The medical industry has taken long strides into bettering its diagnosis, and one such step was implementing artificial intelligence. Artificially intelligent software will be able to diagnose symptoms a lot faster and more accurately.
For instance, if you are facing the preliminary symptoms of skin cancers, AI software will be recognized faster and more efficiently than a certified dermatologist. In addition to that, such a program only requires a training data set, whereas being an accredited dermatologist will have to go through a decade of expensive medical education.
While it may look like such technology will hinder the requirement of physicians, there is more to it than shown on the surface.
How Has AI-Powered Programs Benefitted the Medical Industry?
AI software often includes natural language processing, robotics and machine learning which makes it applicable in almost any medicinal field. It plays an important role in medical education, delivery of health care and biomedical research. Given its ability to learn and integrate extensive collections of clinical data, Ai can help with:
- Decision making
- Personalized medication
For instance, AI-based algorithms used by Benoit Morin are getting used to assist in the diagnosis of breast cancer. This is a great way for radiologists to get a second opinion.
On top of that, virtual human avatars can also indulge in human interaction that can help in both recognizing and treating psychiatric diseases. AI-integrated systems have also made their way into the physical realm with robotic prosthetics and mobile manipulators helping with timely telemedicine delivery.
That being said, powerful technology also comes with its ethical limitations. These challenges should immediately be addressed and bettered since AI technology can pose an immense threat to a patient’s safety, preference, and even privacy.
Application of AI in Health Research
The data that you have created for electronic health records plays an important role in health research. This data can be quite difficult to use if the database and system do not refrain from spreading low-quality data. Nevertheless, AI has a great role to play in quality improvement, health care optimization and scientific study.
Before you choose the usual path of guideline formation and scientific study, using AI that is trained with ample data can help discover the best clinical practices from the data provided. By evaluating these clinical trends, you can develop better clinical practice models for healthcare delivery.
Application of AI in Drug Development
Users are expecting that AI will simplify and enhance drug development in the near future. It can covert drug discovery from labour-intensive, data-intensive and capital-intensive processes. It does this by using models of genetic targets, organs, drugs, robotics, diseases and their stages of progress and pharmacokinetics.
AI helps make the process more efficient and cost-effective. Much like with any other drug study, recognizing the lead molecule does not mean a safe product is created; it is the same with AI. However, with such programs, you will be better suited to find better alternatives and quicken the process.
What Issues Are Yet To Be Addressed?
Since the medical industry is fast-paced and directly deals with patients, a lot of the ethical issues that you might face with Ai-powered technology have already been mitigated. However, there are still some that are yet to be noticed.
For starters, the right balance between the risks and benefits of AI technology. The main benefit of shifting to AI technology is that it enhances health care delivery as well as the quality of patient care.
Having said that, it is also important to minimize the ethical risks of implementing such new technology, some being confidentiality, patient autonomy and informed consent. You also need to consider how you can properly integrate AI into clinical practice.
The second major concern revolves around how AI can play a role in changing medical education. This deals with both preparing physicians for a career that uses AI and directly using such technology in the education of students. Since AI systems are not going anywhere soon, it only makes sense to help the future generation of physicians to be well accustomed to it and know all the functions.
Lastly, the legal and health policies that conflict with the rise of AI use in health care are a serious concern. Legal issues like product liability and malpractice come with using ‘black box algorithms.
This is mainly because the users give a logical explanation of how the final output was derived. In addition to that, patients might have to use face recognition technology. This can further threaten informed consent, data security and incidental findings.
In short, to achieve the full potential of AI in healthcare, four major ethical issues have to be solved. Namely, informed consent to use the data provided, transparency and safety, data privacy and algorithm biases.
That said, this is a very interesting time in the medical industry as AI has also brought with it several benefits.