Establishing a second-generation artificial intelligence-based system for improving diagnosis, treatment, and monitoring of patients with rare diseases

Abstract

Patients with rare diseases are a major challenge for healthcare systems. These patients face three major obstacles: late diagnosis and misdiagnosis, lack of proper response to therapies, and absence of valid monitoring tools. We reviewed the relevant literature on first-generation artificial intelligence (AI) algorithms which were designed to improve the management of chronic diseases. The shortage of big data resources and the inability to provide patients with clinical value limit the use of these AI platforms by patients and physicians. In the present study, we reviewed the relevant literature on the obstacles encountered in the management of patients with rare diseases. Examples of currently available AI platforms are presented. The use of second-generation AI-based systems that are patient-tailored is presented. The system provides a means for early diagnosis and a method for improving the response to therapies based on clinically meaningful outcome parameters. The system may offer a patient-tailored monitoring tool that is based on parameters that are relevant to patients and caregivers and provides a clinically meaningful tool for follow-up. The system can provide an inclusive solution for patients with rare diseases and ensures adherence based on clinical responses. It has the potential advantage of not being dependent on large datasets and is a dynamic system that adapts to ongoing changes in patients' disease and response to therapy.

Artificial Intelligence in Medical Imaging and its Impact on the Rare Disease Community: Threats, Challenges and Opportunities

“…48 Inability to do so due to an underrepresentation of uncommon conditions can conceivably induce biases in the performance and evaluation of disease burden. 49 [51][52][53] The power of AI can be used to address these obstacles. As such, the implications of AI in health care are critical for RD diagnosis and management.…”

Next-Generation Personalized Medicine: Implementation of Variability Patterns for Overcoming Drug Resistance in Chronic Diseases

“…Recent studies described the development of a platform for overcoming drug resistance based on the following three phases [ 1 , 140 , 141 ]: introducing irregularity in therapeutic regimens for improving drug responsiveness; establishing a closed-loop algorithm for generating individualized patterns of irregularity to overcome drug resistance; upscaling the algorithm by implementing quantified personal variability patterns along with additional personalized signatures based on genes, proteins, and other disease or host relevant variables [ 1 , 25 , 43 , 68 , 76 , 85 , 98 , 99 , 100 , 101 , 142 , 143 , 144 , 145 , 146 , 147 , 148 , 149 , 150 , 151 ].…”

Deep Learning Paradigm for Cardiovascular Disease/Stroke Risk Stratification in Parkinson’s Disease Affected by COVID-19: A Narrative Review

“…To stratify an early CVD/stroke risk in PD patients embraced by the COVID-19 framework, AI is the most promising and optimal solution due to its ability to handle non-linearity during the training process [ 186 ]. The class of AI was first dominated by the ML systems consisting of a variety of applications, including diabetes [ 139 , 187 , 188 ], neonatology [ 189 ], genetics [ 190 , 191 ], coronary artery disease risk stratification [ 140 , 192 ], classification of carotid plaques [ 193 ], and cancer risk stratification in organs such as the thyroid [ 39 , 194 , 195 ], breast [ 196 ], ovary [ 142 , 197 ], and prostate [ 144 , 198 ], to name a few. These methods have generic drawbacks such as ad hoc feature extraction during the training/prediction design.…”