AI models are the good, the bad and the ugly, all at once

This article generally aligns with a Center Left point of view. Learn more about this AI-generated analysis

The following AI-generated content is powered by Perplexity. The Los Angeles Times editorial staff does not create or edit the content.

Ideas expressed in the piece

• The article argues that artificial intelligence in healthcare is simultaneously promising and perilous, suggesting that its “good, bad and ugly” qualities coexist within the same systems rather than appearing in isolation.
• In describing the “Good,” the piece emphasizes AI as a pragmatic augmentation of human capability, highlighting examples such as AI-assisted mammography that can detect more cancers while significantly reducing radiologists’ workload, and tools that draft clinical notes or generate affordable market research so clinicians and small businesses can focus on judgment rather than paperwork or data-gathering. Similar benefits are documented in recent analyses showing AI can automate routine tasks, reduce clinician burden, improve diagnostic accuracy and support earlier disease detection^[1][3][6][9].
• Moreover, the article contends that the most responsible use of AI keeps humans genuinely “in the loop,” with models widening clinical judgment rather than substituting for it, aligning with findings that AI is most effective when it complements rather than replaces health professionals in areas such as imaging interpretation, risk prediction and patient monitoring^[7][8][12].
• The piece further notes that AI could unlock trillions of dollars in productivity by reducing wasteful healthcare spending, echoing studies that describe how AI may lower costs through more precise diagnostics, better resource allocation and streamlined administrative workflows^[5][10].
• Turning to the “Bad,” the article warns that the central risk is not machine malice but machine indifference, arguing that algorithms optimized narrowly for engagement, approval rates or cost containment will obediently pursue those goals even when they amplify discrimination, outrage or under-treatment, and framing this as a governance failure as much as a technical one. Reviews of AI in health care similarly point to risks of biased decision-making, inequitable outcomes and models that prioritize efficiency over patient welfare when objectives are poorly defined^[3][10][12].
• In this view, the article suggests that boards and institutions must treat AI oversight as a core fiduciary responsibility rather than a compliance afterthought, because leadership ultimately authorizes the objectives that drive predictable harms; related policy and ethics literature underscores that clarity about intended use, robust validation and explicit consideration of patient interests are essential to avoid unethical deployment^[3][11][14].
• Addressing the “Ugly,” the piece stresses that current models can hallucinate facts with full confidence, inherit biases from training data, and perform impressively in controlled settings yet stumble in real-world deployment, a pattern echoed in recent research documenting gaps between laboratory performance and clinical reliability, as well as persistent concerns over data quality, bias and generalizability^[3][10][12].
• At the same time, the article highlights an AI access divide, arguing that rural hospitals and smaller employers are often priced out of tools that large systems can readily adopt, even as evidence shows AI can improve early detection and operational efficiency; broader evaluations note that uneven access to advanced AI may deepen existing disparities unless deployment is consciously oriented toward low-resource and remote settings^[7][11][12].
• The piece uses a localized prostate cancer scenario to illustrate how AI can either clarify or distort choice: built well, AI makes trade-offs among surgery, radiotherapy and active monitoring legible so patients can weigh cancer control against urinary, sexual and bowel side effects, but built carelessly, it turns population-level averages into a verdict that ignores what an individual values. Narrative reviews of AI in clinical decision support similarly emphasize that models can help personalize care when they incorporate patient preferences, yet risk undermining autonomy when recommendations are treated as deterministic rather than context-dependent^[3][4][10].
• The article also notes that about one-third of U.S. adults now turn to AI for health information or advice, including for physical and mental health, arguing that the public has already become a key actor in how AI enters care. Parallel reporting on consumer use of digital health tools points to rising reliance on AI chatbots and symptom checkers, which can improve access to information but also raise concerns about accuracy and appropriate triage^[6][9].
• Consequently, the piece suggests that society’s task is to give AI the “code” the cinematic Wild West never had, calling for transparency about what models optimize for, rigorous testing before deployment, safeguards against foreseeable harm, and a constant habit of asking “best for whom?”; this aligns with proposals that emphasize clear communication of training data, intended use, performance and limitations as central pillars of trustworthy medical AI^[3][11][14].
• Ultimately, the article contends that because the good, bad and ugly traits of AI ship together in the same systems, the most critical choice is which traits institutions and users empower and which they deliberately constrain through design, governance and everyday practice.

Different views on the topic

• In contrast to the article’s emphasis on AI as a morally mixed “package,” some commentary highlights a more straightforwardly optimistic narrative, arguing that AI’s transformative potential to improve diagnostics, personalize treatment and relieve clinician workload should dominate the conversation, with governance challenges viewed as manageable technical problems rather than defining risks. Overviews from major academic and industry sources describe AI primarily as a tool that accelerates diagnosis, enhances patient outcomes and humanizes care by freeing clinicians from routine documentation, with relatively less focus on moral hazard or indifference to human values^{[1][2][4][8][13]}.
• Building on this perspective, certain analyses frame cost savings and system efficiency as paramount, suggesting that large-scale adoption of AI is necessary to address clinician shortages, rising chronic disease burdens and unsustainable expenditures, and that delaying deployment in the name of precaution could itself harm patients by perpetuating slower, less accurate care. Economic evaluations and technology reviews that project substantial reductions in healthcare spending and improvements in throughput often treat governance primarily as a means to enable rapid, safe scaling rather than as a central moral debate about which objectives are encoded in the models^[5][10][11].
• At the same time, some expert reviews take a more cautious stance than the article, contending that current AI systems should not be relied upon for high-stakes clinical decisions until there is much stronger evidence of real-world safety, robustness and fairness. These pieces stress that data quality issues, biased training sets, and limited external validation can lead to systematic misdiagnosis or unequal performance across populations, and argue that the pace of commercialization has outstripped the development of rigorous regulatory and evaluation frameworks^[3][10][12].
• From this more skeptical vantage point, commentators argue that the article may understate the risk that AI could erode human judgment and patient autonomy, warning that clinicians may become over-reliant on algorithmic recommendations and that patients could be nudged into decisions they do not fully understand. Reviews of AI decision support systems describe concerns about automation bias, lack of explainability and the possibility that opaque models will subtly reconfigure power dynamics in clinical encounters, shifting control away from patients and frontline clinicians toward institutions and vendors that set optimization targets^[3][10][14].
• Additionally, some transparency and ethics scholars argue that the article’s call for “code” in the form of transparency and safeguards is necessary but insufficient, insisting that many current medical AI products lack basic disclosure about training data, performance metrics and limitations, and that meaningful transparency must be paired with enforceable accountability and public oversight. Research on medical AI transparency emphasizes that simply stating what a model optimizes for does not resolve deeper questions about whether those objectives are appropriate, and calls for mechanisms such as auditability, independent testing and clear routes for contesting algorithmic decisions^[11][14].
• Finally, certain analyses of AI in healthcare place stronger emphasis than the article on structural inequities, arguing that without explicit redistributive policies, AI may primarily benefit well-resourced health systems and tech companies while widening gaps for rural clinics and marginalized communities. These critiques point out that many case studies of successful AI deployment occur in large academic centers with robust data and infrastructure, and caution that celebratory narratives about efficiency and precision can obscure the need for targeted investment and governance to ensure that underserved populations gain equal access to AI’s potential advantages^[7][9][12].