Artificial Intelligence in Medicine
Where do we see the highest impact?
With ever-increasing expectations for all healthcare sectors to deliver timely, fiscally responsible, high-quality health care, AI has the potential to have numerous impacts. Commercial AI healthcare platforms represent a multi-billion industry with many AI products ready for implementation to improve various tasks, including diagnostics, patient management, workflow and efficiency.
The four general areas with most significant AI impacts in healthcare are:
- Image analysis.
- Improved workflow and efficiency.
- Public health and epidemiology.
- Processing large volumes of patient and medical data.
Image analysis has seen the most AI health care applications. AI has shown potential in interpreting medical images, including pathology slides, radiographs of various kinds, retina and other eye scans, and photographs of skin lesions. Many studies have demonstrated that AI can interpret these images as accurately as or even better than experienced clinicians. Studies have suggested that AI interpretation of radiographs may better distinguish patients infected with COVID-19 from other causes of pneumonia. AI interpretation of pathology slides may detect specific genetic mutations not previously identified without additional molecular tests.
The second area in which AI can impact healthcare is improving workflow and efficiency. AI has improved surgery scheduling, saving significant revenue, and decreased patient wait times for appointments. AI can screen and triage radiographs, directing attention to critical patients. This use would be valuable in many busy clinical settings, such as the recent COVID-19. Similarly, AI can screen retina images to prioritize urgent conditions. AI has improved pathologists’ efficiency when used to detect breast metastases. Finally, AI may reduce medical errors, thereby ensuring patient safety.
A third healthcare benefit of AI is in public health and epidemiology. AI can assist with clinical decision-making and diagnoses in low-income countries and areas with limited health care resources and personnel. AI can improve the identification of infectious outbreaks, such as tuberculosis, malaria, dengue fever, and influenza. AI has been used to predict transmission patterns of the Zika virus and the current COVID-19 pandemic. Applications can stratify the risk of outbreaks based on multiple factors, including age, income, race, atypical geographic clusters, and seasonal factors like rainfall and temperature. AI has been used to assess morbidity and mortality, such as predicting disease severity with malaria and identifying treatment failures in tuberculosis.
AI also can dramatically impact healthcare due to processing large data sets or disconnected volumes of patient information, so-called big data. Much of patient information exists in written text: healthcare providers’ notes, laboratory and radiology reports, medication records, etc. Natural language processing (NLP) allows electronic records platforms to sort through extensive volumes of data on complex patients at rates much faster than human capability, which has great potential to assist with diagnosis and treatment decisions. Medical literature is being produced at rates that exceed our ability to digest it. NLP capabilities of AI have the potential to rapidly sort through this extensive medical literature and relate specific verbiage in patient records guiding therapy. AI can assess and compile far greater patient data and therapeutic options than would be feasible by individual clinicians, thus providing customized patient care.
Conclusion
The view that AI will dramatically impact healthcare in the coming years will likely prove true. However, much work is needed, primarily because of the scarcity of prospective clinical trials historically required in medical research. Any concern that AI will replace healthcare providers seems unwarranted. Early studies suggest that even AI programs that appear to exceed human interpretation perform best when working in cooperation with and oversight from clinicians. AI’s greatest potential seems to be its ability to augment care from health professionals, improve efficiency and accuracy, and should be anticipated with enthusiasm as the field moves forward exponentially.
This post is an abbreviated version of the review article that our group have recently published in Federal Practitioner.
Best wishes,
Andrew
