Artificial intelligence (AI) and bioinformatics have played a central role in integrating diverse diagnosis information toward an enhanced diagnosis power for diseases. AI-based deep learning and computer algorithms are poised to revolutionize image-based diagnostics, while other AI subtypes have begun to show similar promise in various diagnostic modalities. Known for our deeply-rooted expertise and abundant experience in the in vitro diagnostics (IVD), Creative Biolabs is dedicated to offering AI and machine learning-based solutions to accelerate your IVD projects and advance digital therapeutics.

AI in Medical Diagnostics

Artificial intelligence is a branch of computer science capable of analyzing complex medical data to promote diagnosis processes, treatment protocol development, drug development, personalized medicine, and patient monitoring. It usually integrates big data, machine learning, algorithm modeling, and data bioinformatics. AI can be divided into two parts: 1) machine learning, structured data (i.e. images and genetic data) are analyzed; 2) natural language processing, unstructured data are analyzed. With the development of analysis methods, algorithms, and applications, AI has revolutionized medical diagnostics in cancer, nervous system disease, cardiovascular disease, liver disease, congenital cataract disease, etc.

The digital pathology AI development ‘roadmap’.Fig.1 The digital pathology AI development 'roadmap'. (Colling, 2019)

Current Applications of AI in Medical Diagnostics

The diagnostic applications of AI and machine learning can be summarized into the following categories:

  • Chatbots: AI-chatbots with speech recognition can identify patterns in patient symptoms to form a potential diagnosis, prevent disease and/or recommend an appropriate course of action.
  • Pathology: Machine learning and AI technologies can help pathologists to review and interpret digital images of surgical pathology slides prepared from formalin-fixed, paraffin-embedded (FFPE) tissue samples. It is a supplement of traditional diagnosis which relies on microscopes.
  • Oncology: Deep learning has been trained to extract the actual meaning from the search query and look for the most relevant results across huge scientific datasets. Algorithms can recognize cancerous tissue at a level comparable to trained physicians.
  • Genetic Diseases: Deep learning and facial analysis can be used to detect phenotypes that correlate with rare genetic diseases.

AI-based IVD for SARS-CoV-2

AI Bioinformatics Services

With the advance of AI in imaging applications and digital pathology, scientists have used AI-based IVD to help defeat the public health crisis brought on by SARS-CoV-2. Diagnosis in collaboration with AI has begun implementing increasingly sophisticated machine-learning techniques to improve the power of data analysis; to improve upon standard risk assessment tools; to elucidate factors that contribute to disease progression; and to advance personalized medicine by predicting a patient's response to treatment. Scientists have developed an intelligence tool that uses three common lab tests to identify patients likely to have COVID-19 infection. Cloud-based data analytics and remote monitoring platform provide clinicians with clinical decision support for early identification of any physiological changes that could indicate deterioration, enabling earlier interventions for better outcomes.

What Can We Do?

Creative Biolabs highlights the role of AI in enhancing understanding and prioritization of variance in the clinical setting and proposes deep learning frameworks for medical diagnostics. We are experienced in digitizing the biological samples and the generation of best-fit AI algorithms for possible AI interpretation. Our AI algorithms are periodically trained to perform a single task: for example, to classify images of skin lesions into diagnostic categories or to provide a molecular diagnosis from a combination of genomic and phenotypic data. It is flexible enough to address other clinical diagnostic tasks. Our specific service categories include computer vision, time series analysis, automatic speech recognition, natural language processing, phenotype-to-genotype mapping, genotype-to-phenotype prediction, etc.

Our goal is to empower pathologists to make a quicker and more accurate diagnosis with state-of-the-art decision-support AI tools. As a leader in IVD development, Creative Biolabs is proud to explore AI and machine learning-based solutions to promote diagnostic precision and early intervention, thus resulting in improved health outcomes, lowering the healthcare burden and costs.

If you want to get more information about our SNPs analysis, please feel free to contact us.


  1. Colling, R.; et al. Artificial intelligence in digital pathology: a roadmap to routine use in clinical practice. The Journal of pathology. 2019, 249(2): 143-150.

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