Artificial Intelligence (AI) is poised to revolutionize health care, providing countless opportunities to improve patient care, optimize operations and accelerate medical research. Or the potentials of AI in healthcare, from increasing accuracy in how we acquire windy up with full duty docs to customize our treatments? At the same time, these opportunities bring with them great challenges in areas such as ethics, data privacy, and regulatory frameworks. This article discusses about what role AI plays in the field of healthcare, benefits of using AI in the field of healthcare and the challenges to be overcome in order to leverage the complete potential of AI.
The Opportunities That AI Is Presenting We Will Have In Healthcare
Better diagnostics and earlier detection-Role of AI in Healthcare
AI can process a large volume of medical data rapidly and correctly, resulting in better diagnostics and faster disease detection. Medical images like X-rays, MRIs, and CT scans are stored and analyzed using machine learning algorithms capable of finding abnormalities in images that may not be observed by the human eye. AI-based diagnostic applications — for example — detection of diseases such as cancer, cardiovascular diseases and diabetic retinopathy have been found to be more sensitive and specific at the right time, which is important as most of the time early the patient is diagnosed, better is the outcome of treatment.
Example: AI in Radiology-Role of AI in Healthcare
An important use case for AI in diagnostics is the field of radiology. AI algorithms can detect patterns in medical images that are related to specific conditions (e.g., tumors or fractures). They’re trained on enormous datasets containing images that are labelled, and will have learned how to identify subtle differences that might indicate that a disease is present. AI, in some instances, has accurately diagnosed at least as well as, and in many cases better than the human radiologist.
Personalized Medicine-Role of AI in Healthcare
Personalised medicine is the tailoring of medical treatment to the individual characteristics of each patient. AI is very critical in here where it analyses genetic makeup, pathology, life style data and few more of a specific patient to find the best treatment. This approach could be more beneficial
Implantation of AI Medics also face several hindrances
But even to the tiniest, most niggling, most ethically and legally fraught concept — a misguided concept.
The introduction of AI in healthcare faces many challenges and ethical and legal implications of AI is one of them. AI enters the decision-making table of many sectors, creating novel challenges around accountability, transparency, and bias9. If, say, an AI system misdiagnoses a patient it will be hard to decide who the blame falls on — the developer, the health care provider or the AI system? In addition, AI algorithms have the potential to exacerbate already present biases in the data that they have been trained on which can consequently lead to differential effects across heterogeneous patient populations.
Addressing Bias in AI
We need to ensure the datasets included in AI algorithm training are well-diversify and accurately define the ecosystem so bias does not complicate the decision-making process. As a result, monitoring and auditing AI systems should happen continuously to identify and address biases as they arise. The onus is on us to develop codes or regulatory frameworks to regulate AI and protect any interests that belong to the patient.
Data Privacy and Security
You do not have to spend much time on the physician use data of healthcare AI because it is positively flooded. But, it also gives rise to issues about privacy and security of data. As the data is sensitive in nature, it is essential to protect patient data from unauthorized access and breaches. Patients should be informed on the usage of their data, and given an option to opt out, if desired.
Scenario 1: Data Privacy Protection
Strong data encryption techniques, access controls, and other measures should be employed by healthcare organizations to keep patient data safe. Also, it has to be transparent — patients need awareness about how their data is being used, and most importantly — why their data needs to be shared for AI-determain
Example: Seamless Integration
To ensure better integration, health care organizations will need to partner with AI vendors to ensure that the AI tools are built to integrate into existing systems. They should receive trained programs that outline how a healthcare professional is supposed to utilize the AI tools and how the information from AI operates. A pilot can also function as a pilot sketch for testing or trialing the AI tools on a smaller scale.
Regulatory Challenges
The regulatory environment for AI in healthcare is still uncertain. As AI technology evolves, regulators will have to develop new standards and metrics to ensure safety, quality and appropriate use in healthcare. That’s including guidelines for how to authorize medical devices, and even software that uses artificial intelligence, and being transparent ways how we can monitor and adapt these systems in the longterm.
Example: Regulatory Oversight
Regulatory agencies writing playbooks for determining the safety of software-based or AI-enabled medical devices, including (but not limited to) the U.S. Food and Drug Administration (FDA). Those frameworks risk safer but effective innovative AI tools. While AI technologies are complex and poorly defined, these two evolutions of regulation and technology are inherently disparate, which will likely require ongoing collaboration between the regulators, healthcare and AI developers.
Conclusion
Having the colossal potential to change the healthcare sector, there are infinite opportunities that AI brings forth to improve diagnosis, personalize treatment, treat patients in minutes in a remote manner, delight patients in less than 15 minutes, discovering drugs faster and cheaper than traditional medical science, and also, optimizing healthcare operations. However, realizing the full promise of AI in health care requires overcoming significant barriers such as ethics, privacy, integration, and regulation. Provided that one navigates through these obstacles or hardships carefully and