AI has the ability to change the landscape of modern medicine, and take some of the burden off of physicians, if used correctly.
by Alex Tate
AI can improve all aspects related to diagnostics of a practice easily. They can improve and assess results more practically and feasibly for practices and track disease and treatment response more accurately. This will assess and measure patient outcomes, and can free up doctor’s time and help with burnout. If the change is resisted it is because roles of clinicians will change.
AI can not only further digital diagnostics, but create new workflows that will change the current pace of diagnoses, prognoses and medical documentation. With accurate results, the less room for human error, and churning out a faster diagnosis can mean treating patients with care and better facilities. You can imagine why this may be met with enthusiasm for anyone in a white coat.
What will AI do in diagnostics?
- Increase lab report results.
- Overcome the current shortage of trained lab workers.
- Give accurate diagnosis in real time, without having to go through recurrent tests to rule out different diseases, illnesses, and diagnosis.
- Catch symptoms faster
- Cut down physician’s time spent looking at medical tests, scans, and spend more time treating a patient based on AI diagnosis.
AI is known to make significant contributions in radiology and pathology diagnostics. 75% of physicians are excited about advancements according to a research published in Digital Medicine (A. D. Shihab Sarwar, 2019). AI is beneficial for workflow efficiency and quality assurance in pathology. In the same research many physicians were welcoming towards training and other implications before AI can be used wholly in a practice. Pathology and radiology are image-focused and diagnostic-focused, and constant improvements in the computational algorithms for these specifications have been developed and powered for best outcomes. AI carries the potential to transform the clinical practice of physicians. In Pathology, AI diagnostics may perform image analysis for tissue histology, analysis of molecular outputs and predict the prognosis accurately.
Medical diagnostics are a category of medical tests designed to detect infections, conditions and diseases. AI is playing an integral role in the evolution of the field of medical diagnostics. Pathologists manually go through all blood types for diagnosis. AI in medical diagnostics is still a relatively new approach. Clinicians need convincing about how reliable, sensitive, and integrated it is in diagnostics. Why is that? A lot of reading, rigorous testing, and attention to detail is required in medical diagnostics. A mistake can be fatal. AI applications are created with precise computational algorithms that can effectively produce diagnostics in clinical practices.
Some clinicians are worried about what it means to them if a machine can read blood tests. It is likely to do a lot more. AI using neural networks can train diagnostic machines to understand the image. Pooled data is the way forward. Machine learning ensure that the machine picks up more information through pattern recognition.
AI can cut down time a physician spends on an EHR. Digital diagnostics can increase physician’s “pajama time” (late evening time spent with family or otherwise) if it performs all tasks. It also leads to a standardization in how data is interpreted by monitoring all lab information in real time. How does this do that? According to Mark Benjamin, the CEO of Nuance, the job of AI is not just to transfer voice text into written text, but it should also have the means to decipher text. That is the added advantage of Conversational AI. He explicates the four uses of healthcare industry; an evident improvement in a physicians’ life, enhanced quality of care and a discernibly healthier population, and a diminishing healthcare cost. Without technology (and AI), these goals cannot be actualized.
Physicians’ enthusiasm for improvements in AI functions in healthcare means that all manual readings and errors will visibly decrease. Saving possible time, lab use, money and easing compliance with government regulations is favorable to them. Virtual assistants are no longer a thing seen in Sci-Fi movies either. There are fast advances in AI. Every physician might be able to have their own “Jarvis” like Iron Man. Their job will be to save patients’ lives, like Tony Stark saves the planet. You don’t even have to imagine a “Jarvis” that is yours; the question is when will AI be yours.
Strong AI is still in the experimental stage. Documentation via voice recognition, and conversational AI prevents physician burnout and prevents it from relapsing. Clinical documentation will soon be able to write for itself, and have enough machine learning to predict text, symptoms and even diagnostics intelligently to cut further documentation time and perform data retrieval from EHRs quickly. AI is a workforce productivity tool and should be used as such to reduce time-intensive workflows. AI is also supposed to provide insights through Predictive and Prescriptive Analytics. Machine learning and Deep Learning (a distinct usage of machine learning) are method to achieve Artificial Intelligence.
Currently, physicians spend more time with their EHR charts than with patients. Even AI is no substitute for human touch or eye contact between caregiver and receiver. Medicine is an evidence-based profession and is rigid to change unless it does not see vast improvements in caregiving.
AI improves operations, with automating scheduling and billing. Clinical outcomes and decisions will still lie with the practice managers. Clinicians also like to see evidence before they believe how impactful AI is in their practice without taking away control. The idea for using AI in healthcare has never been to replace doctors, but rather to bring about support to health care delivery.
If AI can assist in diagnostics, robotic assisted surgery is not so far away. AI will be able to perform these tasks and many more with time. The only question is when. mHealth is joining in, and AI researchers say that it is only a matter of time until HQ mobile phone cameras will be able to understand images and send them to a database for further consideration.
Alex Tate is a Healthcare IT Researcher and freelance writer who focus various engaging and informative topics related to the health IT industry. He loves to research and write about topics such as Affordable Care Act, EHR, revenue cycle management, privacy and security of patient health data. You can reach him via email
Find out more about her company here: https://oxfordhousetherapy.com/
Disclaimer: The viewpoint expressed in this article is the opinion of the author and is not necessarily the viewpoint of the owners or employees at Healthcare Staffing Innovations, LLC.