Transforming Healthcare through Artificial Intelligence & Robotics
Over the past few decades, Artificial Intelligence (AI) has played a vigorous role in transforming our world. Especially today, AI presents itself in many different forms and influences our daily life. It is incontestable, that the potential of AI and robotics is nearly endless, opening up new doors for a vast array of industries. Inevitably, Healthcare is one of the latter. Technology allows for more accurate, systematic, and impactful interventions in patient care. Artificial Intelligence is expected to drive the changes and improvements towards healthcare excellence, by truly augmenting human activity.
Applications and Benefits of AI and Robotics in Healthcare
AI has become extremely sophisticated at doing what humans have been traditionally doing. What’s more, it’s incomparably time-efficient and cost-effective. It lays the ground for proactive health management and encourages an appropriate lifestyle for well-being. Although the applications of AI and Robotics in healthcare are vast, we list just a few of them below:
- Self-Care, Prevention, and Healthy Lifestyle – Contemporary patients are becoming increasingly empowered to take care of their health and well-being. Starting from numerous healthcare applications all the way to sleep trackers and blood pressure measurers, AI-based solutions are able to provide personalized plans and guidance. They can track and process greater amounts of data and gain insights into care processes.
- Improving Healthcare Operations – With the Help of AI, operations such as scheduling, hospital admissions, discharge, and capacity management become simplified.
- Diagnosis – Many AI-based technologies can help triage patients and detect whether the symptoms present require additional attention. In turn, it creates a positive trend for the patients to appear at hospitals and emergency rooms only when essential.
- Early Detection and Diagnostics – AI solutions suggest effective ways for improving early detection mechanisms and timely diagnosis along with its accuracy. This is due to strengthened pattern recognition abilities that can have significant clinical implications.
- Decision-Making Support – AI can present a patient’s medical information in a well-structured way, that allows healthcare professionals to select the appropriate treatment option. Such facilitation results in a time-effective and evidence-based decision-making process.
- Care Assistance – Contemporary robots and machines can refill stocks, take appropriate notes, and retrieve medical records if needed. These technologies can as well be of great help while monitoring the patient’s condition.
- Innovation Support – along with the clinical practice, AI can successfully support research and development activities.
- Expanding access to healthcare – AI and Robotics could help fill the shortages of healthcare workers, especially in underserved or developing regions. E.g. AI imaging tools can screen chest x-rays for signs of tuberculosis, often achieving a level of accuracy comparable to humans.
- Lowering the Burden of Record Use – AI can facilitate the process of receiving routine requests from the inbox, including medication refills and result notifications. It can help prioritize tasks that truly require the clinician’s attention, allowing the users to work through their to-do lists. In addition, Voice recognition and dictation can accelerate the clinical documentation process.
- Prediction and Management of Patient Risks – AI can extract and analyze the abundance of patient data in an accurate, timely, and reliable manner. This makes patient risk assessment and management way easier.
What are the challenges with AI applications in Healthcare?
The main challenge with AI application in healthcare is reliable and relevant data. AI solutions are just as good as the data they base decisions on. Typically, in healthcare, the data is fragmented across various systems and in multiple formats. Such a fragmentation can increase the risk of errors and decreases the comprehensiveness of the datasets. In addition, AI systems might become biased on the data they are being trained on.
Furthermore, there are ongoing privacy concerns related to AI incorporation in the healthcare industry. Some patients have worries regarding their privacy being violated. This assumption becomes even more sizeable due to AI’s ability to predict the private information of the patient without the algorithm ever having received it.
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