Artificial intelligence (AI) has avoided the headlines until now, yet it has been with us for 75 years [1, 2]. Still, few understand what it really is and many feel uncomfortable about its rapid growth, with thoughts going back to the computer rebelling against the human crew onboard the spaceship heading out into the infinity of space in Arthur C. Clarke’s visionary novel “2001: a Space Odyssey” [3]. Just as in the novel, there is no way back since the human mind cannot continuously operate at an unwavering level of accuracy or simultaneous interact with different sections of large-scale information (Big Data), areas where AI excels. The World Economic Forum has made a call for a faster adoption of AI in the field of healthcare, a fact discussed at length in a very recent white-paper report [4] arguing that progress is not forthcoming as fast as expected despite the evident potential for growth and innovation at an all-time high and strong demand for new types of computer processors. Among the reasons mentioned for the slow uptake in areas dealing with healthcare are barriers, such as complexity deterring policymakers, and the risk for misaligned technical and strategic decisions due to fragmented regulations [4].

The growing importance of AI in the medical and veterinary fields strengthened by recent articles and editorials published in The Lancet Digital Health and The Lancet [5, 6] underlining actual and potential roles of AI in healthcare. We survey this wide spectrum highlighting current gaps in the understanding of AI and how its application can assist clinical work as well as support and accelerate basic research.

AI technology development

From rules to autonomy

Before elaborating on these issues, some basic informatics about the technology that has moved AI to the fore is in order. In 1968, when both the film and the novel were released, only stationary, primitive computers existed. Rather than undergoing development in the preserve of large companies and academic institutions, they morphed into today’s public laptops, smartphones and wearable sensor networks. The next turn came with the gaming industry’s insatiable need for ultra-rapid action and life-like characters necessitating massively parallel computing, which led to switching from general-purpose, central processor units (CPUs) to specialized graphics processors (GPUs) and tensor processors (TPUs). Fuelled by this expansion of the processor architecture, neural networks, machine learning and elaborate algorithms capable of changing in conjunction with new data (meta-learning) were ushered in, with the rise of the power to understand and respond to human language through generative, pre-trained transformation (GPT) [7] showing the way forward. Breaking out of rule-based computing by the emergent capability of modifying internal settings, adapting to new information and understanding changing environments put these flexible systems, now referred to as AI, in the fast lane towards domains requiring high-level functionality. Computer systems adapted to a wide range of tasks, for which they were not explicitly programmed, could then be developed and launched into the public area as exemplified by automated industrial production, self-driving vehicles, virtual assistants and chatbots. Although lacking the imagination and versatility that characterize the human mind, AI can indeed perform tasks partly based on reasoning and planning that typically require human cognitive functions, and with enhanced efficiency and productivity.

Agent-based AI

Here, the agent is any entity that can perceive its environment, make decisions and act toward some goal, where rule-based AI has been replaced with proactive interaction. Agent-based AI generally uses many agents working separately to solve joint problems or even collaborating like a team. This approach was popularized by Wooldridge and Jennings in the 1990s, who described decentralized, autonomous AI systems capable of ‘meta-learning’ [8]. They felt that outside targets can be in sanitated and dealt with as computational objects, a methodology that has advanced the study of polarization, traffic flow, spread of disease, and similar phenomena. Although technology did not catch up with this vision until much later, AI today encompasses a vital area of active research producing powerful tools for simulating complex distributed and adaptive systems. The great potential of this approach for disease distributions and transmission dynamics may provide the insights needed to successfully control the neglected tropical diseases (NTDs) as well as dealing with other challenges in the geospatial health sphere [9]. The Internet of Things (IoT) [10], another example agent-based AI, represents the convergence of embedded sensors and software enabling collection and exchanging data with other devices and systems; however, operations are often local and do not necessarily involve the Internet.

While the rule-based method follows a set of rules and therefore produces an outcome which is to some degree predictable, the two new components in the agent-based approach include the capability of learning from experience and testing various outcomes by one or several models. This introduces a level of reasoning, which allows for non-human choice, as schematically shown in Fig. 1.

AI applications

Clinical applications

Contrary to common belief, a diagnostic program that today would be sorted under the heading AI was designed already 50 years ago at Stanford University, California, United States of America. The system, called MYCIN [11], was aimed to assist physicians with regard to bacterial blood infections. It was originally produced in book format, utilized a knowledge base of approximately 600 rules and operated through a series of questions to the user ultimately providing diagnosis and treatment recommendation. In the United States, similar approaches aimed at the diagnoses of bacterial infections appeared in the following decades but were not often used due to lack of computational power at the time. Today, on the other hand, this is no longer the limiting factor and AI is revolutionizing image-based diagnostics. In addition to the extensive use of AI-powered microscopy in parasitology, the spectrum includes both microscopic differentiation between healthy and cancerous tissue in microscope sections [12], as well as interpretations of graphs and videos from electrocardiography (EKG) [13], computer tomography (CT) [14, 15], magnet resonance imaging (MRI) [15] and ultrasonography [16]

Some AI-based companies are doing well, e.g., ACL Digital (https://www.acldigital.com/) that analyzes data from wearable sensors detecting heart arrhythmias, hypertension, sleep disorders; AIdoc (https://www.aidoc.com/eu/) whose platform evaluates clinical examinations and coordinates workflows beyond diagnosis; and the da Vinci Surgical System (https://en.wikipedia.org/wiki/Da_Vinci_Surgical_System), which has been used for various interventions, including kidney and hysterectiomy [17, 18]. However, others have failed, e.g., ‘Watson for Oncology’, launched by IBM for cancer diagnosis and optimized chemotherapy (https://www.henricodolfing.com/2024/12/case-study-ibm-watson-for-oncology-failure.html) and Babylon Health (https://en.wikipedia.org/wiki/Babylon_Health), a tele-health service that connected people to doctors via video calls, offered wholesale health promotion with high precision and virtual health assistants (Chatbots) that even remind patients to take medication. These final examples of AI-assisted medicine show that strong regulation is needed before this kind of assistance can be released for public use.

Basic research

The focus in the 2024 Nobel ceremony granted AI a central role: while the Physics Prize was awarded for the development of associative neural networks, the Chemistry Prize honored the breakthrough findings regarding how strings of amino acids fold into particular shapes [19]. This thorny problem was cracked by AlphaFold2, a robot based on deep-learning developed at DeepMind, a company that now belongs to Google’s parent Alphabet Inc. The finding that all proteins share the same folding process widened the research scope making it possible to design novel proteins with specific functions (synthetic biology), accelerate drug discovery and shed light on how diseases arise through mutations. The team that created this robot as its current sight on finding out how proteins interact with the rest of the cellular machinery. AlphaFold3, an updated version of the architecture generates accurate, three-dimensional molecular structures by pair-wise interaction between molecular components, which can be used to model how specific proteins work in union with other cell components exposing the details of protein interaction. These new applications highlight the exponential rise of AI’s significance for research in general and for medicine in particular.

The solution to the protein-folding problem not only reflects the importance of the training component but also demonstrates that AI is not as restricted as the human mind is when it comes to large realms of information (Big Data), which is needed for a large number of activities in modern society, such as autonomous driving, large-scale financial transactions as dealt with in banks on a daily basis. Big Data is common also in healthcare and it involves not only when dealing with hospital management and patient records, but also with large-sale diagnostic approaches. An academic paper, co-authored with clinicians and Google Research, investigated the reliability of diagnostic AI system, finding that machine learning reduced the number of false positives in a large mammography dataset by 25% (and also reached conclusions considerably faster), compared with the standard, clinical workflow without missing any true positives [20], a reassuring result.

Epidemiological surveillance

AI tools have been widely applied in epidemiological surveillance of vector-borne diseases. Due to vectors’ sensitivity to temperature and precipitation, the arthropod vectors are bellwether indicators, not only for the diseases they often carry but also for climate change. By gaining deeper insights into the complex interactions between climate, ecosystems and parasitic diseases with intricate life cycles, AI technologies assist by handling Big Data and even using reasoning to deal with obscure variations and interactions of climate and biological variables. To keep abreast of this situation, the connections between human, animal and environmental health not only demand data-sharing at the local level but also nationally and globally. This move towards the One Health/Planetary Health approach is highly desirable, and AI will unquestionably be needed for sustaining diligence with respect to the Big Data repositories required for accurate predictions of disease transmission, while AI-driven platforms can further facilitate real-time information exchange between stakeholders, optimize energy consumption and improve resource management for infections in animals and humans, in particular with regard to parasitic infections [21]. Proactive synergies between public health and other disciplines, such as ecology, genomics, proteomics, bioinformatics, sanitary engineering and socio-economy make the future medical agenda not only exciting and challenging, but also highly relevant globally.

In epidemiology, there has been a strong advance across the fields of medical and veterinary sciences [22], while previously overlooked events and unusual patterns now stand a better chance of being picked up by AI analysis of indirect methods, e.g., phone tracing, social media posts, news articles and health records. Technically less complex, but no less innovative operations are required to update the roadmap for elimination of the NTDs issued by the World Health Organization (WHO) [23]. The Expanded Special Project for the Elimination of Neglected Tropical Diseases (ESPEN) is a collaborative effort between the WHO regional office for Africa, member states and NTD partners. Its portal [24] offers visualization and planning tools based on satellite-generated imagery, climate data and historical disease patterns that are likely to identify high-risk areas for targeted interventions and allocate resources effectively. In this way, WHO’s roadmap for NTD elimination is becoming more data-driven, precise and scalable, thereby accelerating progress.

The publication records

Established as far back as 1993, Artificial Intelligence Research was the first journal specifically focused on AI, soon followed by an avalanche of similar ones (https://www.scimagojr.com/journalrank.php?category=1702). China, India and United States are particularly active in AI-related research. According to the Artificial Intelligence Index Report 2024 [25], the total number of general AI publications had risen from approximately 88,000 in 2010 to more than 240,000 in 2022, with publications on machine learning increasing nearly sevenfold since 2015. If also conference papers and repository publications (such as arXiv) are included along with papers in both English and Chinese, the number rises to 900,000, with the great majority originating in China [26].

A literature search based solely on PubMed, carried out by the end of 2024 by us using “AI and infectious disease(s)” as search term resulted in close to 100,000 entries, while the term “Advanced AI and infectious disease(s)” only resulted in about 6600. The idea was to find the distintion between simpler, more rule-based applications and proper AI. Naturally, the results of this kind can be grossly misleading as information on the exact type of computer processor used, be it CPU, GPU or TPU, is generally absent and can only be inferred. Nevertheless, the much lower figure for “Advanced AI and infectious disease(s)” is an indication of the preference for less complex AI applications so far, i.e. work including spatial statistics and comparisons between various sets of variables vis-à-vis diseases, aiming at estimating distributions, hotspots, vector breeding sites, etc.

With as many as 100,000 medical publications found in the PubMed search, they clearly dominate in relation to the total of more than 240,000 AI-assisted research papers found up to 2022 [25]. The growing importance of this field is further strengthened by recent articles and editorials [27, 6]. Part of this interest is probably due to the wide spectrum of the medical and veterinary fields and AI’s potential in tracing and signalling disease outbreaks plus its growing role in surveillance that has led to a surge of publications on machine learning, offering innovative solutions to some of the most pressing challenges facing health research today [28].

Investing in top growth stocks is a great way to achieve strong returns and potentially outperform the market as a whole. The S&P 500 is an index of the leading companies on the U.S. markets, and historically, it has risen by 10% per year, though that’s an average including up and down years. That return is not guaranteed, but at such a high rate, an investment would double after a little more than seven years.

One artificial intelligence (AI) stock that has routinely outperformed the broad index is Broadcom (AVGO -1.12%).

The semiconductor and infrastructure company has benefited from the growth in tech in recent years, and that has allowed it to outperform the market on a consistent basis. With strong gains once again so fare this year, is Broadcom still a great buy, or could it be due for a pullback?

Broadcom has been a top growth stock over the past decade

Here’s a look at just how well Broadcom has performed over the previous 10 years, compared to the S&P 500.

Data source: YCharts.

What’s surprising is that the one year when the S&P 500 did better than Broadcom was 2019, when the index finished higher at nearly 29%, versus 24% gains for Broadcom.

The past doesn’t predict the future, but the tech stock’s terrific run can’t be ignored. In 10 years, shares of Broadcom have risen by more than 2,000%, while the S&P 500 has increased by around 200%.

Can Broadcom’s impressive gains continue?

As of the end of last week, Broadcom’s stock was up around 19% for the year, which was comfortably above the S&P 500’s returns of more than 6%. But with a valuation of around $1.3 trillion and Broadcom trading at 33 times its estimated future earnings (based on analyst estimates), it’s not a cheap stock to own.

The biggest risk is that the company relies heavily on demand from hyperscalers. These are big tech giants that have significant infrastructure needs related to tech and AI. If they scale back on their expenditures, that could significantly weigh on Broadcom’s results. The company estimates that its top five customers account for around 40% of its revenue.

The company’s revenue during the most recent reported period — which ended on May 4 — grew by a rate of 20% year over year, as its top line came in at just over $15 billion, while profits more than doubled, rising to nearly $5 billion.

If Broadcom can continue producing strong results such as these, it wouldn’t be surprising to see it outperform the market once again this year. Though that risk of hyperscalers cutting spending remains.

Is Broadcom stock a buy right now?

If you’re bullish on AI and expect there to be much more growth ahead, Broadcom can make for a compelling investment to simply buy and hold. But at the same time, it’s also important to consider the risks ahead, especially as tariffs and trade wars could impact growth in the tech sector in the near future.

Earlier this year, Broadcom’s stock was underperforming the S&P 500 due to the uncertainty in the markets. While that looks like a distant memory right now, investors should brace for a possible slowdown for the stock as it’s trading at an elevated valuation and it may be due for a decline. Its track record may be impressive, but that by no means guarantees it’ll always be a market-beating stock.

I’d hold off on buying shares of Broadcom only because the markets appear to be a bit too bullish right now, and with high expectations priced in, there’s a lot of downside risk that comes with owning the stock. Broadcom isn’t a bad buy, but I think there are better AI stocks to invest in today.

This kind of rapid diagnosis is one of the big promises of artificial intelligence for use in health care. Proponents of the technology say that over the coming decades, AI has the potential to save hundreds of thousands, even millions of lives.

What’s more, a 2023 study found that if the health care industry significantly increased its use of AI, up to US$360 billion annually could be saved.

WATCH: How artificial intelligence impacted our lives in 2024 and what’s next

But though artificial intelligence has become nearly ubiquitous, from smartphones to chatbots to self-driving cars, its impact on health care so far has been relatively low.

A 2024 American Medical Association survey found that 66% of U.S. physicians had used AI tools in some capacity, up from 38% in 2023. But most of it was for administrative or low-risk support. And although 43% of U.S. health care organizations had added or expanded AI use in 2024, many implementations are still exploratory, particularly when it comes to medical decisions and diagnoses.

I’m a professor and researcher who studies AI and health care analytics. I’ll try to explain why AI’s growth will be gradual, and how technical limitations and ethical concerns stand in the way of AI’s widespread adoption by the medical industry.

Inaccurate diagnoses, racial bias

Artificial intelligence excels at finding patterns in large sets of data. In medicine, these patterns could signal early signs of disease that a human physician might overlook – or indicate the best treatment option, based on how other patients with similar symptoms and backgrounds responded. Ultimately, this will lead to faster, more accurate diagnoses and more personalized care.

AI can also help hospitals run more efficiently by analyzing workflows, predicting staffing needs and scheduling surgeries so that precious resources, such as operating rooms, are used most effectively. By streamlining tasks that take hours of human effort, AI can let health care professionals focus more on direct patient care.

WATCH: What to know about an AI transcription tool that ‘hallucinates’ medical interactions

But for all its power, AI can make mistakes. Although these systems are trained on data from real patients, they can struggle when encountering something unusual, or when data doesn’t perfectly match the patient in front of them.

As a result, AI doesn’t always give an accurate diagnosis. This problem is called algorithmic drift – when AI systems perform well in controlled settings but lose accuracy in real-world situations.

Racial and ethnic bias is another issue. If data includes bias because it doesn’t include enough patients of certain racial or ethnic groups, then AI might give inaccurate recommendations for them, leading to misdiagnoses. Some evidence suggests this has already happened.

Humans and AI are beginning to work together at this Florida hospital.

Data-sharing concerns, unrealistic expectations

Health care systems are labyrinthian in their complexity. The prospect of integrating artificial intelligence into existing workflows is daunting; introducing a new technology like AI disrupts daily routines. Staff will need extra training to use AI tools effectively. Many hospitals, clinics and doctor’s offices simply don’t have the time, personnel, money or will to implement AI.

Also, many cutting-edge AI systems operate as opaque “black boxes.” They churn out recommendations, but even its developers might struggle to fully explain how. This opacity clashes with the needs of medicine, where decisions demand justification.

WATCH: As artificial intelligence rapidly advances, experts debate level of threat to humanity

But developers are often reluctant to disclose their proprietary algorithms or data sources, both to protect intellectual property and because the complexity can be hard to distill. The lack of transparency feeds skepticism among practitioners, which then slows regulatory approval and erodes trust in AI outputs. Many experts argue that transparency is not just an ethical nicety but a practical necessity for adoption in health care settings.

There are also privacy concerns; data sharing could threaten patient confidentiality. To train algorithms or make predictions, medical AI systems often require huge amounts of patient data. If not handled properly, AI could expose sensitive health information, whether through data breaches or unintended use of patient records.

For instance, a clinician using a cloud-based AI assistant to draft a note must ensure no unauthorized party can access that patient’s data. U.S. regulations such as the HIPAA law impose strict rules on health data sharing, which means AI developers need robust safeguards.

WATCH: How Russia is using artificial intelligence to interfere in election | PBS News

Privacy concerns also extend to patients’ trust: If people fear their medical data might be misused by an algorithm, they may be less forthcoming or even refuse AI-guided care.

The grand promise of AI is a formidable barrier in itself. Expectations are tremendous. AI is often portrayed as a magical solution that can diagnose any disease and revolutionize the health care industry overnight. Unrealistic assumptions like that often lead to disappointment. AI may not immediately deliver on its promises.

Finally, developing an AI system that works well involves a lot of trial and error. AI systems must go through rigorous testing to make certain they’re safe and effective. This takes years, and even after a system is approved, adjustments may be needed as it encounters new types of data and real-world situations.

AI could rapidly accelerate the discovery of new medications.

Incremental change

Today, hospitals are rapidly adopting AI scribes that listen during patient visits and automatically draft clinical notes, reducing paperwork and letting physicians spend more time with patients. Surveys show over 20% of physicians now use AI for writing progress notes or discharge summaries. AI is also becoming a quiet force in administrative work. Hospitals deploy AI chatbots to handle appointment scheduling, triage common patient questions and translate languages in real time.

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Clinical uses of AI exist but are more limited. At some hospitals, AI is a second eye for radiologists looking for early signs of disease. But physicians are still reluctant to hand decisions over to machines; only about 12% of them currently rely on AI for diagnostic help.

Suffice to say that health care’s transition to AI will be incremental. Emerging technologies need time to mature, and the short-term needs of health care still outweigh long-term gains. In the meantime, AI’s potential to treat millions and save trillions awaits.

This article is republished from The Conversation under a Creative Commons license. Read the original article.