arXiv subjects

Dipayan Sengupta

Publications and source records attributed to Dipayan Sengupta.

4 recordsLinked to original sources

arXiv2026

How Indian Dermatologists are Utilizing Artificial Intelligence for Clinical Practice and Workflow Management: A Nationwide Survey with a Special Focus on atopic dermatitis

Background: Dermatology AI has mainly focused on image-based diagnosis, while chronic disease workflows have received less attention. We surveyed Indian dermatologists to map routine clinical challenges, with a focus on atopic dermatitis (AD), and assess current AI use. Methods: A nationwide cross-sectional survey commissioned by the Society for Eczema Studies included 377 practicing Indian dermatologists. The survey assessed clinical challenges, AD workflow barriers, AI use, adoption barriers, and ethical concerns. Analyses used descriptive statistics, chi-square tests, false discovery rate correction, and multivariable logistic regression. Results: Patient adherence (61.3%) and treatment planning in difficult or refractory cases (57.0%) were reported more often than diagnostic uncertainty (48.0%). In AD care, severity scoring was reported as a challenge by 47.7% and had the lowest satisfaction among measured workflow areas. Current AI use was reported by 49.9%, most often involving general large language models for literature synthesis, documentation, and academic tasks rather than specialized image analysis. Barriers differed by experience: dermatologists with more than 20 years of practice more often cited lack of training, while those with 5 years or less more often cited lack of clinical utility after trying AI tools. AI users were more likely than non-users to report concern about patient self-misdiagnosis and anxiety, which remained significant after adjustment for experience and academic affiliation. Conclusion: Respondents reported using general-purpose AI mainly for cognitive and administrative tasks, while their clinical needs centered on chronic disease management and AD workflow support. Clinician-supervised workflow tools may be more useful than standalone diagnostic applications.

cs.CY↗

arXiv2026

How Clinicians Think and What AI Can Learn From It

Most clinical AI systems operate as prediction engines -- producing labels or risk scores -- yet real clinical reasoning is a time-bounded, sequential control problem under uncertainty. Clinicians interleave information gathering with irreversible actions, guided by regret, constraints and patient values. We argue that the dominant computational substrate of clinician reasoning is not cardinal optimization but ordinal, non-compensatory decision-making: Clinicians frequently rely on fast-and-frugal, lexicographic heuristics (e.g., fast-and-frugal trees) that stop early after checking a small, fixed sequence of cues. We provide a normative rationale for why such algorithms are not merely bounded rationality shortcuts, but can be epistemically preferred in medicine. First, many clinical trade-offs are constructed through human judgment and are only weakly measurable on absolute scales; without strong measurement axioms, only orderings are invariant, motivating an ordinal-by-default stance. Second, preference and signal elicitation are structurally crude: The mapping from truth $\to$ perception $\to$ inference $\to$ recorded variables introduces layered noise, leaving a persistent uncertainty floor. When this 'crudeness' overwhelms the decision margin, plug-in expected-utility optimization becomes brittle (high flip probability under small perturbations), whereas robust dominance/filtering rules ($\epsilon$-dominance, maximin) stabilize decisions.Finally, we outline a clinician-aligned AI blueprint: Use rich models for beliefs and trajectories, but choose actions through robust ordinal rules; treat heuristics as the low-dimensional special case; and deploy AI as 'selective complexity' -- invoked mainly for tie-breaking when decisions are fragile and information has positive expected impact.

cs.AI↗

arXiv2025

Divergent Realities: A Comparative Analysis of Human Expert vs. Artificial Intelligence Based Generation and Evaluation of Treatment Plans in Dermatology

Background: Evaluating AI-generated treatment plans is a key challenge as AI expands beyond diagnostics, especially with new reasoning models. This study compares plans from human experts and two AI models (a generalist and a reasoner), assessed by both human peers and a superior AI judge. Methods: Ten dermatologists, a generalist AI (GPT-4o), and a reasoning AI (o3) generated treatment plans for five complex dermatology cases. The anonymized, normalized plans were scored in two phases: 1) by the ten human experts, and 2) by a superior AI judge (Gemini 2.5 Pro) using an identical rubric. Results: A profound 'evaluator effect' was observed. Human experts scored peer-generated plans significantly higher than AI plans (mean 7.62 vs. 7.16; p=0.0313), ranking GPT-4o 6th (mean 7.38) and the reasoning model, o3, 11th (mean 6.97). Conversely, the AI judge produced a complete inversion, scoring AI plans significantly higher than human plans (mean 7.75 vs. 6.79; p=0.0313). It ranked o3 1st (mean 8.20) and GPT-4o 2nd, placing all human experts lower. Conclusions: The perceived quality of a clinical plan is fundamentally dependent on the evaluator's nature. An advanced reasoning AI, ranked poorly by human experts, was judged as superior by a sophisticated AI, revealing a deep gap between experience-based clinical heuristics and data-driven algorithmic logic. This paradox presents a critical challenge for AI integration, suggesting the future requires synergistic, explainable human-AI systems that bridge this reasoning gap to augment clinical care.

cs.AI↗

arXiv2025

A Novel Framework for Comparing Combination Therapy Outcomes Using Mechanistic Graph Models

Background: Predicting the efficacy of combination therapies is a critical challenge in clinical decision-making, particularly for diseases requiring multi-drug regimens. Traditional evidence synthesis methods, such as component network meta-analysis (cNMA), often face parameter explosion and limited interpretability, especially when modeling interaction effects between components. Objective: This article introduces a general Efficacy Comparison Framework (ECF), a mechanistically grounded system for predicting combination therapy outcomes. ECF integrates biological pathway-based abstractions with expert knowledge, optimized with quasi-rules derived from clinical trial data to overcome the limitations of traditional methods. Methods: ECF employs a disease pathogenesis graph to encode domain knowledge, reducing the parameter space through mechanistic functions and sparse network structures. Optimization may be performed using a loss function inspired by the Thurstone-Mosteller model, focusing on pairwise regimen comparisons. A pilot study was conducted for acne vulgaris to evaluate ECF's ability in both tested and untested comparisons. Results: In the acne vulgaris case study, the ECF-based model achieved 76% accuracy in predicting both tested and untested regimen outcomes, demonstrating statistically comparable performance across clinical trial data and expert dermatologist consensus (p = 0.977). The agreement between ECF and expert predictions was within the range of inter-expert agreement. Discussion: ECF aligns with recent advancements in network science and synergy prediction, leveraging principles of complementary targeting and biological plausibility. Its use of disease pathogenesis graphs offers a more interpretable and scalable alternative to existing models reliant on chemical similarity or protein-protein interaction (PPI) topology.

q-bio.QM↗