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Lucubrate Magazine, March 2nd, 2023
By analysing a patient’s medical history, genetic data, and other factors, AI systems can identify the most effective treatment options for an individual patient.
How to Use Data and AI for the Identification of Disease
By leveraging advanced analytics, machine learning, and other tools, medical companies can better understand unmet medical needs. We can create education about the disease and the treatment journey in specific regions or healthcare systems with the highest needs. Moreover, engagement models can include specialised peer-to-peer education sessions or well-structured testing programs, such as family testing, to identify potential disease markers.
It can take several years for patients to be diagnosed with a rare disease; symptoms often worsen significantly. Recent advances in analytics, coupled with greater accessibility of large data sets, allow pharmaceutical companies to identify and spotlight patient groups more quickly [1].
For example, one global pharmaceutical company with a sizable rare disease operation deployed digital analytics to improve its patient-finding approach for one of its rare-disease treatments. Using multiple core data sets of potential patients, the company built a predictive model to estimate the likelihood of any given individual having the targeted rare disease. The company then used this model to identify a large population of undiagnosed patients and the physicians disproportionately likely to oversee patients with the disease. This more precise engagement of physicians enabled an increase of more than 40 per cent in the number of patients who could benefit from earlier and better access to diagnosis and care in the five years following the treatment launch [1].
Methods for advancing disease diagnosis.
Contrary to considerable advancements over the past several years, accurate clinical diagnostics face numerous obstacles. They must constantly resolve and improve to treat emerging illnesses and diseases effectively. Even healthcare professionals recognise the barriers we must overcome before we detect sickness in conjunction with artificial intelligence. Doctors only partially rely on AI-based approaches since they are still determining their ability to anticipate illnesses and associated symptoms. Thus, much work is required to train AI-based systems to increase the accuracy of predicting disease diagnosis methods. Hence, AI-based research should be conducted in the future by considering the flaw mentioned earlier to provide a mutually beneficial relationship between AI and clinicians. A decentralised, federated learning model should also be applied to create a single training model for disease datasets at remote places for the early diagnosis of diseases[3].
Detecting any irresistible ailment is nearly an afterwards movement, and forestalling its spread requires ongoing data and examination. Hence, acting rapidly with accurate data affects the lives of individuals around the globe socially and financially [6]. The best thing about applying AI in health care is to improve from gathering and processing valuable data to programming surgeon robots. This section expounds on the various techniques and applications of artificial intelligence, disease symptoms, diagnostics issues, and a framework for disease detection modelling using learning models and AI in healthcare applications [7].
Ethical Issues Offering Patient-centered Care through Data and AI
AI-driven healthcare plans must also address the ethical issues of extracting patient data. We can use AI applications to predict patient behaviour (e.g., who is likely to miss an appointment, skip a test, or refuse treatment), but they must be done in a manner that respects patient privacy and medical information [4].
A way AI developers are using medical data is to create personalised treatment plans for patients. An AI system can determine the most effective treatment options for an individual patient by analysing a patient’s medical history, genetic data and other factors. Personalised medicine can improve treatment outcomes and reduce the risk of adverse side effects [5].
Identification of Patient Groups and Better Disease Diagnosis
AI-powered healthcare initiatives must be mindful of ethical concerns surrounding patient data mining. While AI applications can help predict patient behaviour (like who is likely to miss appointments, skip screenings, or refuse treatments), they need to do so in a way that preserves patient privacy and medical information[3].
Another way AI developers are using medical data is to create personalised treatment plans for patients. By analysing a patient’s medical history, genetic data, and other factors, AI systems can identify the most effective treatment options for an individual patient. This personalised medicine can improve patient outcomes and reduce the risk of harmful side effects from treatment [4].
References
[1] Simon Alfano, Christian Amberg, Nils Peters, Pablo Salazar, Marianne Vieux-Rochas, Sam Welton; Treating rare diseases: How digital technologies can drive innovation, McKinsey February 27, 2023.
[2]Pharmaceutical Management Science Association. https://www.pmsa.org/jpmsa-vol08-article03
[3] Artificial intelligence in disease diagnosis: a systematic literature review, synthesising framework and future research agenda.https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8754556/
[4] Case Studies: The Growing Role of AI and Big Data in Healthcare.https://www.simplilearn.com/role-of-ai-and-big-data-in-healthcare-article
[5] How AI Developers Leverage Medical Data to Develop New Medical Technologies.https://www.medcase.health/blogs/how-ai-developers-leverage-medical-data-to-develop-new-medical-technologies
[6] Minaee et al.: Deep-COVID: Predicting COVID-19 from chest X-ray images using deep transfer learning, 2020
[7] A systematic review of artificial intelligence techniques in cancer prediction and diagnosis
Lucubrate Magazine March 2023
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