AI And The Future Of Healthcare: Transforming Diagnosis, Treatment, And Drug Discovery

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Myth Versus Reality in AI

There is a lot of hope that AI will be able to advance the healthcare sector in a variety of ways, not just for patient diagnosis, patient prognosis, drug discovery, but also to serve as an assistant for physician and provide a better and more personalized experience for patients. This hope has been fueled by some successful applications of AI in healthcare. Side-by-side however, there are unrealistic expectations of what AI can do and what the landscape of the healthcare industry will look like in the future.

Dr. Anthony Chang was one of 2019’s invited speakers for the Society for Artificial Intelligence in Medicine (AIME) conference held in Poznan, Poland, where he presented a lecture entitled: Common Misconceptions and Future Directions for AI in Medicine: A Physician-Data Scientist Perspective. Below we list two of the more common myths regarding the application of artificially intelligent systems in healthcare.

  1. While nobody can entirely predict the future, the fact is that physicians who understand the role of AI in healthcare will likely have an advantage in their career. For instance, the American College of Radiology (ACR) posted a job advertisement for a Radiologist:

    https://jobs.acr.org/job/radiologist-for-teleradiology- ai-practice/50217408/

    listing two requirements for the job:

  2. The use of AI in any field of study consists of many components and programming is just one of them. For the continued growth, development and success of AI applications in healthcare, physicians and data scientists need to continue collaboration to build meaningful AI systems. Physicians need to understand what AI is capable of achieving and need to evaluate how their role can be improved with AI. Physicians need to communicate this information to data scientists who can then build an AI system. The collaboration does not end here. Together physicians and data scientists must figure out what kind of data they have available to use for model training and, further, once the model is built its performance must be analyzed and interpreted, both of which require collaboration between physicians and data scientists. A further trend is the significant commoditization of AI software. For instance, today it is possible to use a visual tool (requiring no coding) to build a visual classifier. An example of such a tool is Teachable Machine by Google.