Cancer has a low median survival rate and is an aggressive disease. Ironically, because of the high rates of recurrence and mortality, the treatment process is drawn out and expensive. To increase the patient's survival rate, accurate early diagnosis and prognosis prediction of cancer are crucial. Numerous scientists have applied computational techniques like multivariate statistical analysis to analyse the prognosis of the disease as a result of advancements in statistics and computer engineering over the years. The accuracy of such analyses is significantly higher than that of empirical predictions. Additionally, as applications for artificial intelligence (AI), particularly machine learning and deep learning, have become more widespread in clinical cancer research in recent years, the accuracy of cancer prediction has increased significantly. This article summarises the benefits of AI for cancer diagnosis and prognosis after reviewing the relevant literature. We look at how AI can help with cancer diagnosis and prognosis, focusing on its unmatched accuracy—which is higher than that of conventional statistical oncology applications. We also show how these techniques are progressing the discipline. Finally, potential and difficulties in the application of AI in medicine are examined. As a result, this essay offers a fresh viewpoint on how AI technology might help advance cancer detection and prognosis, as well as further advance human health.Your research can be presented and published at the 14th International Healthcare, Hospital Management, Nursing, and Patient Safety Conference in front of professionals from around the world. Join us from July 25–27, 2024, in Dubai, UAE, and virtually by submitting your abstract right away.Submit here: used to predict cancerOver the past few decades, professionals, paramedics, and carers from all areas have been asked to estimate cancer prognoses based on their respective expertise. The advent of the digital data era has made clinicians aware of the necessity to embrace AI technologies like DL and ML.They contend that it is challenging to predict how cancer will develop because of the intricate and extensive nature of statistical analysis. Health-care experts are also concerned about the risk that a patient may contract a disease, can have a tumor recurrence after treatment, or die. These considerations have a substantial influence on treatment options and results. In actuality, a substantial corpus of research on clinical cancer focuses on determining prognosis or predicting patient response to therapy. More effective medicines can be given to patients with more accurate prognoses; in fact, these treatment alternatives frequently include personalising or individualised care for every patient. In order to anticipate cancer, AI can analyse and comprehend "multi-factor" data from various patient assessments and provide more detailed information about the patient's survival, prognosis, and disease progression projections. Enshaei et al. explored a variety of tactics, combining classifiers with conventional logistic regression analytic techniques to show how AI may be used to provide ovarian cancer patients with forecasting and predicting information.Artificial intelligence-based algorithms have been demonstrated to be able to analyse unstructured data and accurately predict the likelihood that patients would contract various illnesses, including cancer.47 Accurate agnostic AI algorithms can affect cancer screening recommendation outcomes and improve risk stratification criteria.48-52 For instance, a synthetic "neural network model" for "colorectal cancer risk stratification" showed the highest degree of accuracy compared to "current screening guidelines."We are excited to announce a unique opportunity for student involvement in our upcoming healthcare conference. Register for the CME/CPD/CE accredited 14th International Healthcare, Hospital Management, Nursing, and Patient Safety Conference will be held on July 25-27, 2024, and get a chance to explore Dubai, UAE. We believe that students like you hold immense potential to drive innovation and positive change in the healthcare sector.Register here: Cancer Types Can Be Predicted Easily?Given that there are numerous varieties of cancer and that cancer is a hereditary disorder,54,55 it is not surprising that developments in AI have benefited oncology in particular. For instance, it has been demonstrated that "DNA methylation analysis" in cancer can help with classification and prognosis.56 More than 70% of malignancies that have been labelled by humans can be reclassified using the "machine-determined DNA methylation" technique, which could have a significant impact on the prognosis and treatment options.57 According to a study, MethylationEPIC (850 k) and Illumina HumanMethylation450 had the highest classification accuracy of 93% for 82 different types of "brain tumours."58 The authors claimed even greater accuracy than pathologists.Conventional Cancer Diagnosis and Treatment MethodsTraditionally, a patient seeks medical attention from a doctor when they have symptoms like hard lumps on their bodies or strange patterns on their skin. The clinic compiles the patient's clinical history, screening exams, and medical imaging as the initial step in the cancer-detection process. The screening test looks for people who have a particular cancer or pre-cancer but have not yet displayed any symptoms in order to quickly refer them for further testing and treatment if necessary. Several scan modalities can be used to do a pre-stage analysis.This is carried out as a preventative measure to avoid cancer in a high-risk population being discovered too late. After a questionable discovery, tissue samples from the affected area are collected and examined in a lab. For additional information on the findings, medical professionals are consulted. They compile, synthesise, and analyse the pertinent data while also recommending a diagnosis. The appropriate course of therapy is suggested and the patient is advised of the current diagnosis and prognosis. With possibilities for speedy diagnosis and the capacity to learn from mistakes, this procedure is advantageous for patients as well as the healthcare system. Nevertheless, this procedure has room for error and is adaptable based on the medical speciality.Data Repositories for CancerThe term "digital health" refers to the application of digital transformation to the healthcare sector and includes software, data, technology, and services. Radically interoperable data and AI, according to Deloitte Insights, offer consumer- and prevention-focused healthcare. Data accessibility is crucial for data-driven AI research, and the scarcity of sufficient data to conduct studies often frustrates researchers.Cancer researchers are always creating new clinical trials in an effort to learn how to improve cancer care, therapy, and prevention. Many institutions provide online lists of open clinical trials to help participants find research studies that might be appropriate for them. For finding a cancer clinical trial, the following resources, listings, and searchable databases are helpful.Now the registration of exhibitor/sponsor. Registration is open for the CME/CPD accredited 14th International Healthcare, Hospital Management, Nursing, and Patient Safety Conference. Join us in Dubai, UAE on July 25–27, 2024. It brings exclusive insights and inspirational speakers to discuss the latest research & trends. This conference is for you if you or your coworkers have any questions about nursing, healthcare management, or patient safety.Register here:
Nursing conferences | Nursing conferences in Dubai | Dubai Nursing Conference | Healthcare Conference in UAE | UCE Nursing Conference | Emirates Nursing Conference | Nursing Event | Nursing Symposium | Healthcare Conferences | Patient Safety Conferences | Nursing Conferences | Nursing Meetings | Registered Nursing Congress | Healthcare Workshop | Patient Safety Symposiums | Patient Education Seminars | Healthcare Research | Healthcare Exhibitions | Healthcare Exhibitions | Registered Nurse Meetings | Registered Nurse Congress | Registered Nurse Workshop | Registered Nurse Symposiums | Registered Nurse Seminars | Registered Nurse Expo | Registered Nurse Exhibitions


Please Sign in (or Register) to view further.