Artificial Intelligence and Healthcare: Applications for Diagnosis
Blog Posted: Jan. 14, 2020

Artificial intelligence (AI) is emerging as an incredible tool in specialties which focus on image analysis for medical diagnostics such as radiology and pathology. Specialists may use AI to improve their diagnostic accuracy or address difficult cases where subjectivity may cause inconsistent diagnoses. Pathologists have varying levels of experience and perspective, such as the type and amount of stain to use or the best way to triage samples, which leads to a natural variability in the way they evaluate particular tissue slides. Furthermore, some evaluations are inherently difficult. For instance, the complexity of proper prostate cancer interpretation on biopsy and proper grade scoring has given rise to entire textbooks dedicated to these matters. These are just a few examples why substantial effort has been put toward the development of algorithms that can assist the clinician in image interpretation for diagnosis, prognostication, and production of clinical reports. Many AI methods equip pathologists with an objective, quantifiable and consistent foundation for interpreting the findings in a tissue slide. The predominant technical approach advancing AI for healthcare is machine learning: the development of data-driven algorithms that learn to mimic human behavior on the basis of prior example or experience.

In pathology specifically, AI-based diagnostic platforms may perform image analysis for tissue histology, analyze molecular outputs from diagnostic tests such as next-generation sequencing (NGS) and integrate these with clinical and/or radiological characteristics to improve the predictive and prognostic power of traditional pathology approaches. One such example is with breast cancer. Diagnosis by histopathology has proven to be instrumental to guide breast cancer treatment, but new challenges have emerged as our increasing understanding of cancer has revealed its complex nature. As patient demand for personalized breast cancer therapy grows, we face an urgent need for more precise biomarker assessment and more accurate histopathologic breast cancer diagnosis to make better therapy decisions. The digitization of pathology data has opened the door to faster, more reproducible, and more precise diagnoses through computerized image analysis. Software to assist diagnostic breast pathology through image processing techniques have been around for years. But recent breakthroughs in AI promise to fundamentally change the way we detect and treat breast cancer in the near future.

Because of the direct acquisition of patient images in digital form for central archival and softcopy review, the radiology practice readily incorporates AI into the clinical environment. For patient images generated by different imaging modalities (e.g., positron emission tomography, magnetic resonance imaging, computed tomography, x‐ray, mammograms and ultrasound), AI potentially can be automated to pinpoint areas of interest and diagnosis, or even to check the quality of diagnostic or claim reports by payers or other care providers. AI is rapidly becoming an extremely promising aid in liver image tasks, leading to improved performance in detecting and evaluating liver lesions, facilitating liver clinical therapy, and predicting liver treatment response. Machine-assisted medical services will be the optimal solution for future liver medical care as AI can assist physicians to make more accurate and reproductive imaging diagnosis and greatly reduce the physicians’ workload. When evaluating liver biopsies, AI could be utilized in pathology to detect the pattern of injury and severity of injury based on common histological features characteristic to various disease states and guide clinical decision making. Simultaneously, the pathologist can focus his or her efforts on more problematic lesions with less distinctive patterns and features resulting in faster diagnosis for patients waiting on their biopsy results.

The potential of AI is limitless, and it can serve as a great boon to improving the speed of health care delivery in clinical practice such as instant diagnosis of cancer biopsies. AI software tools, if exploited and implemented well, have the possibility of handling laborious and mundane tasks (e.g., counting mitoses and screening for easily identifiable cancer types), simplifying complex tasks (e.g., ordering appropriate test upfront) and standardizing complex diagnoses (i.e. removing subjectivity from Gleason Scoring). In the context of the rapid development of AI technology, physicians must keep pace with the times and apply the incredible technology we have available to better and more quickly serve patients.

Built on the vision of better patient outcomes, Instapath was founded in 2017 by engineers and scientists to enable patients to immediately know their cancer diagnosis. Our team made it our mission to develop fast and easy digital pathology technology so diagnosis can be made in minutes instead of days. To learn more about Instapath and our technology, visit https://instapathbio.com/tech/ or contact us at info@instapathbio.com.

References

Jarrett D, Stride E, Vallis K, Gooding MJ. Applications and limitations of machine learning in radiation oncology. Br J Radiol. 2019;92(1100):20190001.

Kononenko I. Machine learning for medical diagnosis: history, state of the art and perspective. Artif. Intell. Med. 2001;23:89–109.

Wong STC. Is pathology prepared for the adoption of artificial intelligence? Cancer Cytopathol. 2018;126:373–375.

Zhou LQ, Wang JY, Yu SY, et al. Artificial intelligence in medical imaging of the liver. World J Gastroenterol. 2019;25(6):672–682.

Sarwar S, Dent A, Faust K, et al. Physician perspectives on integration of artificial intelligence into diagnostic pathology. NPJ Digit Med. 2019;2:28.

Robertson S, Azizpour H, Smith K, Hartman J. Digital image analysis in breast pathology-from image processing techniques to artificial intelligence. Transl. Res. 2018;194:19–35.

Tizhoosh HR, Pantanowitz L. Artificial Intelligence and Digital Pathology: Challenges and Opportunities. J Pathol Inform. 2018;9:38.

Mei Wang Mei Wang, Ph.D. | CEO at Instapath
Tags:  digital-pathology artificial-intelligence healthcare