مروری بر کاربردهای هوش مصنوعی در پیش‌بینی رخداد و تشخیص بیماری‌ها در دامپزشکی: چالش‌ها و تکنیک‌ها
محورهای موضوعی : سایر علوم وابسته
مهدی باشی‌زاده
1
,
پرهام صوفی‌زاده
2
,
مهدی ضمیری
3
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آیدا لامعی
4
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متین ستوده‌نژاد
5
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مهسا دانشمند
6
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ملیکا قدرتی
7
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اریکا عیسوی
8
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حسام الدین اکبرین
9
1 - بخش اپیدمیولوژی و بیماری‌های مشترک، گروه بهداشت و کنترل مواد غذایی، دانشکده دامپزشکی دانشگاه تهران
2 - دانش آموخته دانشکده دامپزشکی دانشگاه تهران
3 - دانشجوی دکترای عمومی دامپزشکی، دانشکده دامپزشکی دانشگاه تهران
4 - دانشجوی دکترای عمومی دامپزشکی، دانشکده دامپزشکی دانشگاه تهران
5 - دانشجوی دکترای عمومی دامپزشکی، دانشکده دامپزشکی دانشگاه تهران
6 - گروه علوم زیستی مقایسه‌ای، دانشکده دامپزشکی دانشگاه تهران
7 - دانشجوی دکترای عمومی دامپزشکی، دانشکده دامپزشکی دانشگاه تهران
8 - دانشجوی دکترای عمومی دامپزشکی، دانشکده دامپزشکی دانشگاه تهران
9 - بخش اپیدمیولوژی و بیماری‌های مشترک، گروه بهداشت و کنترل مواد غذایی، دانشکده دامپزشکی دانشگاه تهران
کلید واژه: هوش مصنوعی, دامپزشکی, پیش‌بینی, تشخیص,
چکیده مقاله :
تشخیص زودهنگام بیماری‌ها، یکی از هدف‌های اصلی دستگاه‌های بهداشتی و سلامت است. این تشخیص به‌موقع می‌تواند از آسیب‌های بالقوه‌ی بیماری‌ها بکاهد. اهمیت این مسأله در دامپزشکی به‌سبب تلفیق آن با هدف‌های اقتصادی نیز چندین برابر می‌شود. بنابراین وجود یک رویکرد پیش‌بینی‌کننده برای تشخیص زودهنگام بیماری‌ها ضروری است. این رویکرد باید مبتنی بر شواهد بوده و از دقت بالایی برخوردار باشد. هم‌چنین باید از نظر اقتصادی نیز صرفه‌ی بالایی داشته باشد. هوش مصنوعی توانایی یک کامپیوتر یا ربات کنترل‌شده توسط کامپیوتر برای انجام کارهایی است که معمولاً توسط انسان انجام می‌شود و به هوش و تشخیص انسان نیاز دارد. ظهور تکنیک‌های هوش مصنوعی و یادگیری ماشین در دنیای امروز، موجب بهبود عملکردهای موجود در سامانه‌های مراقبتی و بهداشتی شده است، به‌طوری که با به‌کارگیری این تکنولوژی، پیشرفت چشم‌گیری در رویه‌های پیش‌بینی رخداد و تشخیص بیماری‌ها، مدیریت و بهداشت در سطح کلان و ... شده است. هم‌چنین نوع بیماری قابل تشخیص، می‌تواند بسیار گسترده باشد و هرنوع بیماری‌ای که دارای داده‌ی قابل پردازش با الگوریتم‌های هوش مصنوعی باشد، می‌تواند توسط مدل آموزش داده‌شده تشخیص داده شود، اما صحت تشخیص با توجه به شاخص‌های بیماری و داده‌ی جمع‌آوری‌شده و مواردی مانند این متفاوت خواهد بود. در این مقاله به مهم‌ترین کاربردهای هوش مصنوعی در دامپزشکی اشاره خواهد شد و به‌طور کلی، این کاربردها در حوزه‌های گوناگونی مانند تشخیص بیماری‌های شایع، تشخیص تفریقی، پیش‌بینی رخداد بیماری‌ها، تکنیک‌های تصویربرداری تشخیصی دامپزشکی، کلینیکال پاتولوژی دامپزشکی و ... مورد بررسی قرار خواهند گرفت. علاوه بر این به چالش‌های موجود در این زمینه نیز اشاره خواهد شد. این مقاله مروری بر مطالعه‌های موجود در این زمینه است.
Early diagnosis of diseases is one of the main goals of health and wellness centers. Timely diagnosis can reduce the potential damage of diseases. The importance of this issue in veterinary medicine multiplies due to its combination with economic goals. Therefore, a predictive approach is necessary for early diagnosis of diseases. This approach should be evidence-based and highly accurate. It should also be economically efficient. Artificial intelligence is the simulation of human intelligence and judgment by a computer or a robot that is programmed or trained to perform tasks that normally need human abilities. The emergence of artificial intelligence and machine learning techniques in today's world has improved the existing functions in health care systems. So that with the application of this technology, a significant progress has been made in the procedures of event prediction and disease diagnosis, management and health at the macro level, etc. Furthermore, the scope of diagnosable diseases is extensive, encompassing any ailment for which relevant data can be processed by artificial intelligence algorithms. The trained model has the capability to diagnose a wide range of diseases, with accuracy contingent upon factors such as disease indicators, collected data, and other pertinent variables. In this review article, the most important applications of artificial intelligence in veterinary medicine will be mentioned, and in general, these applications will be examined in various fields such as diagnosis of common diseases, differential diagnosis, prediction of disease occurrence, veterinary diagnostic imaging techniques, veterinary clinical pathology, etc. In addition, the challenges in this field will also be mentioned. This article is a review of recent studies in this fiel.
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