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As Healthcare AI Advances, How Do we Balance the Benefits With Privacy Concerns?by@emmaakin
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As Healthcare AI Advances, How Do we Balance the Benefits With Privacy Concerns?

by Emmanuel Akin-AdemolaMay 29th, 2024
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Artificial intelligence (AI) in healthcare has the potential to revolutionize clinical research and care delivery. AI can be used to sift through massive databases and may have applications in mental healthcare and cancer detection. The concerns are in two categories. The first concern is the privacy of the data used to train these models, second is the model's response based on the input of the patient’s data.
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With an alarming 46 percent statistical concern about access to treatment in the healthcare system around the world, AI is seeking to democratize and simplify the process of diagnosis and patient treatment. With companies like GoogleGE HealthcareSiemens Healthineers, and many other leading AI startups, these AI dreams are achieving more feasibility.


However, how will AI’s health solution be future-proof if privacy concerns and data breaches are still lurking?


Overview of AI advancements


GE Healthcare has been able to develop a technology that assists with ultra-sound imaging, and high-level accurate scans like PET, MRI, and CT. it is also in the U.S. Food and Drug Administration-approved AI medical devices having 58 out of 692 entries.


Siemens Healthineers claims to have more than 80 AI-based healthcare solutions with 40 hardware devices approved lately. The focus of this company is the algorithmic mechanism that automates the post-processing of images and helps radiologists handle heavy workflows and routine tasks.


According to an article on VKTR by Neil Savage, “it includes modules for chest CT, which can highlight lung nodules and estimate tumor burden; brain MR, which divides the brain into different segments and makes volumetric measurements”


host of other startups are promising in harnessing the potential of the intelligent models in addressing the human problems with the current healthcare system.


Can we trust AI with our healthcare data?


AI by default makes decisions and recommendations based on the data it has been trained with and all forms have an underlying principle of large datasets in finding patterns. This leads us to our first question on how the benefits be balanced with the disadvantages in utilizing people’s healthcare data for training and decisions.


The concerns are in two categories. The first concern is the privacy of the data used to train these models, second is the model’s response based on the input of the patient’s data in the working process—or decision-making process of the model.


AI’s advancements in the healthcare venture have received ovations with its impressive abilities ranging from medical evaluations, automation, chest x-rays, smart wearables, medical diagnosis, imaging processing, and robotic surgery—which has been touted as a hypothetical possibility with recent innovations and on a higher level rather than the current level with tiny incisions.


In Health IT security’s words, “Artificial intelligence (AI) in healthcare has the potential to revolutionize clinical research and care delivery. AI can be used to sift through massive databases and may have applications in mental healthcare and cancer detection.”


Over time, the medical profession has prioritized the oath of secrecy in the medical process of diagnosis, treatments, and prescriptions. However, a data-oriented system solving the measure of traditional problems while maintaining the status quo remains a vital cornerstone to the utility of this innovation.


Definitive Healthcare has identified about 80 percent of health data breaches due to hacking or IT incidents. Therefore, there’s still a lot to be addressed when fears abound about the possibility of weaponized models, poisoning, and other sort of invasive possibilities on the technology that was originally meant to assist and automate tasks.


This compounds the problems and leaves gaps as the healthcare AI application accelerates at an unprecedented pace. Several approaches may mitigate problems and tie up the loose ends which will be detailed in succeeding paragraphs.


Looking forward: The turning point and AI promises


A technical approach such as anonymizing, encryption, and creating privacy-oriented algorithms in storing and retrieval of health care data. An example is blockchain technology detailed in this paper by the NLM.


A second way can be developing legal frameworks in protecting the medical records of individuals and according to Valentin Kuzmenco, in an article on Hyperight, “We need robust legal frameworks to safeguard our privacy in the digital age. Stringent data protection laws like GDPR and CCPA offer a starting point,… protecting our data from unauthorized access and misuse, regardless of geographical borders.”


There’s a longtime perpetual concern of big tech with people’s data and their collateral cost of internet surfing, and recommendation algorithms. Patients can be educated on best privacy practices, and providing consent forms—because not all forms of medical data may be deemed private


Thirdly, there is ongoing research in addressing the underlying concerns and a continual progression will address technological challenges with data privacy and automative algorithms. Being on the lookout while supervising automation for progressive steps will go a long way.


In conclusion, the impact of AI in the healthcare industry is promising and has lots of potential to mitigate current and future healthcare problems and automate tasks to lower costs and democratize healthcare. However, best security practices, compliance regulatory frameworks, and continual research would enhance a futuristic possibility with innovations and advancements.