Ethical concerns

The ethical concerns that many professionals express focus on biased outputs and data privacy. 

Bias. AI generates outputs based on algorithms that human beings develop and on information that human beings provide. But if an AI development team isn’t careful about how it trains its machine-learning protocols, the tool may generate outputs that favor one outcome over another. Those biases can render an AI system unreliable. This is, of course, a significant worry for law and tax professionals, who require access to utterly trustworthy legal and regulatory information.

Hallucinations. AI models that aren’t carefully developed may be vulnerable to hallucinations — outputs that deliver misinformation. This is probably one of the reasons why 50% of report respondents said that a lack of “demonstrable accuracy of AI-powered technologies” was a major barrier to their organization’s investment in these tools.

Data security. To provide reliable outputs, AI systems require access to large amounts of data, including sensitive personal information. But this understandably raises concerns about privacy violations, as security vulnerabilities can expose a company to financial penalties, legal difficulties, and reputational damage. 

Among those surveyed in the report, 42% cited a lack of demonstrable security as a barrier to AI investment in their organizations. Many professionals worry that AI systems might compromise sensitive data and make it publicly available. Professional organizations must ensure that they are conforming to data protection regulations and search for AI tools that prioritize data security.

Strategic concerns

Nearly two-thirds (65%) of respondents who have personal AI goals say they aren’t aware of their organization having an AI strategy. More generally, only 22% have a visible strategy. This disconnect can result in a lack of guidance, causing individual professionals to be inconsistent, inefficient, and even unintentionally unethical in their use of AI.

It’s also worth noting that 38% of professionals working for organizations that do have an AI strategy also reported that they don’t have any personal goals for AI adoption. Lacking objectives increases the risk that the organization won’t effectively implement its AI strategy.

Yet another concern that respondents noted in the report is that AI tools might be so good that organizations might rely too heavily on their use. They fear that this overreliance could hinder professional development, particularly when it comes to building the necessary skills for using AI effectively and ethically going forward. This technology is constantly evolving, after all.

Building AI literacy and professional resilience

Ethical and strategic concerns of AI are reasonable worries. But by addressing those concerns, professionals and the organizations they serve can develop competitive advantages in their market.

Strategic training leads to more successful adoption

Professionals are well aware that rapid technological advancements, evolving business needs, and shifting workforce demographics are constantly changing the way they conduct their practices. Professionals who can adopt AI systems effectively will gain a competitive edge, boosting both their personal impact and their organization’s long-term value.

Redazione RHC : 12 September 2025 17:58

Microsoft has begun testing new AI-powered features in File Explorer in Windows 11. These features will allow users to interact with images and documents directly from File Explorer, without having to open files in separate apps.

The new feature is called “AI Actions” and currently works with JPG, JPEG, and PNG images, allowing you to do the following:

• Remove Background in Paint: Quickly cut out the background of an image, leaving only the Subject;
• Remove Objects with the Photos app: Remove unwanted elements from photos using generative AI.
• Blur Background using the Photos app: Focuses on the subject while blurring the background.
• Search Images with Bing Visual Search: Visual Search with Bing finds similar images, objects, landmarks, and more across the web.

“AI Actions in File Explorer make working with files faster and easier—just right-click, for example, to edit an image or get a summary of a document,” say Microsoft representatives Amanda Langowski and Brandon LeBlanc.

These new features are available in Windows 11 Insider Preview Build 27938. Along with these, another useful feature has been introduced: under Settings > Privacy & Security > Text & Image Generation, you now see a list of third-party apps that have recently used Windows local generative AI models.

You can view this activity and manage these apps’ access to AI features.

In early May, Microsoft also introduced AI agents, intelligent assistants that can change Windows settings with a voice or text command. These features are now available on Copilot+ PCs and Snapdragon processors.

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Artificial intelligence (AI) is transforming the pharmaceutical industry. More and more, AI is being used in drug discovery to predict which drugs might work and speed up the whole development process.

But here’s something you probably didn’t see coming: some of the same AI tools that help find new drug candidates are now being used to catch insurance fraud. It’s an innovative cross-industry application that’s essential in protecting the integrity of healthcare systems.

AI’s Core Role in Drug Discovery

The field of drug discovery involves multiple stages, including initial compound screening and preclinical testing to clinical trials and regulatory framework compliance. These steps are time-consuming, expensive, and often risky. Traditional methods can take over a decade and cost billions, and success rates remain frustratingly low. This is where AI-powered drug discovery comes in.

The technology taps machine learning algorithms, deep learning, and advanced analytics so researchers can process vast amounts of molecular and clinical data. As such, pharmaceutical firms and biotech companies can reduce the cost and time required in traditional drug discovery processes.

AI trends in drug discovery cover a broad range of applications, too. For instance, specialized AI platforms for the life sciences are now used to enhance drug discovery workflows, streamline clinical trial analytics, and accelerate regulatory submissions by automating tasks like report reviews and literature screenings. This type of technology demonstrates how machine learning can automatically sift through hundreds of models to identify the optimal one that best fits the data, a process that is far more efficient than manual methods.

In the oncology segment, for example, it’s responsible for innovative precision medicine treatments that target specific genetic mutations in cancer patients. Similar approaches are used in studies for:

• Neurodegenerative diseases
• Cardiovascular diseases
• Chronic diseases  
• Metabolic diseases
• Infectious disease segments

Rapid development is critical in such fields, and AI offers great help in making the process more efficient. These applications will likely extend to emerging diseases as AI continues to evolve. Experts even predict that the AI drug discovery market will grow from around USD$1.5 billion in 2023 to between USD$20.30 billion by 2030. Advanced technologies, increased availability of healthcare data, and substantial investments in healthcare technology are the main drivers for its growth.

From Molecules to Fraud Patterns

So, how do AI-assisted drug discovery tools end up playing a role in insurance fraud detection? It’s all about pattern recognition. The AI-based tools used in drug optimization can analyze chemical structures and molecular libraries to find hidden correlations. In the insurance industry, the same capability can scan through patient populations, treatment claims, and medical records to identify suspicious billing or treatment patterns.

The applications in drug discovery often require processing terabytes of data from research institutions, contract research organizations, and pharmaceutical sectors. In fraud detection, the inputs are different—claims data, treatment histories, and reimbursement requests. The analytical methods remain similar, however. Both use unsupervised learning to flag anomalies and predictive analytics to forecast outcomes, whether that’s a promising therapeutic drug or a suspicious claim.

Practical Applications In and Out of the Lab

Let’s break down how this dual application works in real-world scenarios:

• In the lab: AI helps identify small-molecule drugs, perform high-throughput screening, and refine clinical trial designs. Using generation models and computational power, scientists can simulate trial outcomes and optimize patient recruitment strategies, leading to better trial outcomes and fewer delays and ensure drug safety.
• In insurance fraud detection: Advanced analytics can detect billing inconsistencies, unusual prescription patterns, or claims that don’t align with approved therapeutic product development pathways. It protects insurance systems from losing funds that could otherwise support genuine patients and innovative therapies.

This shared analytical backbone creates an environment for innovation that benefits both the pharmaceutical sector and healthcare insurers.

Challenges and Future Outlook

The integration of AI in drug discovery and insurance fraud detection is promising, but it comes with challenges. Patient data privacy, for instance, is a major concern for both applications, whether it’s clinical trial information or insurance claims data. The regulatory framework around healthcare data is constantly changing, and companies need to stay compliant across both pharmaceutical and insurance sectors.

On the fraud detection side, AI systems need to balance catching real fraud without flagging legitimate claims. False positives can delay patient care and create administrative headaches. Also, fraudsters are getting more sophisticated, so detection algorithms need constant updates to stay ahead.

Despite these hurdles, the market growth for these integrated solutions is expected to outpace other applications due to their dual benefits. With rising healthcare costs and more complex fraud schemes, insurance companies are under increasing pressure to protect their systems while still covering legitimate treatments.

Looking ahead, AI-driven fraud detection is likely to become more sophisticated as it learns from drug discovery patterns. And as healthcare fraud becomes more complex and treatment options expand, we can expect these cross-industry AI solutions to play an even bigger role in protecting healthcare dollars.

Final Thoughts

The crossover between AI drug discovery tools and insurance fraud detection shows how pattern recognition technology can solve problems across different industries. What started as a way to find new medicines is now helping catch fraudulent claims and protect healthcare dollars.

For patients, this dual approach means both faster access to new treatments and better protection of the insurance systems that help pay for their care. For the industry, it’s about getting more value from AI investments; the same technology that helps develop drugs can also stop fraud from draining resources. It’s a smart example of how one innovation can strengthen healthcare from multiple angles.