AI nella sanità: 3 Principali benefici e applicazioni

The applications of AI in assistenza sanitaria are numerous and diverse. Intelligenza artificiale e Apprendimento automatico are two of the biggest technology trends that the world is witnessing at this moment. Microsoft’s announcement of the Assistenza sanitaria NExT initiative is a harbinger of that, and marks the tech giant’s entry into cancer research.

Nei suoi eventi stampa, il vicepresidente aziendale di Microsoft Healthcare NExt, Peter Lee ha detto

"(Affrontare i problemi sanitari) è una sfida più grande. Ma crediamo che la tecnologia - in particolare il cloud, l'AI e gli strumenti di collaborazione e di ottimizzazione del business - sarà centrale per la trasformazione sanitaria".

Another patron at the tech biggie, Chris Bishop, further explained how healthcare is different compared to other industries, and that defeating cancer is this era’s biggest dilemma. Hence, the head honchos at Microsoft feel that applying technologies like Machine Learning and AI in healthcare is imperative for a smarter healthcare transformation.

Reviews of the current healthcare systems in various countries point misdiagnoses and delayed treatments to be the most immediate concerns. Microsoft plans to utilize machine learning systems, cloud storage and business optimization tools to fix these fundamental problems at healthcare facilities. It also looks forward to advancing its role in cancer treatment research, using out-of-the-box approaches. Microsoft’s experts believe that cancer can be treated in a similar fashion as computer viruses or software glitches are removed.

Questi sviluppi ci portano a credere che l'AI nella sanità diventerà la più grande arena di innovazione. Anche alcune conferenze internazionali di AI e ML tenutesi di recente negli Stati Uniti, in Cina, hanno evidenziato come sia imminente un gigantesco rinnovamento dell'assistenza sanitaria digitale.

Perché l'IA nella sanità è importante

It’s no secret that medical research is the most critical area where the data generated is enormous and of the highest value. So, the need for highest efficient data handling systems isn’t surprising, considering not just patient safety and compliance norms but also for the efficient management of studi clinici and emergency cases. Hospitals, research organizations and healthcare aid societies are aware of the various ways in which AI can change the face of healthcare, inside organizations as well as outside. However, it is surprising to note is that only few healthcare agencies are openly integrating Machine Learning and AI into their systems.

The massive overhaul of healthcare systems that AI can bring in such a short span of time is commonly spoken about but not yet witnessed in reality. The computational power of AI is important for healthcare organizations to notice, for it’s the only field that is lagging behind. There is a need for healthcare professionals to openly discuss all the dimensions in which AI and ML can help reduce mishaps, such as increased accuracy in data entries without human intervention, monitor in-patient stats for critically ill patients, etc.

1. Più dati = più potenza

Alcuni dei verticali sanitari in cui l'apprendimento automatico può portare notevoli cambiamenti includono la visualizzazione di enormi record di test di laboratorio per diagnosi più veloci e accurate e lo studio dei modelli di dati dei pazienti per comprendere meglio la prognosi della malattia. Questo migliorerà l'efficacia degli studi clinici e farà risparmiare molto tempo agli operatori sanitari, come McKinsey Inc. riportato di recente. Questo significa che saranno utilizzati più studi di ricerca clinica, saranno sviluppati più strumenti di visualizzazione dei dati e saranno necessari più strumenti di gestione della nuvola di dati.

This also indicates the need for better automation of clinical data handling systems, which will save a lot of expenses for pharma companies, hospitals, care centres and clinical research organizations. The reason for the improved accuracy and speed is that machine learning incrementally works better. The more clinical data that’s fed into the system, more accurate is the diagnosis. Once patient data handling systems are automated, machine learning systems can be incorporated and healthcare organizations will definitely have smoother processes.

2. Migliore prevedibilità delle avversità improvvise di salute

Intelligenza artificiale aids in understanding diseases better, analyzing patient-specific disease characteristics and gauging the course of treatment and its effectiveness. it is a powerful tool to monitor disease progression through set parameters. One of the surprising areas of machine learning research is studying heart diseases. Scientists are currently exploring the risk factors for degenerative heart diseases, including predictions of heart attacks using available machine learning tactics.

A group of scientists at the University of Nottingham in the United Kingdom are collaborating with cardiologists at Carnegie Mellon University to study AI algorithms for predicting the occurrence of heart attacks. Their sample data consist of patients with and without cardiologic medicine prescriptions. They are proposing new theories to indicate the risk factors for cardiac arrests outside the usual list of parameters, such as age and previous heart disease diagnosis. Such a groundbreaking study would be detrimental in the issuing of drugs to varying patient populations and also decide how drug dosages are monitored.

Data modelling methods with AI integration can also be applied to studying course of disease in case of infectious diseases, HIV-AIDS and cancer, among others. In fact, gli psichiatri si affidano anche sui sistemi AI per la diagnosi e la prognosi delle malattie mentali. L'IA aiuta a studiare i modelli comportamentali e a correlare i risultati con i rapporti sul funzionamento del cervello, le placche MRI locali e i modelli di invecchiamento delle cellule per determinare che tipo di malattie neurodegenerative sono in arrivo nei pazienti.

3. L'AI ridurrà le lacune nella comunicazione sanitaria

The fact that Artificial Intelligence itself developed out of the vastness of Big Data is overwhelming and the way data of humans is expanding, AI and ML seem to be the obvious choice to fully use these data. AI engineers are more involved in creating better tools to visualize medical data now than ever before and the results are of most use in behavioural science. In fact, at a recent conference, si è concluso che entro il 2018, oltre 30% dei medici eseguirà strumenti di analisi cognitiva sui dati dei pazienti prima di correlare le cartelle cliniche per paziente con i dati di laboratorio.

Indubbiamente, l'IA ha dimostrato di essere lo strumento che può cambiare il modo in cui i dati fluiscono all'interno dei sistemi sanitari, come questi dati sono applicati dai fornitori e accelerare i passaggi chiave nella diagnosi del cancro. Un gruppo di scienziati ha concluso che il Machine Learning è lo strumento più potente per prevedere l'insorgenza di tumori in humans whose CT and MRI data already show sizable lesions. The fatal disease monitoring protocols contain that early diagnosis is key; if the least amount of effort can be used to predict tumours early, machine learning can easily become the order of the day for aiding cancer diagnosis. Machine learning functions in concert with conventional diagnostic instruments can be utilized for better visualization of cancer progression and functioning of nuclear machinery. But the point where ML creates the effect is on the applicability of data without any time-lags. Healthcare systems need this efficiency, they need this exponential rise in user-friendliness and ease of communication and Intelligenza artificiale is by far, the most effective way to achieve that.

Un nuovo inizio per la sanità

Not too long ago, Artificial Intelligence was touted as the new horizon of technology and the zenith of information processing efficiency, but now AI is definitely much more than that. Since the emergence of a full-blown AI system in 2010 — IBM Watson to this year’s Healthcare NExT, AI’s significance has had a meteoric rise. The intelligence and effectiveness of this technology essentially mark evidence of the fact that AI in healthcare has a bright future ahead. Today, IBM Watson integrates genomica e oncologia solutions in its interface that are applied to accelerate access to better healthcare by being the most powerful and efficient communication bridge. It helps patient access clinical knowledge and information more interactively. It has increased sensitivity to patient concerns, improved on understanding relevance and has reduced information processing speeds to a tenth of a millionth second.

Prevalent market research firms, like Frost and Sullivan, have predicted the high-speed expansion of AI systems in healthcare even for small and medium enterprises. Even Microsoft’s Lee recent statement seconds that, as Microsoft aims to “help each human and company experience the most groundbreaking AI solutions” to have a healthier future. Bernard Marr, the Forbes Contributor of Health, wrote “From malattia del fegato a cancro e anche psicosi e schizofrenia, AI algorithms are changing the game in terms of disease diagnosis. ” Hence, it is not too far a time when we interact with bots to know the status of physician appointments at a clinic nearby and even medical students operate machine learning systems to complete small tasks inside the OT. So medical students will learn more about data sciences and engineers will code more for evolved machine learning systems, most importantly!

Well, we’re just getting started!

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