After more than 50 years of development, AI has become pervasive in the recent decade. These changes have sparked excitement and apprehension about the potential consequences of virtually all human endeavors. Aside from the most apparent AI uses among Silicon Valley's tech behemoths, it has begun to infiltrate healthcare and public health. AI applications in healthcare have been reported to match or outperform physicians in various domains, including radiology, dermatology, and pathology. Furthermore, some hospitals, such as New York University Langone Health's predictive analytics unit, have begun integrating AI into clinical workflow. While AI has received considerable attention in healthcare, its impact on public health has received less attention.
Given this, researchers and practitioners in the field of public health have begun to employ AI in a variety of projects, including scanning the internet for emerging outbreaks, predicting suicide using electronic health records, and identifying risk factors. As a result, there is growing optimism about AI's potential to improve public health; however, few AI systems have been implemented within public health organizations.
Moving forward, there are serious concerns about the effects of AI on privacy, interpretability, and the potential for bias. There has also been criticism that AI used in precision public health is simply a scaled-up version of the precision medicine approach. The potential to move beyond medicinal applications of AI to models containing rich characterizations of the social determinants of health has been noted as a promising yet mostly untapped frontier in AI. A better understanding of AI's relevance to public health is required to navigate the opportunities and risks, which is currently lacking in the literature.
Before useful applications can be widely deployed, barriers to AI adoption in public health must be overcome. Alack of leadership, relevant talent, and funds to pursue AI technologies have all been recognized as major challenges. Experts believe that teaching high-level machine learning principles to public health practitioners could help to jumpstart AI initiatives.
Stakeholders may also wish to consider targeted funding to develop AI initiatives in public health, such as programs for dual-trained AI and public health practitioners who can collaborate effectively with AI experts. Another source of concern is the scarcity of high-quality data. Experts mentioned electronic medical records, administrative databases, health surveys, social media, and news items with widely divergent formats and standards as examples of data sources of public health importance.
The lack of standardization (for example, widely disparate EMR software) and inconsistencies in data entry (for example, in free-form clinical notes) make linking datasets and deploying AI methods difficult. This could be mitigated by enacting legislation that encourages greater integration and access to relevant data and the adoption of common data standards.
AI can improve public health practice; however, many barriers remain, and risks must be better defined. According to some experts, AI has the potential to improve disease surveillance and health promotion interventions, and this should be the focus of future research and evaluation studies. Initiatives to increase AI expertise and funding for public health are required for successful AI implementation. Innovations in public health policy should improve the standardization, integration, and availability of relevant, high-quality data. More research is also required to determine the best AI use-cases, mitigate bias, and ensure a positive impact on health equity. Meanwhile, to combat hype, training initiatives for AI practitioners in public health should emphasize the limitations of AI.
The cyberattack surface in modern enterprise environments is massive, and it is rapidly expanding. This means that analyzing and improving a company's cybersecurity posture requires more than just human intervention. AI and machine learning are quickly becoming indispensable in information security, as these technologies are capable of rapidly analyzing millions of data sets and detecting a wide range of cyberthreats, from malware threats to shady behavior that may result in a phishing attack. These systems are always learning and improving, using data from previous and current attacks to identify new forms of attacks that could happen today or tomorrow.
AI offers numerous benefits and applications in various fields, one of which is cybersecurity. With today's rapidly evolving cyberattacks and rapid device proliferation, AI and machine learning can assist in keeping up with cybercriminals, automating threat detection, and responding more effectively than traditional software-driven or manual techniques. Here are some of the benefits and applications of using AI in cybersecurity:
It can be used to detect cyberthreats and potentially malicious behavior. Traditional software systems simply cannot keep up with the sheer volume of new malware created each week, so this is an area where Artificial Intelligence can be extremely useful. Artificial Intelligence systems are being trained to detect malware, run pattern recognition, and detect even the smallest behaviors of malware or ransomware attacks before they enter the system using sophisticated algorithms.
AI enables superior predictive intelligence through natural language processing, which curates data on its own by scraping articles, news, and cyber threat studies. This can provide information about new anomalies, cyberattacks, and prevention methods. After all, like everyone else, cybercriminals follow trends, so what's popular with them changes all the time. AI-based cybersecurity systems can provide the most up-to-date knowledge of global and industry-specific threats, allowing you to make more informed prioritization decisions based on what could be used to attack your systems and what is most likely to be used to attack your systems.
Bots account for a significant portion of internet traffic today, which can be dangerous. Bots can be a real threat, from account takeovers using stolen credentials to bogus account creation and data fraud. Manual responses will not suffice to combat automated threats. AI and machine learning assist in developing a comprehensive understanding of website traffic and distinguishing between good bots (such as search engine crawlers), bad bots, and humans. AI allows us to analyze massive amounts of data and allows cybersecurity teams to adapt their strategy to an ever-changing landscape.
AI systems assist in determining the IT asset inventory, which is a precise and detailed record of all devices, users, and applications with varying levels of access to various systems.
Taking into account the asset inventory and threat exposure (discussed above), AI-based systems can now predict how and where you are most likely to be compromised, allowing you to plan and allocate resources to areas with the greatest vulnerabilities.
Prescriptive insights derived from AI-based analysis allow you to configure and improve controls and processes to strengthen your cyber resilience.