний;

  • на 20% снижение стоимости (продолжительности) разработки;

  • в 10 раз увеличение скорости исследований;

  • 1–2 года сокращение времени одобрения лекарственного средства [41].

Примеры:

Наименование потребителя / заказчика / поставщика

Инфраструктура, программное обеспечение

Amgen Inc.

NVIDIA AI Enterprise, NVIDIA BioNeMo, Суперкомпьютер Freyja - NVIDIA DGX SuperPod (31 узел NVIDIA DGX H100 и 248 граф. процессоров с тензорным ядром H100)

Ono Pharmaceutical Co., Ltd.

Суперкомпьютер Tokyo-1 (16 систем NVIDIA DGX H100, каждая с восемью графическими процессорами с тензорным ядром NVIDIA H100)

Deloitte Consulting LLP

Собственное ПО Quartz Atlas, NVIDIA DGX Cloud, NVIDIA BioNeMo, NVIDIA AI Enterprise, NVIDIA NIM

Terray Therapeutics

NVIDIA RTX ™ A6000, DGX Cloud, NVIDIA AI Enterprise

Sanger Institute

Сервер NVIDIA DGX-1™, NVIDIA® NVLink®

Например, использование ИИ позволило обнаружить 9 антибиотиков на основе анализа базы данных химических соединений. Это первое успешное применение ИИ для поиска антибиотика. А также первое за 30 лет открытие нового класса антибиотиков.

1.8. Прочее

4,8 млрд долл. / 2024 г. — объем субрынка.

Прочее применение ИИ в здравоохранении включает:

  • обеспечение безопасности данных, кибербезопасность;

  • обеспечения работы регулирующих органов и взаимодействия с ними [42];

  • обеспечение верификации, сохранения знаний;

  • прочее применение.

Оценочно, объем российского рынка ИИ в здравоохранении составит 0,4 млрд долл. или 40,0 млрд. руб. / 2024 г. (см. табл. 2).

Таблица 2. Прогноз российского рынка ИИ в здравоохранении, млрд долл.

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Несмотря на то, что технологии ИИ демонстрируют эффективность в здравоохранении, большинство проектов оказываются неудачными. Например, ИИ на текущем этапе не доказал эффективность использования для уменьшения симптомов депрессии у подростков [43].

Однако, объем мирового рынка стоимости врачебных ошибок, мошенничества в здравоохранении с учетом косвенных потерь для экономики и систем социального обеспечения составляет триллионы долларов. Именно поэтому идет поиск применения технологии ИИ и других инструментов для сокращения этого объема упущенной выручки.

Необходимо отметить, что здравоохранение — консервативная отрасль, так как несет высокие риски здоровью и качеству жизни. Применение технологий ИИ, помимо традиционных вопросов по эффективности технологий / решений, затрагивает комплекс сложных вопросов:

  1. конфиденциальность данных

  2. изменение характера работы — взаимодействие человека и искусственного интеллекта

  3. надежность и доверие при принятии решений, основанных на искусственном интеллекте

  4. юридические проблемы ответственности врачей и разработчиков при использовании (или не использовании) ИИ

  5. этические вопросы и нечувствительность ИИ к потенциальному воздействию.

В целом, использование технологии ИИ сейчас находится на стадии итерации. ИИ — это инструмент или «второе мнение» для врача.

Следующие 5–7 лет будут посвящены пониманию и ценности, которые общество может извлечь из этих цифровых активов, и тому, как они могут быть преобразованы в достижение лучших клинических результатов и последующего создания новых информационных ресурсов и инструментов.

Функции по уходу за пациентами никогда не могут быть полностью заменены искусственным интеллектом; однако задачи, которые отвлекают врачей от пациентов, могут.

Автор: Мартынов А.И.

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5. Artificial Intelligence (AI) in Healthcare Market (By Component: Software, Hardware, Services; By Application: Virtual Assistants, Diagnosis, Robot Assisted Surgery, Clinical Trials, Wearable, Others; By Technology: Machine Learning, Natural Language Processing, Context‑aware Computing, Computer Vision; By End User) — Global Industry Analysis, Size, Share, Growth, Trends, Regional Outlook, and Forecast 2022 — 2030 [Электронный ресурс] // Precedence Research — аналитическая компания. URL: https://www.precedenceresearch.com/artificial‑intelligence‑in‑healthcare‑market (дата обращения 16.06.2024).

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7. Artificial Intelligence (AI) in Healthcare Market by offering (Hadrware, Software, Services), Technology (Machine Learning, Natural Language Processing), Application (Medical imaging & Diagnostics, Patient Data & Risk Analysis), End User & Region — Global Forecast to 2029 [Электронный ресурс] // MarketsAndMarkets — аналитическая компания. URL: https://www.marketsandmarkets.com/Market‑Reports/artificial‑intelligence‑healthcare‑market-54 679 303.html (дата обращения 16.06.2024).

8. Artificial Intelligence in Healthcare Market Size, Growth, Opportunities & Trends: Global Industry Analysis and Forecast (2024–2030) [Электронный ресурс] // Maximize Market Research — аналитическая компания. URL: https://www.maximizemarketresearch.com/market‑report/global‑artificial‑intelligence‑ai‑healthcare‑market/21 261/ (дата обращения 16.06.2024).

9. Artificial Intelligence (AI) In Drug Discovery Market (By Type: Preclinical and Clinical Testing, Molecule Screening, Target Identification, De Novo Drug Design and Drug Optimization; By Application: Neurology, Infectious Disease, Oncology, Others; By Drug Type: Small Molecules, Large Molecules; By Offering: Software, Services; By Technology; By End User) — Global Industry Analysis, Size, Share, Growth, Trends, Regional Outlook, and Forecast 2023–2032 [Электронный ресурс] // Precedence Research — аналитическая компания. URL: https://www.precedenceresearch.com/artificial‑intelligence‑in‑drug‑discovery‑market (дата обращения 16.06.2024).

10. AI‑based Surgical Robots Market To Reach USD 34.9 Billion By 2032 | DataHorizzon Research [Электронный ресурс] // Yahoo — СМИ. URL: https://finance.yahoo.com/news/ai‑based‑surgical‑robots‑market-120 000 135.html?guccounter=1&guce_referrer=aHR0cHM6Ly93d3cuZ29vZ2xlLmNvbS8&guce_referrer_sig=AQAAAGIKNRLNCD2aAkv‑Gyg2AKDXHBbXXUJ_A7pZgpAxgmxORNFFJwNY8CNRHI6so3T_NXzYkLHcsIWlSY0SajbJVFQFJAeon3HEfn‑QPASZRCe11LK9elGFZgUHRiwtMrmAKIeu69VQ‑lVBtYrB7pEmXKu8WZEzxn9BOxuwFuZAps9x (дата обращения 16.06.2024).

11. Health Intelligent Virtual Assistant Market Predictions: CAGR of 36.8%, Reaching USD 20,901.0 Mn by 2032 | Insights by Dimension Market Research [Электронный ресурс] // Yahoo — СМИ. URL: https://finance.yahoo.com/news/health‑intelligent‑virtual‑assistant‑market-160 600 780.html (дата обращения 16.06.2024).

12. Digital Assistants in Healthcare Market Size & Share Analysis — Growth Trends & Forecasts (2024 — 2029) Source: https://www.mordorintelligence.com/industry‑reports/digital‑assistants‑in‑healthcare‑market [Электронный ресурс] // Mordor Intelligence — аналитическая компания. URL: https://www.mordorintelligence.com/industry‑reports/digital‑assistants‑in‑healthcare‑market (дата обращения 16.06.2024).

13. AI in Emergency Room and Hospital Management Market Research Report — Segmented By Offerings (Hardware, Software, Services); By Technology (Machine Learning, Natural Language Processing, Context‑aware Computing, Computer Vision); By End User (Hospital & Healthcare Providers, Patients, Pharmaceuticals & Biotechnology Companies, Healthcare Payers, Others); and Region — Size, Share, Growth Analysis | Forecast (2024 — 2030) [Электронный ресурс] // Intellect Markets — аналитическая компания. URL: https://www.intellectmarkets.com/report/aI‑emergency‑room‑hospital‑management‑market (дата обращения 16.06.2024).

14. Healthcare Fraud Detection Market Size, Share, Competitive Landscape and Trend Analysis Report by Type, by Component, by Application, by End User: Global Opportunity Analysis and Industry Forecast, 2022–2031 [Электронный ресурс] // Allied Market Research — аналитическая компания. URL: https://www.alliedmarketresearch.com/medical‑fraud‑detection‑management‑market (дата обращения 16.06.2024).

15. Artificial Intelligence in Healthcare Market: Global Industry Analysis and Forecast (2024–2030) [Электронный ресурс] // Maximize Market Research — аналитическая компания. URL: https://www.maximizemarketresearch.com/market‑report/global‑artificial‑intelligence‑ai‑healthcare‑market/21 261/ (дата обращения 16.06.2024).

16. AI in Medical Imaging Market (By Image Acquisition Technology: X‑Ray, Computed Tomography, Magnetic Resonance Imaging, Ultrasound Imaging, Molecular Imaging; By End Use: Hospitals and Clinics, Research Laboratories and Diagnostic Centers, Others; By Application: Digital Pathology, Oncology, Cardiovascular, Neurology, Lung, Breast, Liver, Oral Diagnostics, Other) — Global Industry Analysis, Size, Share, Growth, Trends, Regional Outlook, and Forecast 2024–2033 [Электронный ресурс] // Precedence Research — аналитическая компания. URL: https://www.precedenceresearch.com/ai‑in‑medical‑imaging‑market (дата обращения 16.06.2024).

17. Artificial Intelligence in Diagnostics Market To Reach USD 7.8 Billion By 2032 | DataHorizzon Research [Электронный ресурс] // Yahoo — СМИ. URL: https://finance.yahoo.com/news/artificial‑intelligence‑diagnostics‑market‑reach-064 000 883.html (дата обращения 16.06.2024).

18. The power of today»s market ready AI to reduce transaction fraud [Электронный ресурс] // Forrester — консалтинговая компания. URL: https://www.ibm.com/products/watsonx‑assistant#Customer+benefits (дата обращения 16.06.2024).

19. Virtual Assistants and chatbots [Электронный ресурс] // Healthcare Communications UK Ltd. — медицинская ИТ‑компания. URL: https://healthcare‑communications.com/solutions/virtual‑assistants‑and‑chatbots/ (дата обращения 16.06.2024).

20. Richard Payerchin. Physicians spend 4.5 hours a day on electronic health records [Электронный ресурс] // Medical Economics — СМИ. URL: https://www.medicaleconomics.com/view/physicians‑spend-4–5-hours‑a‑day‑on‑electronic‑health‑records (дата обращения 16.06.2024).

21. Andrew Cass. The hours 23 physician specialties spend on paperwork, administration [Электронный ресурс] // Becker's Healthcare — СМИ. URL: https://www.beckershospitalreview.com/hospital‑physician‑relationships/the‑hours-23-physician‑specialties‑spend‑on‑paperwork‑administration.html (дата обращения 16.06.2024).

22. Eva Anskär, Malou Lindberg, Magnus Falk, Agneta Andersson. Time utilization and perceived psychosocialwork environment among staff in Swedishprimary care settings [Электронный ресурс] // ResearchGate — СМИ. URL: https://www.researchgate.net/publication/323 619 953_Time_utilization_and_perceived_psychosocial_work_environment_among_staff_in_Swedish_primary_care_settings#pf8 (дата обращения 16.06.2024).

23. Megan Kostner. Tampa General Hospital taps Palantir for care coordination AI software [Электронный ресурс] // Babcox Media Inc. — СМИ. URL: https://tbbwmag.com/2024/06/12/tampa‑general‑hospital‑taps‑palantir‑for‑care‑coordination‑ai‑software/ (дата обращения 16.06.2024).

24. Hamza Sohail. Northwell Health Achieves $1M in Savings with Laudio»s AI Platform [Электронный ресурс] // HIT Consultant Media — СМИ. URL: https://hitconsultant.net/2024/03/13/northwell‑health‑achieves-1m‑in‑savings‑with‑laudios‑ai‑platform/ (дата обращения 16.06.2024).

25. The Profound Impact of AI in Healthcare Administration [Электронный ресурс] // ShiftMed — медицинская ИТ‑компания. URL: https://www.shiftmed.com/blog/impact‑of‑ai‑in‑healthcare‑administration/ (дата обращения 16.06.2024).

26. Prevent and save: advanced AI for fraud, waste and abuse [Электронный ресурс] // Mastercard — платежная система. URL: https://brighterion.com/healthcare‑ai‑reduce‑fwa/ (дата обращения 16.06.2024).

27. The power of today»s market‑ready AI to reduce transaction fraud [Электронный ресурс] // Mastercard — платежная система. URL: https://brighterion.com/market‑ready‑ai‑to‑reduce‑transaction‑fraud‑ebook‑thank‑you/ (дата обращения 16.06.2024).

28. AI Voice Assistants In Healthcare: Top Use Cases [Электронный ресурс] //Enterprise Bot — медицинская ИТ‑компания. URL: https://www.enterprisebot.ai/blog/ai‑voice‑assistants‑in‑healthcare‑top‑use‑cases (дата обращения 16.06.2024).

29. How Generative AI Can Help Reduce Medical Errors [Электронный ресурс] // Khalpey AI Lab — медицинская ИТ‑компания. URL: https://khalpey‑ai.com/how‑generative‑ai‑can‑help‑reduce‑medical‑errors/ (дата обращения 16.06.2024).

30. Карпов О. Э., Храмов А. Е. Информационные технологии, вычислительные системы и искусственный интеллект в медицине. — М.: ДПК Пресс, 2022. — 480 с., ил.— ISBN 978-5-91976-232-4

31. How Does Ai Reduce Human Error In Healthcare [Электронный ресурс] // Ambula Health — медицинская ИТ‑компания. URL: https://www.ambula.io/how‑does‑ai‑reduce‑human‑error‑in‑healthcare/ (дата обращения 16.06.2024).

32. GE HealthCare Announces New Data Validating Artificial Intelligence Models for Predicting Patient Response to Immunotherapies [Электронный ресурс] // GE HealthCare Technologies Inc. — медицинская ИТ‑компания. URL: https://www.gehealthcare.com/middle‑east/about/newsroom/press‑releases/ge‑healthcare‑announces‑new‑data‑validating‑artificial‑intelligence‑models‑for‑predicting‑patient‑response‑to‑immunotherapies (дата обращения 16.06.2024).

33. Leveraging AI to Improve Detection of Cervical Precancer Infographic [Электронный ресурс] // National Cancer Institute — главным агентством федерального правительства по исследованию рака и подготовке кадров. URL: https://www.cancer.gov/research/improve‑detection‑of‑cervical‑precancer‑leveraging‑ai‑infographic (дата обращения 16.06.2024).

34. Lung Cancer Screening with AI: Benefits and Challenges [Электронный ресурс] // Rayscape — Научно‑исследовательская компания. URL: https://rayscape.ai/single‑article?id=ZDPrkBAAAB8A5zn‑ (дата обращения 16.06.2024).

35. Kendall K. Hall, Eleanor Fitall. Artificial Intelligence and Diagnostic Errors [Электронный ресурс] // Agency for Healthcare Research and Quality — Агентство по исследованиям и качеству здравоохранения. URL: https://psnet.ahrq.gov/perspective/artificial‑intelligence‑and‑diagnostic‑errors (дата обращения 16.06.2024).

36. Theresa Sevilis, Thomas Devlin. Validation of Artificial Intelligence to Limit Delays in Acute Stroke Treatment and Endovascular Therapy [Электронный ресурс] // Viz.ai, Inc. — медицинская ИТ‑компания. URL: https://www.viz.ai/publications/validation‑of‑ai‑to‑limit‑delays‑in‑acute‑stroke‑treatment‑endovascular‑therapy (дата обращения 16.06.2024).

37. Richard C. Mohsa, Nigel H. Greigb. Drug discovery and development: Role of basic biological research [Электронный ресурс] // National Library of Medicine — Национальная библиотека по биомедицинской и геномной информации США. URL: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5 725 284/ (дата обращения 16.06.2024).

38. Тихонова Ю. Искусственный интеллект ускорит создание лекарств в два‑три раза [Электронный ресурс] // ComNews — СМИ. URL: https://www.comnews.ru/content/231 682/2024–02–21/2024-w08/1007/iskusstvennyy‑intellekt‑uskorit‑sozdanie‑lekarstv‑dva‑tri‑raza?ysclid=lvqhypp8k3 783 806 908 (дата обращения 16.06.2024).

39. Duxin Sun, Wei Gao, Hongxiang Hu, Simon Zhou. Why 90% of clinical drug development fails and how to improve it? [Электронный ресурс] // ScienceDirect — ведущая платформа рецензируемой научной литературы. URL: https://www.sciencedirect.com/science/article/pii/S2 211 383 522 000 521 (дата обращения 16.06.2024).

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Источник

  • 07.09.23 16:24 CherryTeam

    Cherry Team atlyginimų skaičiavimo programa yra labai naudingas įrankis įmonėms, kai reikia efektyviai valdyti ir skaičiuoti darbuotojų atlyginimus. Ši programinė įranga, turinti išsamias funkcijas ir patogią naudotojo sąsają, suteikia daug privalumų, kurie padeda supaprastinti darbo užmokesčio skaičiavimo procesus ir pagerinti finansų valdymą. Štai keletas pagrindinių priežasčių, kodėl Cherry Team atlyginimų skaičiavimo programa yra naudinga įmonėms: Automatizuoti ir tikslūs skaičiavimai: Atlyginimų skaičiavimai rankiniu būdu gali būti klaidingi ir reikalauti daug laiko. Programinė įranga Cherry Team automatizuoja visą atlyginimų skaičiavimo procesą, todėl nebereikia atlikti skaičiavimų rankiniu būdu ir sumažėja klaidų rizika. Tiksliai apskaičiuodama atlyginimus, įskaitant tokius veiksnius, kaip pagrindinis atlyginimas, viršvalandžiai, premijos, išskaitos ir mokesčiai, programa užtikrina tikslius ir be klaidų darbo užmokesčio skaičiavimo rezultatus. Sutaupoma laiko ir išlaidų: Darbo užmokesčio valdymas gali būti daug darbo jėgos reikalaujanti užduotis, reikalaujanti daug laiko ir išteklių. Programa Cherry Team supaprastina ir pagreitina darbo užmokesčio skaičiavimo procesą, nes automatizuoja skaičiavimus, generuoja darbo užmokesčio žiniaraščius ir tvarko išskaičiuojamus mokesčius. Šis automatizavimas padeda įmonėms sutaupyti daug laiko ir pastangų, todėl žmogiškųjų išteklių ir finansų komandos gali sutelkti dėmesį į strategiškai svarbesnę veiklą. Be to, racionalizuodamos darbo užmokesčio operacijas, įmonės gali sumažinti administracines išlaidas, susijusias su rankiniu darbo užmokesčio tvarkymu. Mokesčių ir darbo teisės aktų laikymasis: Įmonėms labai svarbu laikytis mokesčių ir darbo teisės aktų, kad išvengtų baudų ir teisinių problemų. Programinė įranga Cherry Team seka besikeičiančius mokesčių įstatymus ir darbo reglamentus, užtikrindama tikslius skaičiavimus ir teisinių reikalavimų laikymąsi. Programa gali dirbti su sudėtingais mokesčių scenarijais, pavyzdžiui, keliomis mokesčių grupėmis ir įvairių rūšių atskaitymais, todėl užtikrina atitiktį reikalavimams ir kartu sumažina klaidų riziką. Ataskaitų rengimas ir analizė: Programa Cherry Team siūlo patikimas ataskaitų teikimo ir analizės galimybes, suteikiančias įmonėms vertingų įžvalgų apie darbo užmokesčio duomenis. Ji gali generuoti ataskaitas apie įvairius aspektus, pavyzdžiui, darbo užmokesčio paskirstymą, išskaičiuojamus mokesčius ir darbo sąnaudas. Šios ataskaitos leidžia įmonėms analizuoti darbo užmokesčio tendencijas, nustatyti tobulintinas sritis ir priimti pagrįstus finansinius sprendimus. Pasinaudodamos duomenimis pagrįstomis įžvalgomis, įmonės gali optimizuoti savo darbo užmokesčio strategijas ir veiksmingai kontroliuoti išlaidas. Integracija su kitomis sistemomis: Cherry Team programinė įranga dažnai sklandžiai integruojama su kitomis personalo ir apskaitos sistemomis. Tokia integracija leidžia automatiškai perkelti atitinkamus duomenis, pavyzdžiui, informaciją apie darbuotojus ir finansinius įrašus, todėl nebereikia dubliuoti duomenų. Supaprastintas duomenų srautas tarp sistemų padidina bendrą efektyvumą ir sumažina duomenų klaidų ar neatitikimų riziką. Cherry Team atlyginimų apskaičiavimo programa įmonėms teikia didelę naudą - automatiniai ir tikslūs skaičiavimai, laiko ir sąnaudų taupymas, atitiktis mokesčių ir darbo teisės aktų reikalavimams, ataskaitų teikimo ir analizės galimybės bei integracija su kitomis sistemomis. Naudodamos šią programinę įrangą įmonės gali supaprastinti darbo užmokesčio skaičiavimo procesus, užtikrinti tikslumą ir atitiktį reikalavimams, padidinti darbuotojų pasitenkinimą ir gauti vertingų įžvalgų apie savo finansinius duomenis. Programa Cherry Team pasirodo esanti nepakeičiamas įrankis įmonėms, siekiančioms efektyviai ir veiksmingai valdyti darbo užmokestį. https://cherryteam.lt/lt/

  • 08.10.23 01:30 davec8080

    The "Shibarium for this confirmed rug pull is a BEP-20 project not related at all to Shibarium, SHIB, BONE or LEASH. The Plot Thickens. Someone posted the actual transactions!!!! https://bscscan.com/tx/0xa846ea0367c89c3f0bbfcc221cceea4c90d8f56ead2eb479d4cee41c75e02c97 It seems the article is true!!!! And it's also FUD. Let me explain. Check this link: https://bscscan.com/token/0x5a752c9fe3520522ea88f37a41c3ddd97c022c2f So there really is a "Shibarium" token. And somebody did a rug pull with it. CONFIRMED. But the "Shibarium" token for this confirmed rug pull is a BEP-20 project not related at all to Shibarium, SHIB, BONE or LEASH.

  • 24.06.24 04:31 tashandiarisha

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  • 26.06.24 18:46 Jacobethannn098

    LEGAL RECOUP FOR CRYPTO THEFT BY ADRIAN LAMO HACKER

  • 26.06.24 18:46 Jacobethannn098

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  • 04.07.24 04:49 ZionNaomi

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  • 17.07.24 02:26 thompsonrickey

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  • 01.10.24 14:54 Sinewclaudia

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  • 02.10.24 22:27 Emily Hunter

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  • 31.10.24 00:13 ytre89

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    If I hadn't found a review online and filed a complaint via email to support@deftrecoup. com , the people behind this unregulated scheme would have gotten away with leaving me in financial ruins. It was truly the most difficult period of my life.

  • 22.11.24 04:41 [email protected]

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