The healthcare area is showing a huge interest towards Artificial Intelligence and especially for Machine Learning technologies capable to provide better instruments to the entire medical staff and a better customization of caregiving. The field of research is now projected into the use of Machine Learning techniques capable of improving diagnoses. This may lead to the identification of the patterns of a given pathology and, more generally, to provide better services to doctors, so that they can operate on individual patients through countermeasures adapted to specific needs. Furthermore, recent deep learning techniques could allow in the near future to have fully automated phases of therapies, allowing doctors and practitioners to devote more time to the study of new solutions. AI & Medicine What if a group of experts could always be at the service of an individual patient? How can we reduce the errors that can be made in making a diagnosis?In an ideal world, each therapy would be formulated by meeting the needs of the individual with extreme precision, taking cues both from his or her clinical history and from the collective knowledge given by millions of other patients. Thanks to Machine Learning, physicians can break the limits imposed by circumstantial knowledge, resulting from the application of therapies on a small number of patients, through the use of a “shared experience”, thanks to which they can formulate therapies optimized for a specific person . The adoption of artificial intelligence techniques can allow the total automation of some clinical steps, facilitating the work of specialists. Prognosis A prognosis is a process by which the development of a certain disease can be predicted. A machine learning model can allow doctors to predict future events: how likely is it that a patient will be able to return to everyday life after undergoing treatment? How quickly will a disease progress? An algorithmic model requires data to provide a complete picture, including the results of past treatments . In clinical area, different types of data will be collected, such as: phenotypic, genomic, proteomic and pathological test results, along with medical images . Diagnosis The best doctors are able to understand when a particular clinical event is actually normal or if it represents a risk for the patient’s health. The American Institute of Medicine has pointed out that all people in the course of their lives incur at least once in a misdiagnosis : reducing any kind of error can be crucial in the case of uncommon pathologies, without considering the fact that this can have a beneficial effect even in the case of diseases that are more familiar to us. Suffice it to say that complications thought to be eradicated such as tuberculosis and/or dysentery have at least a chance of going undetected, even though in developed countries there is adequate access to therapies capable of dealing with these dysfunctions . Through data collected during everyday therapies, AI techniques can identify the most likely diagnoses during a clinical visit and project what conditions will manifest themselves in the patient in the future . Treatment In a nationwide healthcare system, with thousands of physicians engaged with as many patients, variations might arise about how certain symptoms are treated. An ML algorithm can detect these natural variations in order to help doctors identify one treatment to be preferred over another . One application might be to compare what the doctor would prescribe to a patient with a treatment suggested by an algorithmic model . Workflows for physicians The introduction of electronic health records (EHRs) has facilitated access to data, but at the same time has brought out “bottlenecks” resulting from bureaucratic and administrative steps, creating additional complications for physicians. ML techniques can enable the streamlining of inefficient and cumbersome steps within the clinical workflow . The same technologies that are used in search engines can highlight relevant information in a patient’s medical record, facilitating the work of specialists. This also allows for further facilitation of new data entry by taking into account a subject’s clinical history . Involvement of more experts The adoption of artificial intelligence can give the possibility to reach more specialists who can provide a medical assessment without their direct involvement . For example: a patient could send a photo from his smartphone so that an immediate diagnosis can be obtained, without resorting to medical channels intended for more urgent cases . Machine Learning techniques commonly used in the medical field The most commonly used ML techniques in the medical literature will be discussed below. It is emphasized that here will be a treatment more oriented to the medical field, not delving into the technical aspects. Support Vector Machine The SVMs are mainly used to classify subjects within two groupings, having Y = 1 and Y = -1 as “label” respectively. These groupings are defined by a decision boundary defined by the input X data: The goal of SVM training is to find the optimal parameter w so that the classification is as accurate as possible (Figure 1). One of the most important properties of SVMs is that parameter determination is a convex optimization problem, so the solution is always a global optimum. Figure 1: An example of how a Support Vector Machine works . Convolutional Neural Network The growth of the computational capabilities of modern devices has allowed Deep Learning to become one of the most popular fields of research within various scientific disciplines: in this sense, medicine has known no exception . Thanks to these “deep” learning techniques (so called because of the numerous presence of layers, able to abstract very complex schematics), it is possible to perform a detailed analysis of medical images, such as X-ray scans, exploiting the ability of a neural network to manage voluminous and extremely complex data, such as images, in an efficient way . Over the years, Convolutional Neural Networks have gained enormous popularity, especially in the medical world: suffice it to say that from 2014 onwards, this particular type of neural network has supplanted methodologies such as Recurrent Neural Network and Deep Belief Neural Networks  (Figure 2). Figure 2: popularity of Deep Learning algorithms in the medical field . A CNN is based on the use of an operation called convolution, which can keep track of the various changes of a multidimensional data, having the position of a gauge within a space . Considering a two-dimensional image I, and a kernel K (a multidimensional array carrying the parameters learned by the algorithm): Figure 3 shows a broad structure of a CNN, while Figure 4 shows what is produced through the application of the convolution operation. Figure 3: Draft of a CNN . Figure 4: Result of some convolution operations. Random Forest The Random Forest is a technique that involves the use of multiple regressors, structured “tree” (i.e.: decision trees). Each tree expresses its own candidate through a classification algorithm: subsequently, the votes of all the trees are averaged. In the equation, the term B stands for individual bagging, i.e., decision tree trainings on different instances of the dataset . In the medical field, this type of technique can be used, for example, to discriminate phenotypic characters of an organism , or to classify the clinical data of a patient in such a way that an accurate diagnosis can be provided . Limitations of AI in the medical field Availability of quality data One of the central issues in building an ML model is being able to draw on a representative dataset of all possible subjects, so that it is as diverse as possible . The ideal would be to train algorithms using data very similar, if not identical, to those reported in electronic medical records : unfortunately, many times we will have to deal with small datasets, collected by small clinical centers, sometimes of poor quality (reporting noise, i.e. irregularities generated by erroneous data). Privacy As mentioned, having datasets consisting of correctly compiled medical records would be ideal. However, these data are considered sensitive in the eyes of current legislation, and therefore difficult to find, making it more difficult to outline ML models. A natural solution might be to hand over the clinical data to the patient himself, who will then decide what to do with it . Learning from past bad practices All human activities are unintentionally subjected to cognitive bias: some of the issues to consider when developing an ML system is to understand how much these biases, represented by the data, will affect the final model  and what tools to put in place to address this issue . Experience in final evaluation Similar to healthcare systems, the application of ML techniques requires a sophisticated regulatory framework that can ensure the right use of algorithms in the medical field . Physicians and patients must understand the limitations of these tools, such as the inability of a given framework to generalize to another type of issue . Blindly relying on ML models can lead to erroneous decisions: for example, a physician might let his guard down if the algorithm returns an incorrect result, below a certain alarm threshold . Interdisciplinary cooperation Teams of computer scientists, biologists, and physicians must collaborate so that they can build models that can be used in their respective fields. A lack of communication can lead to unusable results from physicians . Scientific publications are often published online as preprints on portals such as arXiv and bioRxiv, not to mention the multitude of computer manuscripts that are not published in traditional scientific journals but rather within conferences such as NeurIPS and ICML . AI techniques applied to COVID-19 Since the beginning of the SARS-CoV-2 virus pandemic, the scientific world has focused its attention on methods that can counteract the growth of infections: in March alone, 24000 preprints were published on the arXiv and bioRxiv portals concerning the use of AI techniques with the task of identifying patients affected by COVID-19 . Many authors have carried out works in which CNNs are used to discriminate COVID patients from others with more common diseases, using datasets consisting of X-ray scans of patients with pneumonia (Figure 5) . Figure 5: structure of a CNN for the detection of SARS-CoV-2 patients. In addition, several real-time methods have been proposed for the immediate detection of the disease: the use of smart-watches, for example, allows the monitoring of several physical parameters that can indicate whether the subject has contracted the virus or not . Conclusions Marco, a 49-year-old patient, felt a pain in his shoulder and, despite this, decided not to seek medical assistance. A few months later, he decides to see a doctor who diagnoses seborrheic keratosis (a skin growth similar to a large mole). Next, Marco undergoes a colonoscopy and a nurse notices a dark spot on his shoulder. Marco decides to visit a dermatologist, who obtains a sample of the excrescence: the analyses carried out show a benign pigment lesion. The dermatologist, however, does not trust him and decides to make a second analysis: this time the diagnosis speaks of an invasive melanoma. So, an oncologist subjects Marco to chemotherapeutic treatment but, in the meantime, a friend of the doctor asks the poor patient why he has not yet undergone immunotherapy . If Marco had access to the latest ML technology, he could have simply taken a picture of his shoulder via his smartphone and then forwarded the image to an experienced dermatologist via a dedicated app. Subsequent to a biopsy of the lesion, recommended by the dermatologist, a diagnosis of stage 1 melanoma would have been made: at that point, the dermatologist could have severed the lesion . The application of artificial intelligence in medicine will be able to save time, make better use of specialists’ know-how, enable more accurate diagnoses and, more generally, improve patients’ lives and streamline the work of practitioners. Pros: – More accurate diagnoses – Streamlining of medical and bureaucratic procedures – Acquisition of global therapeutic knowledge – Increased specialization of medical personnel Cons: – Availability of quality data – Lack of shared data collection procedures – Fundamental human component in the final evaluation of the diagnosis
 Rajkomar, A. – Dean, J. – Kohane, I.. “Machine Learning in Medicine.” New England Journal of Medicine, 380. 1347-1358. (2019)  Qayyum, A – Junaid Q. – Muhammad, B. – Ala A.. “Secure and Robust Machine Learning for Healthcare: A Survey.” IEEE reviews in biomedical engineering (2020)  McGlynn, E. – McDonald, K. – Cassel C..”Measurement is Essential for Improving Diagnosis and Reducing Diagnostic Error: A Report from the Institute of Medicine.” The Journal of the American Medical Association, Vol. 314(23), pp. 1-2 (2015)  Das, J. – Woskie, L. – Rajbhandari, R. – Abbasi, K. – Jha, A.. “Rethinking assumptions about delivery of healthcare: implications for universal health coverage.” The British Medical Journal (Clinical research ed.), Vol. 361 (2018)  Jiang, F. – Jiang, Y. – Zhi, H. – Dong, Y. – Li, H. – Ma, S. – Wang, Y. – Dong, Q. – Shen, H. – Wang, Y.. “Artificial Intelligence in Healthcare: Past, Present and Future.” Stroke and Vascular Neurology Vol. 2 (2017)  Mori, J. – Kaji S. – Kawai H. – Kida S. – Tsubokura M. – Fukatsu, M. – Harada, K. – Noji H. – Ikezoe T. – Maeda T. – Matsuda A.. “Assessment of dysplasia in bone marrow smear with convolutional neural network.” Scientific Reports Vol.10 (2020)  Goodfellow, I. – Bengio, Y. – Courville, A.. “Deep Learning.” MIT Press (2016)  Yamashita, R. – Nishio, M. – Do, R., – Togashi, K.. “Convolutional neural networks: an overview and application in radiology.” Insights into imaging, Vol. 9(4), pp. 611–629 (2018)  Breiman, L.. “Random Forests.” Machine Learning Vol.45(1), pp. 5 – 32 (2001)  Chen, T. – Cao, Y. – Zhang, Y. – Liu, J. – Bao, Y. – Wang, C. – Jia, W. – Zhao, A.. “Random forest in clinical metabolomics for phenotypic discrimination and biomarker selection.” Evidence-based complementary and alternative medicine : eCAM (2013)  Alam, Z. – Rahman, S. – Rahman, S.. “A Random Forest based predictor for medical data classification using feature ranking.” Informatics in Medicine Unlocked, Vol. 15, (2019)  Gianfrancesco, MA. – Tamang, S. – Yazdany, J. – Schmajuk, G.. “Potential Biases in Machine Learning Algorithms Using Electronic Health Record Data.” Journal Of American Medical Association, Internal Medicine, Vol.178(11), pp. 1544-1547 (2018)  Rajkomar, A. – Hardt, M. – Howell, M. D. – Corrado, G. – Chin, M. H.. “Ensuring Fairness in Machine Learning to Advance Health Equity.” Annals of internal medicine, Vol.169(12), pp. 866–872 (2018)  Krumholz, H. M.. “Big data and new knowledge in medicine: the thinking, training, and tools needed for a learning health system.” Health affairs (Project Hope), Vol.33(7), pp. 1163-70 (2014)  Hao, K.. ”Over 24,000 coronavirus research papers are now available in one place”. MIT Technology Review, URL: https://www.technologyreview.com/2020/03/16/905290/coronavirus-24000-research-papers-available-open-data/ (2020)  Jain, G. – Mittal, D. – Thakur, D. – Mittal, M. K.. “A deep learning approach to detect Covid-19 coronavirus with X-Ray images”. Biocybernetics and biomedical engineering, Vol.40(4), pp. 1391-1405 (2020)  Hemdan, E.E – Shouman, M. – Karar, M.. “COVIDX-Net: A Framework of Deep Learning Classifiers to Diagnose COVID-19 in X-Ray Images.” ArXiv, abs/2003.11055 (2020) Mishra, T. – Wang, M. – Metwally, A.A., Bogu G.K. – Brooks A. W. – Bahmani A. – Alavi A. – Celli A. –  Higgs, E. – Dagan-Rosenfeld O. – Fay, B. – Kirkpatrick, S. – Kellogg, R. – Gibson M. – Wang, T. – Hunting E. M. – Mamic P. – Ganz A. B. – Rolnik, B. – Li, X. – Snyder M. P.. “Pre-symptomatic detection of COVID-19 from smartwatch data.” Nature Biomedical Engineering (2020).