Artificial intelligence is often used in medical diagnosis. Healthcare AI can help detect diseases. It also helps predict progression. Furthermore, automated AI and health screening deals with the issue of misdiagnosis (Ishaq & Siraj, 2002). Below are examples of solutions screening and medical diagnosis.
1. AI Ultrasound Ultrasound is a type of imaging test used to create pictures of organs, tissues, and other structures within the body (also known as a sonogram). Unlike X-rays, ultrasound does not use radiation (Minoda et al., 2020). Ultrasound imaging surpasses other medical imaging modalities in terms of ease, non-invasiveness, and real-time functionality. Computed tomography (CT) exposes patients with radiation, while magnetic resonance imaging (MRI) is not and is invasive, expensive, and time-consuming (Muse & Topol, 2020). As a result, US imaging is used in a variety of medical professions for both screening and final diagnosis (Kuang et al., 2021). Deep learning, a popular AI technique, is characterized by image pattern recognition, and is especially useful for physicians who rely heavily on photography, such as ophthalmologists, radiologists, and pathologists. Despite the fact that obstetric and gynecological ultrasound are two of the most popular imaging procedures, AI has achieved minimal impact in this field so far (Minoda et al., 2020). However, Artificial intelligence (AI) has the potential to replicate ultrasound activities, such as the automatic selection of high-quality acquisitions and immediate quality assurance. Interdisciplinary interaction between AI developers and super-high-ranking experts should not forget this possibility (Muse and Topol, 2020).
2. Billions of sample points are recorded in biological wave shapes, including sleep signals for sleep diagnosis, breast movements as a patient moves up and down, electrical impulses between neurons in dream states, and rapid eye movements (Papinetl., 2020). These are clues to understanding a patient’s health, but deciphering its meaning and discovering all the underlying patterns is not always available with the naked eye. Artificial intelligence (AI) and machine learning can uncover trends in large datasets of people’s health and provide valuable knowledge (Shaheen, 2021b). Data collected during sleep and processed by AI can help you identify who will get sick in the future, long before symptoms develop. In theory, restless sleep throughout the night indicates who will develop dementia later in life, or variations in heart rate fluctuations could warn of the onset of pneumonia.
3. AI-Cancer Recognition Solutions In virtually every country, cancer is the leading cause of mortality and a major obstacle to increasing life expectancy. According to the World Health Organization, cancer was the leading or second leading cause of death from cancer in 23 countries between 2000 and 2019, with 23 of those diagnosed before the age of 70. A new technology is the lynchpin, allowing the use of health gaps across the continuum of cancer treatment. Artificial intelligence (AI) is being developed as a groundbreaking technology. Diagnostic AI treatments can make a huge difference in eliminating health disparities, especially in low-resource environments. The integration of AI into cancer treatment can increase diagnosis accuracy and speed, support health care decisions, and promote better health outcomes (Al-Shamasneh and Obaidellah, 2017). Artificial intelligence (AI) has the potential to improve cancer screening, tumor genetic characterization, drug discovery, and cancer surveillance. Cancer is a complex and multifaceted disease, caused by a variety of genetic and epigenetic changes. AI-based algorithms have great potential for early detection of genetic abnormalities and abnormal protein interactions. Modern biomedical research is also used in safe and ethical AI technologies in clinics (Kim et al., 2020). Despite major breakthroughs in treatment and diagnostic approaches, cancer remains one of the most deadly diseases. AI may change this in the near future. Their patterns can help in treating strategies, reducing false positives and negatives, leading to future prognosis of cancer sclerosis (Patel et al., 2020).
4. Pathology The effects of AI are most commonly seen in pathology. As more laboratories switch to digital pathology, the necessary infrastructure to provide these tools exists, and their use becomes the norm in clinical practice. In pathology, AI can develop image analysis tools that can be used to support diagnosis, or record a unique understanding of disease biology that cannot be gained by human viewers. Many cases of diagnostic support are already available, including tumor identification, automated tumor classification, immunohistochemistry evaluation, and prediction of mutation status. There are many hurdles to consider, especially the reviews and regulatory environment of these products (Salto-Tellez et al., 2019) (Tizhosch and Pantanowitz, 2018) (Niazi et al., 2019).
5. AI PCR Testing PCR is short for polymerase chain reaction. It is a test that looks for genetic material from a particular organism, like a virus. If we have a virus at the time of testing, the test will detect its existence. Even if we are no longer sick, the test can reveal fragments of the virus (Murphy et al., 2020). A SARS-COV-2 virus-specific reverse transcriptase polymerase chain reaction (RT-PCR) test is commonly used to diagnose coronavirus disease 2019 (COVID-19). However, as this test can take up to 2 days to complete, serial testing may be necessary to eliminate the possibility of false negative results. Mei et al., (2020) employed artificial intelligence (AI) techniques to combine chest CT images with clinical symptoms, exposure history, and laboratory testing to detect COVID-19 positive individuals in this study. Of the 905 patients, 419 (46.3 percent) tested positive for SARS-COV-2 using real-time RT-PCR assay and next-generation sequencing RT PCR. When compared to a senior thoracic radiologist, the AI system achieved an area under the curve of 0.92 and had similar sensitivity in the test set of 279 patients. The AI approach also enhanced the detection of COVID-19 positive patients with normal CT scans who were positive by RT-PCR, accurately detecting 17 out of 25 (68%) patients, while the radiologist classified all these patients as COVID-19 negative. The suggested AI system can aid in quick diagnosis of COVID-19 patients when available with CT scans and clinical history (Mei et al., 2020).
6. Liver Diagnosis The liver is the largest organ in the human body. It is in charge of all metabolic functions in the body, from converting nutrients in the diet into useful body chemicals to storing and later providing these substances to the cells as needed. It is also in charge of the conversion of harmful chemicals into non-toxic substances. Bile generation is among the other important activities of the liver. Protein synthesis, glucose storage and release, hemoglobin processing, blood cleansing, and immunological defense factor production, bilirubin clearance, and so on. As a result, it is the most important and primary body organ. Its health must be maintained to improve overall health. However, the reality is that most people are best off ignoring it when it comes to health. Given the intensity of symptoms in the early stages of liver disease, it can be difficult to recognize. Because the liver continues to operate even when partially damaged, problems with liver related diseases are often not recognized until it is too late. Early detection has the potential to save lives. Early symptoms of various disorders can be noticed, even if they are not visible to even the most expert medical practitioner. Patients who are diagnosed early in their lives have a longer life expectancy. Machine learning algorithms have been developed to predict disease risk and outcomes using a number of clinical parameters, such as assessing liver fibrosis and steatosis, predicting hepatic decompensation in primary sclerosing cholangitis, monitoring and selecting liver transplant recipients, and predicting post-transplant survival and complications (Dikaratanachart et al, 2021).
7. Detects diseases in the earliest stages, the importance of early detection: Delays in medical treatment have the potential for devastating repercussions. Many doctors and hospitals across the country have seen major declines in patients turning up for everything from basic checkups to cancer tests such as mammograms, colonoscopies, and even pediatric care. Delaying care that delays the discovery, diagnosis, and treatment of major health disorders can result in a lower quality of life, diseases that worsen rather than cure, and even fatalities that could have been avoided. According to preliminary assessments from hospitals across the country, life-saving treatments including cardiac catheterization to treat a major, potentially fatal form of heart attack and the number of individuals diagnosed with cancer are both on the decline (Etzioni et al., 2003). In many critical illness cases, the prognosis for treatment is determined by how early diseases are diagnosed. Ideally, symptoms appear early enough for us to recognize that something is wrong and for us to seek professional help. However, some diseases lack early warning signs, and we often hear about cases when early warning signs come too late (Jiang et al., 2017). Furthermore, many people cannot afford to consult medical specialists on a regular basis. For some, the time it takes to see a doctor can also be a barrier. This is where AI applications can assist in performing first-level screens to detect small features that may indicate underlying concerns and then refer them to professionals (Shen et al., 2019).
8. Diabetic Retinopathy Diabetic retinopathy is a disorder that affects vision by causing lesions on the retina. DR affects many diabetic people in the early stages without causing any symptoms. If these asymptomatic patients do not seek medical care from an eye specialist as soon as possible, further stages of DR can develop, resulting in irreversible blindness (Paddy et al., 2019). Timely diagnosis and treatment can help prevent DR, which is one of the primary causes of blindness. By detecting DR problems in their early and asymptomatic stages, deep learning can help mitigate some of the issues. This could mean that more diabetic individuals will be screened, and, more importantly, an early diagnosis of DR will be achievable, allowing the patient to be transferred to an eye specialist for treatment. This is possible thanks to color fundus photos (CFPs) (Grzybowski et al., 2020) (Wong & Bressler, 2016), which are evaluated using deep learning to provide target outcome predictions. The deep learning method is trained on high-quality CFPs that have been rated by professionals for DR severity. The dataset, which Kaggle provides as one of many public sources, is a key aspect in deep learning model training. Several articles have used a deep learning approach based on Convolution Neural Networks (CNNs) with various improvement strategies to improve accuracy. The use of such a predictive DR screening system can help governments deal with the huge issue of diabetic patients going blind, as well as reduce the cost of dealing with this problem (Grzybowski et al., 2020).
9. Heart Conditions Individual components (segments) of the ECG wave can be identified to diagnose heart problems. To properly detect each segment of the ECG wave, researchers look for slight patterns and recurring symptoms (Chen et al., 2021). Coronary heart disease, which is a blockage of the veins that irrigate the heart, cardiac arrhythmias, which occur when the electrical impulses that synchronize the heartbeat are not conducted properly, and other heart problems can be discovered (Abdel-Motaleb & Acula, 2012). The ECG waveform is a periodic time-series signal composed of P waves, QRS complexes, and T waves. The easiest way to assess the mixture of periodicity and specific properties of this signal is to use the Conv-LSTM deep learning method. The CONV component is essentially a convolutional neural network (CNN) that extracts features. Long short-term memory (LSTM) is a specific variation of a recurrent neural network (dedicated to time-series signals) that extracts the time series characteristic waveform and captures the long-term temporal relationships of the ECG signal (Djerioui et al, 2020). Deep learning-based screening for cardiac issues can help individuals with underlying medical conditions, seek expert help sooner and avoid serious medical incidents. In the above use cases including scene-recognition, outlier detection, convolutional neural networks, AI plays a vital role.
Conclusion
Surveillance and data collection tools are common in modern hospitals, with large-scale information systems collecting and sharing data. Machine learning techniques are now well adapted to the evaluation of medical data, and much work is being done in medical diagnosis, especially in subtle or small clinical conditions. When it comes to using machine learning to diagnose diseases, there are some challenges to consider. First, machine learning does not have the capability to replace a doctor. While machine learning can help predict the likelihood of developing a disease, it cannot take over all the responsibilities of a specialist. For example, machine learning can help determine whether someone has cancer faster and early, but the therapy must still be chosen by a doctor. Good data is necessary for machine learning to succeed. It is useless if the data is of poor quality or if no patterns can be detected in it. Similarly, when a model is trained using a set of data, there is a chance that existing patterns will not apply to fresh data. Mostly Doctors and hospitals should employ newly developed models. Upgrading machine learning models is not always part of a doctor’s job description. It can be hard for a health professional to find time to assist with data and result validation. Using a proper trained model that does not yield more accurate predictions can be risky, especially when human health is at stake. It is also very dangerous to abandon a tried-and-true paradigm in favor of one that may not succeed.
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