AI in healthcare

When you walk into the waiting room, do you still wait for the nurse to take your medical chart? Or do you just slot it into the machine, pick a reason for your visit, and sit down until your turn comes up?

And does your doctor already have a chat where you can ask anything instead of a lengthy visit?

In some places these things are already routine. Let's take a look at how AI helps in healthcare.

AI first appeared in healthcare back in the 1960s. The first AI medical consultant was called INTERNIST-1 and was used from 1971. It applied search algorithms to determine a likely diagnosis from a patient's symptoms.

The modern era of AI began in the early 21st century with systems such as IBM's Watson (2010). These pushed AI beyond diagnosis based on symptoms alone. Watson could already take questions in human language.

AI in healthcare serves both healthcare professionals and patients.

For ordinary mortals, it shows up in areas like these:

AI helps process personal health data from wearable devices and electronic health records. You can find AI, for example, in the Apple Watch. The watch's advanced sensors capture complex health data, and AI algorithms analyze it and give you a personalized overview.

Chatbots have proven effective, for example, at easing the symptoms of depression. If you'd like to try one, check out the popular Psychologist from ChatGPT. And if you just need advice on symptoms or are curious about something health-related, try MediSearch from Slovak developers. MediSearch is a chatbot that searches through studies and scholarly articles instead of the whole internet. It speaks English and Slovak, and it manages Czech too.

Besides chatbots, plenty of other applications use AI, such as the Czech UpHeal. It helps clinicians with AI transcription, notes, and their analysis.

AI can analyze DNA sequences, so it helps diagnose genetic disorders. It can also analyze proteins, which lets it predict various health conditions or responses to treatment.

Computer-aided detection (CAD) helps with image analysis. This technology is meant to reduce the number of oversights, and with it the rate of false negatives, among doctors reading medical images. Prospective clinical trials, for instance, have shown that CAD raises the breast cancer detection rate.

CAD can also help detect conditions such as stroke, large vessel occlusion, intracranial bleeding, pulmonary embolism, and various cancers.

In dermatology, the GOOGLE Inception V3 model, trained on more than 1 million non-specific images and more than 100,000 dermatological images, can detect skin malignancies at a level comparable to trained physicians.

AI-powered digital health tools have improved immunization information systems. They provide real-time data and help address gaps in vaccination rates. Tools such as 2D barcodes cut data errors by uploading information from vaccine vials straight into the systems. Interactive dashboards and geographic information systems (GIS) have benefited immunization campaigns: with real-time information, they help catch emerging outbreaks and improve surveillance.

The COVID-19 pandemic drove a sharp rise in the use of telemedicine. Many healthcare facilities quickly switched from in-person to "virtual" visits. Telemedicine improved access to healthcare, especially for people in rural areas, people who can't travel easily, and patients with physical disabilities.

Telemedicine also turned out cheaper than traditional in-person visits and saved time for both patients and physicians.

Adding AI makes telemedicine better still. AI keeps learning from feedback and analyzes data quickly, which saves time and money while helping physicians make decisions.

| 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 "black boxes" of many algorithms. | Explainable AI (XAI): techniques are being developed to explain why a model has reached a particular conclusion. Some jurisdictions are even mandating disclosure of how the AI was trained and how it works. |

AI has been in medicine since the second half of the 20th century. The first machines could give a diagnosis from symptoms. Today they handle more advanced things like analyzing DNA or reading X-rays, which makes doctors' work much faster and more efficient. AI in healthcare has long since stopped being used only by healthcare professionals. Anyone can run into it, for example through apps like MediSearch or ChatGPT.

The future is sure to bring more remarkable developments in AI that will further improve healthcare and make it more accessible.

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