A new direction to promote the implementation of artificial intelligence in natural clinical settings

Artificial intelligence (AI) researchers claim that they have made great `achievements' in clinical realms. However, clinicians point out the so-called `achievements' have no ability to implement into natural clinical settings. The root cause for this huge gap is that many essential features of natural clinical tasks are overlooked by AI system developers without medical background. In this paper, we propose that the clinical benchmark suite is a novel and promising direction to capture the essential features of the real-world clinical tasks, hence qualifies itself for guiding the development of AI systems, promoting the implementation of AI in real-world clinical practice.

[1]  Chris Anderson.  Clinicians Should Cast a Discerning Eye on AI , 2017 .

[2]  Isaac S Kohane,et al.  Artificial Intelligence in Healthcare , 2019, Artificial Intelligence and Machine Learning for Business for Non-Engineers.

[3]  J. Kvedar,et al.  Artificial intelligence powers digital medicine , 2018, npj Digital Medicine.

[4]  M. Abràmoff,et al.  Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices , 2018, npj Digital Medicine.

[5]  Mohammad Mansouri,et al.  An explainable deep-learning algorithm for the detection of acute intracranial haemorrhage from small datasets , 2018, Nature Biomedical Engineering.

[6]  Rayid Ghani,et al.  Machine learning and AI research for Patient Benefit: 20 Critical Questions on Transparency, Replicability, Ethics and Effectiveness , 2018, ArXiv.

[7]  Christopher Mabey,et al.  Computerised Interpretation of Fetal Heart Rate during Labour (INFANT): A Randomised Controlled Trial , 2017 .

[8]  Sebastian Thrun,et al.  Dermatologist-level classification of skin cancer with deep neural networks , 2017, Nature.

[9]  Igor Kononenko,et al.  Machine learning for medical diagnosis: history, state of the art and perspective , 2001, Artif. Intell. Medicine.

[10]  Daniel S. Kermany,et al.  Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning , 2018, Cell.

[11]  All eyes are on AI , 2018, Nature Biomedical Engineering.

[12]  Jonathan H. Chen,et al.  Machine Learning and Prediction in Medicine - Beyond the Peak of Inflated Expectations. , 2017, The New England journal of medicine.

[13]  Michael Mittelman,et al.  Patient commentary: Stop hyping artificial intelligence—patients will always need human doctors , 2018, British Medical Journal.

[14]  Philip J. Fleming,et al.  How not to lie with statistics: the correct way to summarize benchmark results , 1986, CACM.

[15]  Philip R. O. Payne,et al.  Questions for Artificial Intelligence in Health Care. , 2019, JAMA.

[16]  A. L. Baker,et al.  Performance of four computer-based diagnostic systems. , 1994, The New England journal of medicine.

[17]  Lei Wang,et al.  Landscape of Big Medical Data: A Pragmatic Survey on Prioritized Tasks , 2019, IEEE Access.

[18]  Ara Darzi,et al.  Preparing for precision medicine. , 2012, The New England journal of medicine.

[19]  Avshalom Caspi,et al.  All for One and One for All: Mental Disorders in One Dimension. , 2018, The American journal of psychiatry.

[20]  Trevor Mudge,et al.  MiBench: A free, commercially representative embedded benchmark suite , 2001 .

[21]  E. Brown,et al.  Artificial Intelligence in Medical Practice: The Question to the Answer? , 2017, The American journal of medicine.

[22]  N. Sohoni,et al.  Passive Detection of Atrial Fibrillation Using a Commercially Available Smartwatch , 2018, JAMA cardiology.

[23]  Marta Koch,et al.  Artificial Intelligence Is Becoming Natural , 2018, Cell.

[24]  Till Acker,et al.  DNA methylation-based classification of central nervous system tumours , 2018, Nature.

[25]  Yuqing Zhu,et al.  BigDataBench: A big data benchmark suite from internet services , 2014, 2014 IEEE 20th International Symposium on High Performance Computer Architecture (HPCA).

[26]  S. Park,et al.  Methodologic Guide for Evaluating Clinical Performance and Effect of Artificial Intelligence Technology for Medical Diagnosis and Prediction. , 2018, Radiology.

[27]  Jie Xu,et al.  The practical implementation of artificial intelligence technologies in medicine , 2019, Nature Medicine.

[28]  Carlos Guestrin,et al.  "Why Should I Trust You?": Explaining the Predictions of Any Classifier , 2016, ArXiv.

[29]  Richard A Scolyer,et al.  Computer-assisted diagnosis for skin cancer: have we been outsmarted? , 2017, The Lancet.

[30]  Jörg Goldhahn,et al.  Could artificial intelligence make doctors obsolete? , 2018, British Medical Journal.

[31]  Geraint Rees,et al.  Clinically applicable deep learning for diagnosis and referral in retinal disease , 2018, Nature Medicine.

[32]  Christopher M Haggerty,et al.  Predicting deterioration of ventricular function in patients with repaired tetralogy of Fallot using machine learning , 2018, European heart journal cardiovascular Imaging.

[33]  Eric J Topol,et al.  High-performance medicine: the convergence of human and artificial intelligence , 2019, Nature Medicine.

[34]  A. Adamson,et al.  Machine Learning and Health Care Disparities in Dermatology. , 2018, JAMA dermatology.

[35]  Masoumeh Haghpanahi,et al.  Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network , 2019, Nature Medicine.

[36]  Ziad Obermeyer,et al.  Regulation of predictive analytics in medicine , 2019, Science.

[37]  Bishal Gyawali,et al.  Does global oncology need artificial intelligence? , 2018, The Lancet. Oncology.

[38]  A. Mehrotra,et al.  Evaluation of Artificial Intelligence–Based Grading of Diabetic Retinopathy in Primary Care , 2018, JAMA network open.

[39]  M. Abràmoff,et al.  Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices , 2018, npj Digital Medicine.

[40]  Andreas Geiger,et al.  Are we ready for autonomous driving? The KITTI vision benchmark suite , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[41]  Zoubin Ghahramani,et al.  Probabilistic machine learning and artificial intelligence , 2015, Nature.

[42]  Andrew H. Beck,et al.  Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer , 2017, JAMA.

[43]  Subhashini Venugopalan,et al.  Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. , 2016, JAMA.