The impact of artificial intelligence in medicine on the future role of the physician

The practice of medicine is changing with the development of new Artificial Intelligence (AI) methods of machine learning. Coupled with rapid improvements in computer processing, these AI-based systems are already improving the accuracy and efficiency of diagnosis and treatment across various specializations. The increasing focus of AI in radiology has led to some experts suggesting that someday AI may even replace radiologists. These suggestions raise the question of whether AI-based systems will eventually replace physicians in some specializations or will augment the role of physicians without actually replacing them. To assess the impact on physicians this research seeks to better understand this technology and how it is transforming medicine. To that end this paper researches the role of AI-based systems in performing medical work in specializations including radiology, pathology, ophthalmology, and cardiology. It concludes that AI-based systems will augment physicians and are unlikely to replace the traditional physician–patient relationship.

[1]  Diana S. M. Buist,et al.  Will Machine Learning Tip the Balance in Breast Cancer Screening? , 2017, JAMA oncology.

[2]  Charlene Liew The future of radiology augmented with Artificial Intelligence: A strategy for success. , 2018, European journal of radiology.

[3]  Nils J. Nilsson,et al.  Artificial Intelligence , 1974, IFIP Congress.

[4]  Peter Szolovits,et al.  The coming of age of artificial intelligence in medicine , 2009, Artif. Intell. Medicine.

[5]  Alain Saad,et al.  Validation of an Objective Keratoconus Detection System Implemented in a Scheimpflug Tomographer and Comparison With Other Methods , 2017, Cornea.

[6]  Sejong Oh,et al.  Development of machine learning models for diagnosis of glaucoma , 2017, PloS one.

[7]  Mary K. Pratt,et al.  Artificial intelligence in primary care , 2018 .

[8]  Nehmat Houssami,et al.  Artificial intelligence for breast cancer screening: Opportunity or hype? , 2017, Breast.

[9]  Jiangtao Cui,et al.  Localization and diagnosis framework for pediatric cataracts based on slit-lamp images using deep features of a convolutional neural network , 2017, PloS one.

[10]  Bernard F King,et al.  Artificial Intelligence and Radiology: What Will the Future Hold? , 2018, Journal of the American College of Radiology : JACR.

[11]  A. Yuille,et al.  Deep Learning in Radiology: Now the Real Work Begins. , 2018, Journal of the American College of Radiology : JACR.

[12]  Paul Nagy,et al.  Big Data and Machine Learning-Strategies for Driving This Bus: A Summary of the 2016 Intersociety Summer Conference. , 2017, Journal of the American College of Radiology : JACR.

[13]  J. Bisley,et al.  Evaluating tactile feedback in robotic surgery for potential clinical application using an animal model , 2016, Surgical Endoscopy.

[14]  Abhimanyu S. Ahuja,et al.  Understanding the advent of artificial intelligence in ophthalmology , 2019, Journal of current ophthalmology.

[15]  Fangyuan Zhao,et al.  Feasibility of Reidentifying Individuals in Large National Physical Activity Data Sets From Which Protected Health Information Has Been Removed With Use of Machine Learning , 2018, JAMA network open.

[16]  J. Kai,et al.  Can machine-learning improve cardiovascular risk prediction using routine clinical data? , 2017, PloS one.

[17]  C. Krittanawong,et al.  The rise of artificial intelligence and the uncertain future for physicians. , 2017, European journal of internal medicine.

[18]  P. Lakhani,et al.  Deep Learning at Chest Radiography: Automated Classification of Pulmonary Tuberculosis by Using Convolutional Neural Networks. , 2017, Radiology.

[19]  R Nick Bryan,et al.  Artificial Intelligence: Threat or Boon to Radiologists? , 2017, Journal of the American College of Radiology : JACR.

[20]  Bianca S. Gerendas,et al.  Fully Automated Detection and Quantification of Macular Fluid in OCT Using Deep Learning. , 2017, Ophthalmology.

[21]  Amir Sadeghipour,et al.  Artificial intelligence in retina , 2018, Progress in Retinal and Eye Research.

[22]  Ronald M. Summers,et al.  Deep Learning in Medical Imaging: Overview and Future Promise of an Exciting New Technique , 2016 .

[23]  E H Shortliffe,et al.  Watson for Oncology and breast cancer treatment recommendations: agreement with an expert multidisciplinary tumor board , 2018, Annals of oncology : official journal of the European Society for Medical Oncology.

[24]  Nico Karssemeijer,et al.  Large scale deep learning for computer aided detection of mammographic lesions , 2017, Medical Image Anal..

[25]  Daniel L. Rubin,et al.  Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions , 2017, Journal of Digital Imaging.

[26]  Daniel Rueckert,et al.  Machine Learning of Three-dimensional Right Ventricular Motion Enables Outcome Prediction in Pulmonary Hypertension: A Cardiac MR Imaging Study , 2017, Radiology.