IA na área da saúde: 3 Principais Benefícios e Aplicações

The applications of AI in saúde are numerous and diverse. Inteligência Artificial e Aprendizagem da máquina are two of the biggest technology trends that the world is witnessing at this moment. Microsoft’s announcement of the Saúde NExT initiative is a harbinger of that, and marks the tech giant’s entry into cancer research.

Em seus eventos para a imprensa, o Vice-Presidente Corporativo da Microsoft Healthcare NExt, Peter Lee disse

"(Enfrentar problemas de saúde) é um desafio maior. Mas acreditamos que a tecnologia - especificamente a nuvem, a IA e as ferramentas de colaboração e otimização dos negócios - serão centrais para a transformação da saúde".

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.

Estes desenvolvimentos nos levam a acreditar que a IA na saúde se tornará a maior arena de inovação. Mesmo algumas conferências internacionais recentemente realizadas em AI e ML nos Estados Unidos, China, destacaram como é iminente uma gigantesca renovação digital na área da saúde.

Por que a IA em matéria de saúde

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 ensaios clínicos 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. Mais dados = Mais potência

Algumas das verticais de saúde onde o aprendizado de máquinas pode trazer mudanças notáveis incluem a visualização de enormes registros de testes laboratoriais para diagnósticos mais rápidos e precisos e o estudo dos padrões de dados dos pacientes para melhor compreender o prognóstico da doença. Isto melhorará a eficácia dos testes clínicos e economizará muito tempo para os prestadores de serviços de saúde, uma vez que McKinsey Inc. relatado recentemente. Isto significa que mais estudos de pesquisa clínica serão utilizados, mais ferramentas de visualização de dados serão desenvolvidas e mais ferramentas de gerenciamento de nuvens de dados serão necessárias.

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. 2. Melhor previsibilidade das adversidades repentinas da saúde

Inteligência Artificial 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, os psiquiatras também estão confiando sobre sistemas de IA para diagnóstico e prognóstico de doenças mentais. A IA ajuda a estudar padrões de comportamento e correlacionar os resultados com relatos de funcionamento do cérebro, placas de ressonância magnética local e padrões de envelhecimento celular para determinar que tipo de doenças neurodegenerativas estão disponíveis nos pacientes.

3. AI Reduzirá as Lacunas na Comunicação em Saúde

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, concluiu-se que até 2018, mais de 30% de médicos executarão ferramentas analíticas cognitivas sobre os dados dos pacientes antes de correlacionar os registros médicos por paciente com os dados do laboratório.

Sem dúvida, a IA provou ser a ferramenta que pode mudar a maneira como os dados fluem dentro dos sistemas de saúde, como esses dados são aplicados pelos provedores e acelerar as etapas-chave no diagnóstico do câncer. Um grupo de cientistas concluiu que o Aprendizado de Máquina é a ferramenta mais poderosa para prever a ocorrência de cancros em humanos cujos dados de TC e MRI já mostram lesões de grande porte. Os protocolos de monitoramento de doenças fatais contêm que o diagnóstico precoce é fundamental; se a menor quantidade de esforço puder ser usada para prever tumores precocemente, o aprendizado da máquina pode facilmente se tornar a ordem do dia para auxiliar no diagnóstico do câncer. O aprendizado de máquinas em conjunto com instrumentos convencionais de diagnóstico pode ser utilizado para melhor visualização da progressão do câncer e do funcionamento de máquinas nucleares. Mas o ponto onde o ML cria o efeito é sobre a aplicabilidade dos dados sem qualquer desfasamento no tempo. Os sistemas de saúde precisam desta eficiência, eles precisam deste aumento exponencial na facilidade de utilização e facilidade de comunicação e a Inteligência Artificial é de longe, a maneira mais eficaz de se conseguir isso.

Um novo começo para a saúde

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 genomics 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 doença hepática para câncer e mesmo psicose e esquizofrenia, 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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