The Role of Artificial Intelligence in Personalized Medicine
Written by Nidhi Kulkarni
Personalized medicine involves customizing medical care to an individual's unique genomic and molecular characteristics, with Artificial Intelligence (AI) playing a crucial role in this advancement. The challenge lies in analyzing vast data to create tailored treatments. Integrating AI into personalized medicine requires adjustments in healthcare infrastructure. Upon patient arrival, their personal and clinical data (e.g., images, genetic data, blood pressure) are collected and input into the AI system with consent. The AI then uses this data to provide healthcare recommendations, aiding clinicians in decision-making. The outcomes of these recommendations, whether accurate or not, are recorded and used to refine the AI system's accuracy.
Personalized medicine will shift medical practices from reactive disease treatment to proactive healthcare management, including screening, early treatment, and prevention. This transformation will change the roles of both physicians and patients, increasing reliance on electronic medical records and decision support tools in an industry traditionally resistant to information technology.
Health information management (HIM) professionals, responsible for handling patient data, are crucial to this model, highlighting the importance of AI. A National Academy of Medicine report notes AI's potential to enhance specialist care, mitigate human error, and handle fatigue and inattention. While these technologies must be used cautiously, they offer significant promise. AI addresses the challenge of analyzing vast amounts of data to create personalized treatment plans, overcoming the limitations of traditional methods by identifying patterns and correlations to develop more accurate and effective treatments.
Doctors are increasingly using whole-genome sequencing to find genetic explanations for ailments undiagnosable by conventional methods, and other healthcare experts are analyzing genomic data. In December 2013, the FDA approved Illumina’s MiSeqDx, the first high-throughput genomic sequencer, marking a significant step in utilizing genetic data in healthcare by enabling new genome-based tests. The FDA and the National Institute of Standards and Technology developed genomic reference materials for performance evaluation.
AI in healthcare is applied in image analysis, medicine discovery, and diagnostics. Studies using data-intensive biomedical technology reveal significant variations in disease processes and treatment responses among individuals, indicating the need for personalized medications. AI, through its interaction with data-intensive assays, can help develop personalized medicine and identify effective intervention targets. The early 20th-century discovery of blood types for safer blood transfusions is an early example of personalized medicine.
However, challenges in using AI for personalized medicine include the need for vast high-quality data, potential bias in data analysis, and ethical concerns about privacy and security. Analyzing personal genetic data is complex due to large genomic datasets and the need for skilled analysts, databases, algorithms, software, and computing resources, which most clinicians lack. Even with access to excellent genomic analysis programs and powerful workstations, healthcare professionals often struggle to select the best tools and parameters due to insufficient training. Employing personal genomic information in customized medicine faces challenges in data accuracy and bias. High-throughput genomic technologies produce vast amounts of data that are difficult to manage and analyze, making data security and privacy crucial. Laws like GINA and tools such as Interpretome and GenePING help protect this sensitive information. Health Information Management (HIM) professionals, who manage and update genetic information, require specialized training to handle these evolving needs effectively.
To address challenges in personalized medicine, potential solutions include increasing collaboration among healthcare providers, implementing quality control measures, and developing ethical guidelines for AI. Advances in research have improved understanding of genetic changes and their impact on disease risk and treatment responses, but also complicate the assessment of individual genomic data. A robust security framework is needed to protect personal genomic data before its widespread clinical use. Healthcare professionals must educate the public on the benefits and risks of genetic testing, and software engineers should collaborate with providers to integrate genetic data into medical records. A national information infrastructure with set standards could aid this integration.
To manage the large volume of genomic data, solutions include using compression algorithms and maintaining a reference genome with recorded differences. Systems like openBIS manage DNA sequences from next-generation sequencing technologies. Health Information Management (HIM) training should cover managing, protecting, and applying genomic data in clinical settings. HIM professionals might also develop knowledge-based decision-making systems for personalized medicine.
In conclusion, the future of AI in personalized medicine is promising, with potential to revolutionize healthcare delivery. Future research should focus on developing more accurate AI algorithms, improving data quality and access, and addressing ethical and privacy concerns. Overcoming these challenges can greatly enhance patient outcomes and healthcare quality.
References:
Parekh, Ad-Duhaa E et al. “Artificial intelligence (AI) in personalized medicine: AI-generated personalized therapy regimens based on genetic and medical history: short communication.” Annals of medicine and surgery (2012) vol. 85,11 5831-5833. 13 Sep. 2023, doi:10.1097/MS9.0000000000001320
Written by Nidhi Kulkarni from MEDILOQUY