Health

Will artificial intelligence replace your doctor?

In recent years, there has been a lot of news about the potential of artificial intelligence (AI).Chat GPT, launched more than a year ago, and other similar applications based on AI algorithms have attracted attention The benefits and risks of using these technologies in different areas of our livesand the need for its supervision.

One area where artificial intelligence has begun to be used successfully is medicine, for example to aid diagnosis or to design new treatments. despite this, This technology has limitations and its uncritical use may put patients at risk. Here, we review some of the major current and future medical applications of artificial intelligence.

Artificial intelligence is a technology that allows computers to imitate certain abilities of the human brain, such as learning, language understanding, and decision-making. With this technology, we can enable computers to learn from experience, perform tasks, solve problems, and make decisions just like humans do, even faster and more efficiently. And all this without constant human intervention: AI can learn on its own and improve over time. Therefore, it is not surprising that this potential has applications in the medical field.

Artificial intelligence algorithms can Process large amounts of data from patients’ electronic medical records. This skill is useful in biomedical research, but also aids in the diagnosis and prevention of disease. Using clinical data, AI tools can be trained to identify specific patterns associated with disease or even discover new ones.

The types of medical data processed by AI are very diverse. It includes medical images such as X-rays, CT or MRI scans, microscopic images of tissue, and photographs of different parts of the skin or eyes. There is also data from medical records, such as diagnoses, treatments, medications, or vital signs, and results from laboratory tests, such as common blood and urine tests. Likewise, genetic data can be processed through continuous monitoring devices, such as heart and blood glucose monitors, or connected devices, such as smart watches that record physical activity, sleep, and other health-related parameters.

Endless applications

In medicine, Artificial intelligence can revolutionize the way diseases are diagnosed and treated. Its potential is being studied to improve the accuracy and speed of diagnosis, predict the evolution of disease and detect changes in a patient’s health status early; and to predict a patient’s response to specific treatments, thus enabling personalized treatment. Artificial intelligence can also play a key role in accelerating the era of biomedical and pharmacological research, for example by finding new therapeutic targets and developing new drugs. In hospitals, it can be used to improve management and administrative processes in health systems, and in areas such as telemedicine, it can be used as a virtual assistance tool.

Despite its huge potential, The application of artificial intelligence in medicine poses a series of methodological and ethical challenges. Concerns raised by this technology include the transparency of algorithms and their biases, data privacy, and equitable access to technology.

Many artificial intelligence models are difficult to interpret, especially those based on so-called deep neural networks.These algorithms lack transparency because We don’t know how they make decisions, which may cause distrust among health professionals and patients. In order for AI tools to be accepted and adopted, it is crucial to be able to explain the reasoning behind the diagnosis. Finally, these tools must undergo rigorous clinical trials like any other medical device and prove their effectiveness and safety before widespread adoption.

Algorithms that do not reflect reality

The quality of AI results largely depends on the quality and representativeness of the training data. If certain patient groups are underrepresented due to their origin, gender, age or socioeconomic level, these biases will be reflected in the AI’s behavior. For example, If the data are primarily from white men of a certain age, the model may not apply to other patients, it will not be as accurate and may lead to incorrect diagnosis and inappropriate advice. This was the case with some algorithms designed to identify skin cancers through biopsy analysis, which found more severe lesions in dark-skinned patients.

Algorithms can also be affected by bias if the data set used does not well reflect all characteristics of the disease and its subtypes. In these cases, the model may not generalize to what occurs in the real world, and new algorithms trained with more representative datasets must be developed. AI also often fails to understand a patient’s full clinical context: their past medical history and other relevant factors that doctors consider to make an accurate diagnosis. Algorithms can also make errors if mutated or atypical forms of the disease emerge instead of during training.

Disease diagnosis

Researching AI applications as a diagnostic tool in different medical fields such as cardiology, ophthalmology, dermatology, oncology or pathological anatomy. The latter studies the causes, development and effects of disease based on the structural changes that occur in our body’s cells and tissues, which can be detected through microscopy such as biopsies. Cytology (cells) or histology (tissue) preparations can be scanned and digitized, making them easy to analyze using artificial intelligence.

in pathoanatomy Artificial intelligence tools have been developed to diagnose diseases such as hepatitis B In oncology, its use has been explored for the detection of cancers such as breast, prostate, stomach or colon.

In 2020, researchers in the United Kingdom and the United States collaborated with Google to develop an artificial intelligence system based on the Deep Mind algorithm that can detect breast cancer in the early stages of the disease through mammograms, with better performance than radiologists. Treatment may be more effective.That same year, researchers at the University of Pittsburgh developed an algorithm Prostate tumors are identified with high accuracy from biopsy images.

In the field of ophthalmology, AI has been used to diagnose diabetic retinopathyIt is an eye complication that, if not detected in time, can lead to vision loss and blindness in diabetic patients.

In 2018, the U.S. Drug Administration approved the first autonomous diagnostic tool based on artificial intelligence.This is IDx-DR (currently Luminetics Core), a program Detecting the presence of diabetic retinopathy in retinal images. The deep learning algorithm was conceived by Michael Abramoff, an ophthalmologist and artificial intelligence expert at the University of Iowa, who designed it to allow technicians with minimal ophthalmology training to use it in primary care settings. Using, these technicians are responsible for scanning the patient’s retina. If the result is positive, the patient will be referred to an ophthalmologist’s office for an in-depth clinical evaluation, and if the result is negative, the test will be repeated one year later.

In the field of ophthalmology, images are crucial for diagnosing diseases and tracking their evolution.For this reason, many AI applications to diagnose cataracts, glaucoma and other eye diseases such as macular degeneration Related to age.

However, not all mountains are oregano. In 2021, researchers at the University of Washington in Seattle examined seven artificial intelligence algorithms designed to detect diabetic retinopathy and found that only one passed doctors’ discretion under typical use conditions.Such results show There is still much work to be done for deep learning algorithms to diagnose eye diseases with performance comparable to clinical experts.

Drugs designed using artificial intelligence

On average, the development of a new drug takes more than ten years to complete. at the moment, The application of artificial intelligence reduces this time and a lot of moneyor information assigned to the task by the laboratory. Although it will still take time for them to reach the market, the first drugs developed with artificial intelligence are already being studied in clinical trials to determine whether they are effective and safe.

For every new drug that comes to market, hundreds of molecules must first be evaluated in the laboratory, most of which are unsuccessful. Artificial intelligence can help find the most suitable therapeutic targets in our bodiesdesigning drugs to interact with them, and identifying subgroups of patients who respond best to specific drugs.

For example, natural language processing is used to extract data from large archives of scientific publications and find relationships between them, thereby identifying potential targets for treating disease.Machine learning algorithms are used Analyze large amounts of chemical data and build models to predict how a drug will work in our bodies. In this way, artificial intelligence can help select the best candidates from countless compounds, and only the most promising ones are tested in the laboratory. It is also used to design new molecules or modify existing ones so that they fit like a glove against their target: for example, in the case of antibodies that block proteins linked to cancer. All of this work is now done by computers, whereas previously it would have taken months or even years in the lab and required a large number of researchers.

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