When you get to the waiting room, are you still waiting for the nurse to take your medical chart? Or do you just stick it in the machine, pick a reason for your visit, and sit down before your turn is called?
And does your doctor already have a chat room where you can ask questions instead of a lengthy visit?
All these things are already commonplace in some places. Let's take a look at how AI is helping in healthcare.
The origins of AI in healthcare
AI first appeared in healthcare in the 1960s. For example, the first AI medical consultant was called INTERNIST-1 and has been in use since 1971. It applied search algorithms to determine a likely diagnosis based on a patient's symptoms
The modern era of AI began in the early 21st century with systems such as IBM's Watson (2010), which expanded the capabilities of AI beyond symptom-based diagnosis alone. Watson could already take questions in human language.
Where can we meet AI in healthcare?
AI in healthcare serves both healthcare professionals and patients.
For ordinary mortals, for example, these categories are:
Personal health
AI helps address the processing of personal health data from wearable devices and electronic health records. . Apple Watch's advanced sensors capture complex health data, while AI algorithms analyse this data to provide personalised information.
Chatbots
Chatbots, for example, have proven effective in . If you'd like to try it out, check out, for example, the popular from ChatGPT. If you just need advice on symptoms or are interested in something health related, try by Slovak artists. MediSearch is a chatbot that searches through studies and scholarly articles instead of the entire internet. It can speak English or Slovak, but it can speak Czech too.
Besides chatbots, there are many other applications that use AI, such as Czech . This will help with AI transcription, annotation and analysis for clinicians.
Healthcare professionals can then use AI in the following ways:
Genetics and disease
AI can also, for example, analyse DNA sequences and can thus help diagnose genetic disorders. It can also help with protein analysis, from which various health conditions or responses to treatment can be predicted.
Extra pair(set) of eyes
Computer-aided detection (CAD) can help in image analysis. This is a technology designed to reduce the number of observation oversights - and thus the rate of false negatives - by doctors interpreting medical images. For example, prospective clinical trials have shown an increase in breast cancer detection rates with CAD.
They can also help detect conditions such as stroke, large vessel occlusion, intracranial bleeding, pulmonary embolism and various cancers.
In dermatology, for example, GOOGLE Inception V3, trained with more than 1 million non-specific images and more than 100,000 dermatological images, can detect dermatological malignancies at levels comparable to trained physicians.
Immunisation and public health
AI-powered digital health interventions have improved immunization information systems. They provide real-time data, and help address gaps in vaccination rates, for example. Tools such as 2D barcodes help reduce data errors by directly uploading information from vaccine vials into information systems. Interactive dashboards and geographic information systems (GIS) have benefited immunization campaigns by providing real-time information, helping to capture emerging outbreaks and improve surveillance.
Telecare
The COVID-19 pandemic has triggered a significant increase in the use of telemedicine. Many healthcare facilities quickly switched from face-to-face to "virtual" visits. Telemedicine has improved access to health care, especially for people in rural areas, those without easy transportation, and patients with physical disabilities.
In addition, telemedicine has proven to be less expensive compared to traditional (in-person) visits and has saved time for both patients and physicians.
The integration of AI has significantly improved telemedicine, with the ability to constantly update by learning from feedback and quickly analyzing data, saving time and money while helping physicians with decision making.
Ethical aspects and limitations
The Problem | Solution |
Protecting the privacy and security of patient data, especially when private entities obtain patient information. | The application must comply with data protection rules (e.g. GDPR in the EU and HIPAA in the US). |
Potential biases in AI algorithms. | Tools are being developed to detect and quantify bias. In addition, the scientific community and regulators are defining fairness metrics that models must meet. |
Ensuring the safety and validation of AI systems prior to deployment. | As with pharmaceuticals, a phase of clinical trials is underway to verify efficacy and safety. AI tools must also obtain approval from regulatory authorities (e.g. FDA in the US or CE marking in the EU). |
Addressing the "AI gap" between statistically reliable algorithms and meaningful clinical applications. | AI systems are developed in collaboration with physicians to ensure their real-world applicability. Algorithms are therefore designed to provide interpretable outputs that are medically usable. |
Building trust through transparency about the operation of AI systems, particularly with regard to the nature of "" of many algorithms. |
Conclusion
AI has been in medicine since the second half of the 20th century. The first machines could provide a diagnosis based on symptoms. Today, they can do more advanced things like analyse DNA or read X-rays, which can make doctors' work much more efficient and faster. AI in healthcare has long been used not only by healthcare professionals, but anyone can encounter it, for example through apps such as MediSearch or ChatGPT.
In the future, we are sure to see more remarkable developments in AI that will further improve and make healthcare more accessible.