A Doctor’s Guide to Artificial Intelligence (AI)

Artificial intelligence (AI) is a technology that can help doctors by doing tasks like paperwork, and helping them make decisions. It’s becoming popular and attracting a lot of money, but some companies are exaggerating what their AI can do.

This makes it hard for doctors to know what’s real and what’s not. To help them understand AI better, a group called The Medical Futurist Institute wrote a short guide that’s easy to read. The guide explains the basics of AI and how it can help in medicine.

Examples for how healthcare could benefit from A.I.

Here’s how doctors can benefit from using AI in their practice:

Improving in-person and online consultations

Babylon Health launched an application that offers A.I.-driven consultation. It uses the patient’s medical history and common medical knowledge. Patients report their symptoms through the app which checks those in a database of diseases using speech recognition. After that, it offers a course of action14.

Health assistance and medication management

The medical start-up Sense.ly developed Molly, a virtual nurse that was designed to have a smiling face coupled with a pleasant voice. Its goal is to help assist patients with monitoring their health or disease management in-between doctor’s visits using ML. It also provides customized follow-up care, with a focus on chronic diseases15. A similar approach was used by the AiCure app that uses a smartphone’s camera and A.I. to confirm that patients are adhering to their prescriptions16. This could be useful for people with serious medical conditions or participants in clinical trials.

A.I.-driven diagnostics

In 2020, the FDA approved a software programme from the company Caption Health that allows medical professionals to perform cardiac ultrasound imaging without specialized training. It uses A.I. to provide real-time guidance and also the ability to save images of diagnostic quality. It acts as a “co-pilot” for those performing an ultrasound scan as it was designed to emulate the guidance that an expert sonographer would provide to optimize the image. It gives instructions on how to manipulate the transducer, and provides automated feedback on diagnostic image quality17.

Mining medical records

Collecting, storing, normalizing, and tracking medical records is an obvious step for A.I. As an example, Google Deepmind Health is cooperating with the Moorfields Eye Hospital NHS Foundation Trust to improve eye treatment by analyzing retina scans. The images are analyzed by DeepMind’s algorithms resulting in a detailed diagnosis and a so-called “urgency score” in about 30 s. The prototype system can detect diabetic retinopathy, glaucoma, and age-related macular degeneration18.

Precision medicine

The company Deep Genomics aims at identifying patterns in genetic data and medical records of patients trying to link mutations to medical conditions. Oncompass Medicine uses A.I.-based algorithms to match genetic mutations found in patients’ tumor samples with ongoing clinical trials worldwide. This way, patients can receive precisely targeted treatments specific to the kind of cancerous tissue they have.

Designing treatment plans

IBM Watson developed a software that provides evidence-based treatment options for oncologists. It was designed to analyze both structured and unstructured data in medical records that may contribute to decision-making about treatment pathways. The software combines data from the patient’s medical record with clinical expertise, and research papers to suggest promising treatment plans19. There are many similar examples in other specialties. Creating an optimized radiation therapy delivery plan usually takes days. A.I.-based technologies help speed this process, completing the process in a couple of minutes20,21.

Drug creation

The way pharmaceutical companies develop new drugs through clinical trials can take several years and can cost billions of dollars. Speeding this up while making it more cost-effective would have an enormous effect on healthcare. The company Atomwise uses supercomputers to root out treatments from a database of molecular structures. They also launched a search for a previously unknown combination of safe and existing medicines and in a few days found two drugs predicted by the company’s A.I. technology which may significantly reduce the infectivity of Ebola. Such analysis typically would have taken months or years22.

Triage tools

It is crucial to be able to predict how severe a patient’s medical condition is to support the early identification of those who are vulnerable and at high-risk, especially in emergency medical services. In a study, authors developed and validated an A.I.-based algorithm using deep learning that accurately predicted the need for the critical care of patients and outperformed the conventional triage tools and early warning scores23. In another study, authors analyzed online triage tools from more than 150 000 patient interactions with a chatbot, and they found a decreased level of the urgency of patients’ intended level of care in more than one-quarter of the cases24. Both studies indicate that A.I.-based technologies can facilitate triaging even before patients reach the point-of-care.

While AI has the potential to revolutionize healthcare, there are also some potential drawbacks to consider. For example, some people may be hesitant to trust AI with their healthcare information. Additionally, AI systems are only as good as the data they are trained on, so it’s important to ensure that the data used to train these systems is accurate and representative of the population.

Despite these potential drawbacks, the benefits of AI in healthcare are significant. By streamlining administrative tasks, improving diagnoses, and predicting patient outcomes, AI can help doctors provide better care to their patients. As AI technology continues to advance, it’s likely that we will see even more innovative ways of using AI in healthcare in the future.

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