AI In Healthcare: How Self-Learning Tools Impact Medicine
The effects of the artificial intelligence boom can be seen in just about every walk of modern life. While AI’s applications are already ubiquitous, the medical field stands to benefit the most from it. Self-learning tools have incredible potential to advance medical research and patient care. While these tools come with a whole slew of considerations (like the quality of data used to train models), they have already found a plethora of uses in various medical fields. In this article, we’ll review how AI impacts healthcare at large, from diagnosis and treatment to prevention.
Why AI Tools In Medicine Are Approached With Caution
While neural networks with a library of information behind them can exhibit attention to detail that human doctors simply can’t match, they don’t come without downsides. The commercially available AI – such as ChatGPT and its derivatives – often make ridiculous mistakes and even go as far as to make up information.
This machine learning algorithm’s goal is to satisfy its user – and it may “hallucinate” data to achieve that. Developers of AI solutions place disclaimers on their products – “The AI can make mistakes. Check important info.”
With that in mind, the skepticism displayed towards AI by many medical professionals is clear. If a doctor were known for giving bogus info, their patients would take their business elsewhere. All the worse, if the doctor was not a doctor at all but a program. After all, human empathy is important in diagnosing problems and providing treatment.
Another massive shortcoming for medical AI is liability. If a medical AI makes a mistake, who is liable? Who is responsible? Such issues in efficiency, morality, and legality often leave medical professionals preferring the old, tested methods.
Introducing AI to Medicine Safely
The healthcare industry is one of the main beneficiaries of cutting-edge technology. However, none of the advanced machines, from MRI to robotic surgery, have ever replaced a doctor. In fact, they don’t have to—they are just another implement in a medical professional’s toolbox. It would be disingenuous to treat AI models any differently! They are instruments that help a doctor provide higher-quality care for the patient, and a researcher to find new advances in medicine.
Medical AI assistants excel at handling big data. Their capability to sift through massive databases like patient records and lab results makes routine patient care easy. But AI isn’t limited to just handling databases. Medical AI assistants have found intensive use in simplifying routine care, assisting in diagnosis and prognosis, and refining the capabilities of robot-assisted surgery. As the technology becomes more and more refined, it has demonstrated the potential to handle even more complex tasks related to healthcare.
Applications of AI in Medicine
AI has an impressively broad range of practical applications in healthcare. Existing AI tools can help prevent, diagnose, and plan treatment across a wide variety of fields, including imaging, surgery, research, and chronic disease management. Let’s review some of the most prominent examples of AI usage.
AI In Medical Imaging
Recent advances in image-decoding AI greenlit the development of tools like Zebra Medical Vision, AIdoc, PathAI, and others. These tools can analyze vast amounts of medical images, such as X-rays, CT scans, and MRIs. The AI then flags any potential abnormalities (injuries, fractures, tumors, etc.) and reduces the time required for an accurate diagnosis. These tools are widely used in hospitals internationally, including massive for-profit hospitals like HCA Healthcare.
AI in Surgery
AI tools like Proprio make real-time 3D reconstruction of a patient’s anatomy, massively assisting surgeons in planning complex surgery procedures. The AI can then directly assist surgeons even further during the surgery. The Da Vinci Robotic Surgical system, for example, integrates neural networks that virtually guide the surgeon, reduce hand tremors, and minimize user errors. These systems are widely used – Da Vinci SP is estimated to be involved in over 200,000 surgeries a year. Leading industry members such as UW Medicine use such tools.
AI in Mental Health
Neural networks like Wysa pose as automatic personal mental health conversation agents. Although their efficacy was initially met with skepticism due to reasons we discussed in the first section, it has since successfully passed multiple peer-reviewed clinical trials.
Wysa received the FDA Breakthrough Device Designation in 2022 after a trial found it comparable to in-person psychological counseling. Its potential allows it to be used by medical professionals as a standalone helper or supplementary tool.
Wysa and similar generative AI even have the potential to prevent onsets and relapses in mental disorders. Wysa’s chatbot is available 24/7 on any device (with an Internet connection), so it could potentially be integrated into high-risk sectors.
For example, online betting sites could integrate Wysa into their platforms to ensure responsible gambling and mental health guidelines. Wysa could then interact with the gambler through chat, advising against dangerous behaviors and reckless spending. Widespread access to Wysa could make online gambling safer and less addictive.
Summing Up AI In Healthcare
The usage of AI in the medical field comes with some ethical and moral considerations. When used as a tool and not a substitute for medical professionals, though, it becomes a great helper for both the doctor and the patient. AI tools greatly enhance diagnostics, help with administrative tasks, and improve awareness of mental health.
Mentally help-focused AI chatbots such as Wysa even passed clinical trials that marked their support as comparable to in-person therapy. As this technology evolves, it might be possible to integrate AI technology into regular life in such a way that it eliminates the adverse effects of common dangerous activities.