Artificial intelligence can assist doctors in finding the right treatments among many options for cancer, Capturing data from various databases relating to the condition, AI helps physicians identify & choose the right drugs for the right patients, AI supports the decision-making processes for existing drugs & expanded treatments for other conditions, as well as expediting the clinical trials process by finding the right patients from a number of data sources.
- AI in medicine is a broad domain that contains rich set topics like AI supported clinical decision making, bioinformatics, computational biology, NLP in medicine, biomedical intelligent devices and instruments, medical image processing, digital pathology, Machine learning algorithms and predictive models built for pharmacology, cognitive systems, remote healthcare management etc.
- Some usecases / examples can be found in the following links
Article Emotion Based Music Recommendation System Using Wearable Phy...
- https://www.researchgate.net/publication/325819958_Emotion_Recognition_via_Multi_Channel_EEG_Signal_Fusion_and_Pattern_Recognition
Thesis Emotion Aware Artificial Intelligence for Cognitive Systems
AI has the potential to detect the meaningful relationship among the data sets, which are extensively used in diagnosis, treatment and predict the result of the data analysis.
AI plays an important role in health care such as medical image analysis, brain tumor classification, bioinformatics by the use of neural networks based.
Following link might help you.
Article Multi-Grade Brain Tumor Classification using Deep CNN with E...
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Preprint INTELLECT MODELING KIT. TOXICOLOGY CONSULTING POINT. USER'S GUIDE
Article Adaptive learning with e-knowledge systems
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Konstantin M. Golubev
I would like to see block chain to anonymously track medical records and medication taken then use AI to see relationships/patterns between health and medication, location, visits etc. Do we have comprehensive medical history that joins everything together? What affects do multiple medications have on each other? Simple studies involving mortality by cohorts of drug use, diet, location, doctor visits, hospital stays, etc. If we anonymize the data then open it up to the public on Kaggle I think we would higher participation in information disclosure and in scientific trials. Too many trials have sampling bias or are looking to prove one thing. We could add all the features we can collect and see how our clustering algorithms group people. We could create an app where you take a picture of each of your meals before and after to get real diet data (not self reported diet data) this will also use a CNN/AI algorithm. We could also start recording exercises, more AI to group types of exercises, the aerobic vs anaerobic debate is still strong. Add this to medication data and we should start to shake off the old fruit of bad diet, exercise, and medication regimes the yield little results.
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"There is a growing sense of debate that medical AI could cause human doctors to lose their jobs (Lee, 2019). Since the doctoral function that can be replaced by AI is mainly limited to diagnoses at this stage, the opinion that doctors who make good use of AI would have a better chance of surviving seems to be a likely outcome (Lee, 2019). However, a considerable adjustment to the healthcare workforce also seems to be inevitable because healthcare institutions will continue to secure a competitive advantage through an AI’s economic efficiency in the fast-paced healthcare industry, even though ethical debates related to commercial exploitation of such technological advances continues (Lee, 2019)."
AI & ML methods are capable of revolutionizing our understanding of physiology. We do often possess abundant electronic data as ECG or EEG recordings but are unable to decode the underlying physiology and its changes from them.
This is going to be slowly changed. There is ongoing research based on complex systems theory and AI/ML-based searches for hidden interdependencies operating within our physiology. It is the next level of biomedical research.
The outputs of this type of research are basically twofold:
* Discovery of hidden interdependencies among studied data as, e.g., automatized detection of patients at risk fibrillation from ECG/optical signals of smart devices. One example is the Apple Watch.
* Prediction of physiology development in next tens of minutes or even hours. This is my research field. There is developed a software that is capable to predict Torsades de Pointes arrhythmias up to one hour prior to the onset of arrhythmias with sensitivity and specificity of 93% on a rabbit model.
It is a game-changing approach that enables us to save a lot of lives of diseased people, those taking pro-arrhythmia medical drugs, sportsmen, infants and guard those who are responsible for lives of many people such as drivers, pilots, train drivers, firemen, policemen, military personnel, etc.
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