ARTIFICIAL INTELLIGENCE IN HEALTHCARE: TRANSFORMING DIAGNOSIS, TREATMENT, AND PATIENT CARE

Abstract

Artificial Intelligence (AI) is progressively changing healthcare in the modern era, including ways to identify, treat, and monitor disease. Greater access to medical data and advancements in machine learning allow us to extract timely and accurate insights from electronic health records, medical images and genomic data, as examples. One of the most obvious ways AI is making an impact is through diagnostics. Algorithms are being taught to detect cancers, heart disease and diseases of the nervous system early in the disease process, when humans may not see any signs, by drawing on large datasets. Clinical decision-support systems are also supporting clinicians by presenting complicated patient data in an organized manner providing evidence-based recommendations for management. The impact of AI has also been similar in areas of therapeutic medicine and drug discovery and development. In precision medicine, therapies develop on the patient's genetics or medical history. AI software is also applied in the automation of robotic-assisted surgeries and procedures that have been shown to be more accurate, sometimes performed with a smaller incision and have better recovery time. AI platforms can help improve the pharmaceutical discovery pipeline process by facilitating research initiatives while identifying the most viable compounds. AI is used to improve providing care beyond the patient-facing clinical spaces. Predictive platforms are being implemented in inpatient care environments to better understand and plan patient needs and more effective usage of gridspace. Virtual health assistants and telehealth platforms are being utilized to expand access to medical advice and treatment, a meaningful application in rural, under-resourced locations, and across the pandemic which began in 2020. Likewise, there are other impediments on the bottom line of AI. One of those impediments is protecting variable patient information to other stakeholders. Then, you also have the fairness component surrounding algorithms and the transparency in decision-making that accompanies automated decision-making (through AI). Certainly, there is still a barrier to having a broader perspective in adoption, if we simply do not reflect on the oversight of those algorithms and monitoring those algorithms. AI has the potential to change our health system completely, as we know it. We definitely should not lose sight of the opportunity we have regarding some of the dimensions of diagnosis, personalizing care, and possibly providing health care as we envision the future of health care. Ultimately, if AI ultimately succeeds or not depends on whether we are all ethical or responsible within our application of AI in practice.

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