Promises and hazards of artificial intelligence

Abstract

AbstractArtificial intelligence (AI)‐based technologies employing deep‐learning (DL) approaches have proven effective in supporting decisions in many medical specialties, including radiology, cardiology, oncology, dermatology, ophthalmology, and others. For example, AI/DL algorithms have been shown to reduce waiting times, improve medication adherence, customize insulin dosages, and help interpret magnetic resonance images. The number of AI life‐science papers listed in PubMed increased from 596 in 2010 to 12 422 in 2019. The number of papers on the use of AI in the field of ophthalmology also has increased dramatically. Moreover, human cognitive capacity to effectively manage information is often exceeded by the quantity of data generated, and unlike humans, who have bad days and emotions, and who get tired, with subsequent decreases in performance and accuracy, AI works 24/7 without vacations or complaints. AI/DL algorithms have been used to detect diseases based on image analysis, with fundus photos and optical coherence tomography (OCT) scans analysed for retinal diseases, chest radiographs assessed for lung diseases, and skin photos analysed for skin disorders. Retinal photos have also been used to identify risk factors related to cardiovascular disorders, including blood pressure, smoking, and body mass index.One of many challenges in the field of AI is determining what constitutes evidence of impact and benefit for AI medical devices and who should assess the evidence. The majority of AI studies are conducted in experimental conditions and based on preselected data. They might provide inadequate insight into the use of AI applications in heterogeneous, real‐world care settings. One of the potential hazards of the clinical use of algorithms identified was the risk of applying an algorithm trained on a particular demographic group to a population that differs in factors such as ethnicity, age, and sex. Moreover, many studies of algorithms developed with AI have excluded low‐quality images, treating them as ungradable images, and patients with comorbid eye diseases, making them less reflective of real‐world conditions. The future development of ophthalmology depends on better and possibly unlimited access to the medical data stored within electronic health records. However, this access cannot be allowed to compromise of privacy of this very sensitive data. There is a need for effective regulations that will set a balance between individual protection and the common good.A recent report from the National Academy of Medicine highlights some important challenges in the further development of AI applications in health care. The authors advocate the use of openly accessible, standardized, population‐representative data; addressing explicit and implicit biases related to AI; developing and deploying appropriate training and educational programs for health workers to support health‐care AI; and balancing innovation and safety through the use of regulation and legislation to promote trust.