How Does Artificial Intelligence Technology Affect In Vitro Diagnostic Applicati

Posted by Vivian creative on September 7th, 2021

Continuous technological innovations and breakthroughs in the healthcare sector are the keys to improve diagnostic accuracy, advance patient care, and optimize clinical operations. Artificial intelligence (AI) technology has been one of the most promising technologies applied in medicine, health care, and clinical decision-making. In vitro diagnostic (IVD) testing, due to the ability of data generation, plays a critical role in clinical decision-making. Therefore, the combination of AI and IVD has been identified by several professionals and researchers with the potential to drive advancement in laboratories and deliver better global business and clinical outcomes.

What is Artificial Intelligence?


AI is a technology that equips machines with computers to mimic and simulate human intelligence, behaviors, and capabilities of solving problems and making decisions, which is based on a lot of datasets, algorithms, computational science, and time. Artificial intelligence usually is mentioned along with the techniques of machine learning (ML) and deep learning that also involve machines in further learning from new data without reprogramming and automatically imitating human thought patterns.

Implications of Artificial Intelligence for In Vitro Diagnostics

Artificial intelligence for in vitro diagnostics is revolutionizing the development of medical devices and is being leveraged in a wide range of applications, including digital image analysis and in vitro diagnostic testing. A large number of professionals take a positive attitude and believe that AI is likely to dramatically advance current diagnostic testing processes.

When it comes to saving costs in human labor and increasing operational efficiency, the future of AI-based IVD solutions cannot be ignored. AI-based IVD testing means shorter turnaround time, automatized repetitive tasks, better diagnostic test utilization, etc. addressing issues like lack of laboratory staff and technicians in the field of IVD. Also, healthcare professionals can automate tasks and process more data leveraging AI technology, exploring more meaningful solutions to both basic and complicated issues.


What\'s more, artificial intelligence could completely change the current workflow in the laboratory, patient diagnosis, and treatment, which will consequently affect the role of the key stakeholders in the IVD market like HCPs, laboratory staff, IVD manufacturers, and IVD development solution providers.

The Applications of AI-based In Vitro Diagnostics


AI-based IVD tools are expected to unprecedentedly advance patient care, disease management, prevention, and treatment.

l Tumor Screening and Therapy Evaluation

Scientists have understood the role of the immune system playing in controlling tumorigenesis and tumor progression in past decades. However, data acquisition and analysis seem to be a new challenge in tumor detection and immunotherapy evaluation, which is possibly addressed by AI technology.

For example, AI technology has been applied in the area of breast cancer screening via mammography. Current imaging and data analysis tools often ignore cancer cells due to dense breast tissues, while AI-based new imaging technologies and search engines can effectively improve current screening methods with faster analysis, real-time diagnosis, and the absence of human error.

It also takes an important part in the pipeline of evaluating cancer immunotherapies from an array of approaches, including targetable epitopes prediction for cancer vaccinations, matching appropriate immunotherapeutics with responsive patients, and identification of adverse reactions prior to administration.

Other applications of artificial intelligence in cancer include the development of machine-learning algorithms to predict and screen tumor neoantigens, deep learning models to assess tumor-associated stroma, and AI-based biomarker detection in immunohistochemically stained tumor images.

l Digital Diagnostics and Disease Research

Integrating AI into an in vitro diagnostic platform can improve the performance of the device and diagnostic accuracy, in which liquid biopsy and tissue analysis are involved. Compared with traditional methods in disease research, machine-learning enhanced liquid biopsy is more sensitive and specific in measuring a large number of biomarkers and extracting large quantities of molecular data. In addition, the integration of AI algorithms in diagnostic tools allows digital images to replace the manual approaches based on microscopy, bringing insights on tumor grading and prognostic. As a result, researchers are able to accelerate the diagnosis progress, improve diagnostic outcomes, and spare more time on other tasks.

l Healthcare

Another application of AI for in vitro diagnostics is biosensors and other internet forms of things. Digital devices, such as wearable devices are widely used to acquire biomedical signals as digital biomarkers that can be analyzed and processed by artificial intelligence in real-time, turning data into outcomes that can be understood by patients or healthcare staff. A good example is the AI-based biosensors for point of care (POC) cardiac monitoring diagnostics.

Concerns of AI-based Diagnostic Applications


Though a great potential is observed, a lot of uncertainties like reliability, patient safety, and financing, still negatively affect the feasibility of AI diagnostic solutions. Concerns mainly exist over potential medical errors, misdiagnosis, and the reliability of the AI results. Moreover, the upfront costs and constraints such as FDA approval and data security are also named as an inhibitor for AI diagnostic solution adoption.

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Vivian creative
Joined: March 15th, 2021
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